Neurophysiological Characterization of a Non-Human Primate ...

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Neurophysiological Characterization of a Non-Human Primate Model

of Spinal Cord Injury Utilizing Intramuscular Electromyography

by

Farah Masood

A Thesis

presented to

The University of Guelph

In partial fulfilment of the requirements for the degree of

Doctor of Philosophy

in

Engineering

Guelph, Ontario, Canada

© Farah, January 2020

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ABSTRACT

NEUROPHYSIOLOGICAL CHARACTERIZATION OF A NON-HUMAN PRIMATE MODEL OF SPINAL CORD INJURY UTILIZING INTRAMUSCULAR

ELECTROMYOGRAPHY

The lack of a valid non-human primate (NHP) model of spinal cord injury (SCI) and

a robust assessment tool are the key reasons behind the low rate of successful clinical

trials for the development of SCI medication. Accordingly, developing such an NHP model

would fill the gap between preclinical and clinical studies. A histopathological analysis

would help to validate a developed NHP model, while neurophysiological

characterizations could offer a reliable assessment tool for the effect of the injury. The

goal of this work is to submit a neurophysiological analysis using intramuscular

electromyography (EMG) signals collected from the agonist-antagonist pair of tail

muscles of Macaca fasicularis during pre- and post-lesion, and for a treatment and control

groups. The main goal was achieved by setting three sub-goals to be addressed in this

work. First, the general volitional muscle activity was analyzed utilizing some basic

amplitude features. Second, an analysis of the motor unit discharge properties was

submitted using wavelet transforms (WT) and the relative power (RP) of the multi-

resolution EMG signals. Third, the development of an SCI classification system was

studied and analyzed by employing various classification approaches, including regular

machine learning (ML) and deep learning (DL) classification techniques

Farah Masood

University of Guelph, 2019

Dr. Hussein A. Abdullah

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The findings were consistent with that of the literature. Evidence suggested that

the newly proposed general volitional muscle metric (Q-metric) was related directly to the

gross recruitment of motor units required for volitional muscle control pre- and post-lesion.

As well, the results of the multi-resolution analysis demonstrated the capability of utilizing

the WT and the RP as a decomposition method to characterize the discharge properties

of the dynamic muscle activity. The findings of the SCI classification analysis implied that

the proposed Q-metric and WT-RPs features could be used to build an SCI classification

system using K-nearest neighbors (KNN) technique. Finally, the deep learning

classification, which consisted exclusively of convolutional neural network (CNN) using

raw EMG segments as inputs, showed high potential and promising results for use as an

SCI classification system with an F-measure of 84.0% and 86.9%. for the left and the right

side.

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DEDICATION

To the soul of my great father with love …

Mohammed Ridha Masood

To my wonderful mother and my first teacher …

Sabah Yousif

To my best friends and my sisters …

Noor and Shahad

To my wonderful husband who supports me and has always believed in me …

Dr. Wisam Al-Wajidi

To my lovely daughter and son who support me with their continuous love and patience…

Mina & Yousif

Thank you all for being my strong, inspiring, and devoted family…

You have made me what I am today

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank and praise Allah, the Almighty, the most merciful

and compassionate, for His support, help, and generosity, and I pray to Him for guidance

in the future.

I would like to express my deepest appreciation to all those who have contributed directly

and indirectly to the completion of this dissertation.

I am very grateful to my academic supervisor, Dr. Hussein A. Abdullah, for his valuable

guidance, and patient encouragement throughout the work for this dissertation. Great

appreciation goes to Dr. Shanker Nesathurai for his mentoring and kind support through

the long journey of this research and for giving me the great opportunity to be part of his

distinguished team. I would like to extend my thanks to the members of my doctoral

advisory committee, Dr. Bob Dony, Dr. Karen Gordon, Dr. Gerarda Darlington, and Dr.

David Chiu for their constructive suggestions and recommendations. My appreciation also

to Dr. Graham Tylor, and my colleagues Dr. Nitin Seth, Dr. Noor Al-Qazzaz, and Dr. Abeer

Al-Hyari, for their time, accessibility, and advice throughout the research period. Personal

thanks are given to the Iraqi Cultural Counselor in Ottawa, Dr. Asaad Toma Omran, for

his help and support. Thank you to my colleagues in the Robotics Institutes Lab,

especially Cole Tarry and Patrick Wspanialy for their support.

I gratefully acknowledge the Iraqi Ministry of Higher Education and Scientific Research

(IMHESR) for providing me a scholarship to finish my Ph.D. degree in Canada.

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Table of Contents

Contents ABSTRACT............................................................................................................................................... III

DEDICATION ............................................................................................................................................ III

ACKNOWLEDGEMENTS ...................................................................................................................... IV

Table of Contents .................................................................................................................................... V

List of Figures ...................................................................................................................................... VIII

List of Tables ......................................................................................................................................... XII

List of Abbreviations .......................................................................................................................... XIII

Chapter 1 .................................................................................................................................................... 1

Introduction ........................................................................................................................................... 1

1.1. Spinal Cord Injury .................................................................................................................... 1

1.2. Problem Formulation .............................................................................................................. 1

1.3. Research Objectives ............................................................................................................... 2

1.3. Research Challenges .............................................................................................................. 3

1.4. Non-human Primate Model .................................................................................................... 3

1.6. Research Approaches ............................................................................................................ 4

1.7. Impact of the Work ................................................................................................................... 5

1.8. The Structure of the Thesis ................................................................................................... 6

Chapter 2 .................................................................................................................................................... 7

Background and Related Work ........................................................................................................ 7

2.1. Introduction ............................................................................................................................... 7

2.2. Spinal Cord Injury .................................................................................................................... 7

2.3. Motor Neuron Disorder ........................................................................................................... 9

2.4. Nonhuman Primate Model ................................................................................................... 12

2.5. Electromyography Signal .................................................................................................... 13

2.6 EMG Signal Acquisition........................................................................................................ 16

2.7. SCI Assessment Tools.......................................................................................................... 17

2.8. EMG Signal Conditioning ..................................................................................................... 20

2.9. EMG Feature Extraction ....................................................................................................... 23

2.10. EMG Decomposition .......................................................................................................... 25

2.11. Wavelet Transforms .......................................................................................................... 28

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2.12. SCI Classification System ................................................................................................ 31

2.12.1 Machine Learning Classification Technique ........................................................... 33

2.12.2 Deep Learning Classification Technique ................................................................. 35

Chapter 3 .................................................................................................................................................. 39

Materials and Methodology ............................................................................................................. 39

3.1. Introduction ................................................................................................................................ 39

3.2. Materials and Experimental Procedures .............................................................................. 39

3.2.1. Subjects enrolled ................................................................................................................ 39

3.2.2. The initial surgical procedure to facilitate the collection of EMG data ................ 39

3.2.3. The subsequent surgical procedure to create an experimental spinal cord

injury .................................................................................................................................................. 42

3.2.4. Monitoring of subjects ....................................................................................................... 45

3.2.5. Euthanasia and post-mortem examination .................................................................. 45

3.3. EMG Analysis Methods ............................................................................................................. 47

3.3.1. EMG signal preprocessing ............................................................................................... 50

3.3.2. General volitional muscle activity metric ..................................................................... 51

3.3.3. Motor units discharge properties ................................................................................... 59

3..4 SCI Classification System ........................................................................................................ 71

3.4.1 SCI classification system using classical ML classification technique ................ 71

3.4.2 SCI classification using DL technique compared to ML technique ........................ 75

Chapter 4 .................................................................................................................................................. 81

Results and Discussions ................................................................................................................. 81

4.1. Introduction ................................................................................................................................. 81

4.2. General Volitional Muscle Activity Metric ............................................................................ 82

4.2.1. Conventional EMG metrics ............................................................................................... 82

4.2.2. The proposed peak number method (Q-metric) ......................................................... 85

4.3. An Analysis of the EMG Discharge Properties .................................................................. 88

4.3.1. Conventional EMG decomposition................................................................................. 88

4.3.2. The proposed EMG discharge properties analysis utilizing wavelet transform

decomposition and relative power ............................................................................................ 92

4.4. SCI Classification System ...................................................................................................... 101

4.4.1 Classification performance evaluation ........................................................................ 102

4.4.2. SCI classification system using classical ML classification technique ............. 105

VII

4.4.3. SCI classification using DL technique compared to ML technique..................... 115

4.5. Summary ................................................................................................................................ 121

Chapter 5 ................................................................................................................................................ 124

Conclusions and Future Work ...................................................................................................... 124

5.1. Conclusions ............................................................................................................................... 124

5.2. Future Work ............................................................................................................................... 128

References ............................................................................................................................................. 130

List of Publications .............................................................................................................................. 142

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List of Figures

Figure 2.1 The main causes of SCI [4] ____________________________________________________ 8

Figure 2.2 The different types of paralysis resulted from SCI [19] _______________________________ 9

Figure 2.3 The complete course of motor neurons starting from the motor cortex (brain) to the spinal

cord[26] ___________________________________________________________________________ 11

Figure 2.4 The two different pathways of the upper motor neuron [26] __________________________ 11

Figure 2.5 Animal models of SCI [38] ____________________________________________________ 13

Figure 2.6 Motor unit [41] _____________________________________________________________ 15

Figure 2.7 Schematic representation of the anatomical and the physiological model of EMG signal (adopted

from [46]) __________________________________________________________________________ 15

Figure 2.8 Different assessment method of animal model of spinal cord injury [38] ________________ 19

Figure 2.9 EMG raw recording with ECG spikes [65] _______________________________________ 22

Figure 2.10 EMG raw recording contaminated by power noise [65] ____________________________ 23

Figure 2.11 Illustration of EMG signal decomposition [41], [63], [64] ____________________________ 27

Figure 2.12 Classification process: training and testing steps _________________________________ 32

Figure 3.1The main components of the utilized experimental setting [5] _________________________ 41

Figure 3.2 Graphical representation of the collected EMG signal [160] __________________________ 42

Figure 3.3 The CT image of the inflated balloon in the epidural space of the thoracic spine. The balloon is

inflated with air, which appears black in the spinal column (red arrow). Note that 60% of the column is

occupied by the balloon, and the spinal cord is displaced. This image is from a cadaveric subject [6] __ 44

Figure 3.4 Standard radiograph of the catheter inserted into the epidural space via laminotomy in a

cadaveric subject. The balloon is not inflated in this image [6] _________________________________ 44

Figure 3.5 A general block diagram for the EMG analysis - Part 1 _____________________________ 48

Figure 3.6 A general block diagram for the EMG analysis- Part 2 ______________________________ 49

Figure 3.7 Flowchart of the peak number algorithm ________________________________________ 57

Figure 3.8 An illustration of the peak number method _______________________________________ 58

Figure 3.9 Mother wavelet function (db4) _________________________________________________ 66

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Figure 3.10 Wavelet analysis; decomposition and reconstruction steps [160] _____________________ 67

Figure 3.11 Collected Data Structure: The data in this work was collected from the left (L) and the right (R)

side of the tail for five subjects throughout the days of the experiment (d1, d2, …, dn) for the pre- and post-

lesion period. The data for each day was decomposed into seven frequency sub-bands (D1, D2, D3, …,

A6). Three of the subjects received a treatment combining TRH, selenium, and vitamin E post-lesion

(treatment group); the remaining two subjects did not receive any treatment post-lesion (control group)

[160] _____________________________________________________________________________ 70

Figure 3.12 General schematic illustrating the SVM classification technique _____________________ 74

Figure 3.13 A graphical illustration of the K-nearest neighbor classification technique ______________ 75

Figure 3.14 Convolutional neural network structures utilized to build the EMG-based SCI classification

system ____________________________________________________________________________ 80

Figure 4.1 The aggregate fitted Q-values, representing tail movement, are plotted for the left (top) and the

right (bottom) side. Values are normalized to a range between 0 and 1, with higher values indicating greater

muscle activity. Of note, on both the left and the right side, the Q values decreased immediately after the

creation of the lesion. On the left side, the effect of the lesion in the treatment group (orange) was

attenuated, compared with that of the nontreatment group (grey). On both the left and the right side, the Q-

metric improved over time [6] __________________________________________________________ 86

Figure 4.2 Example of valid and invalid MUPs, their MUP templates, and firing patterns [43] ________ 89

Figure 4.3 From (1) to (6), different samples of the detected and clustered MUPs using the K-means

clustering method and fifteen various EMG features ________________________________________ 92

Figure 4.4 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to the creation of

SCI for the left and the right side of the tail. Of note for each band, there is a difference in the RP value

when compared to the left and the right side of the tail. The D2 sub-band of the left and the right side has

the maximum RP, and the significant difference between the two sides is at the D1, D2, D4, and D5 sub-

bands. The star indicates a significant difference [160] ______________________________________ 93

Figure 4.5 The estimated mean of the RP of seven reconstructed EMG sub-bands prior and post to the

creation of the SCI for the left side of the tail. Of note, the RP values for the frequency sub-bands (D4, D6,

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and A6) are significantly higher in the post-lesion period. The star indicates a significant difference [160]

_________________________________________________________________________________ 96

Figure 4.6 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to and post to the

creation of the SCI for the right side of the tail. Of note, the RP values for the lower frequency sub-bands

(D4, D6, and A6) are significantly higher in the post-lesion period, while the higher frequency sub-bands

(D1 and D2) are significantly lower in the post-lesion period. The star indicates a significant difference [160]

_________________________________________________________________________________ 97

Figure 4.7 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the left

side of the treatment (Tr) and the control (Ctrl) groups; of note, the RP values for the frequency sub-bands

(D1, D2, D3, and D6) are significantly different. Subjectively, the distribution of the RP in the treatment

group is similar to the RP distribution in the pre-lesion period for all the subjects. The star indicates a

significant difference [160] ____________________________________________________________ 99

Figure 4.8 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the right

side of the treatment (Tr) and the control (Ctrl) group; of note, the RP values for frequency sub-bands (D1,

D3, D4, D5, D6, and A6) are significantly different. Subjectively, the distribution of the RP in the treatment

group is similar to the RP distribution in the pre-lesion period for all the subjects. The star indicates a

significant difference [160] ___________________________________________________________ 100

Figure 4.9 The basic components of a confusion matrix [205], [206] ___________________________ 103

Figure 4.10 An example of the confusion matrix results _____________________________________ 104

Figure 4.11 Confusion matrix of the SVM classifier using EMG amplitude features / left side ________ 106

Figure 4.12 Confusion matrix of the KNN classifier using EMG amplitude features / left side ________ 107

Figure 4.13 Evaluation metrics of the KNN and the SVM classifier using EMG amplitude features/ left side

________________________________________________________________________________ 108

Figure 4.14 Confusion matrix of the SVM classifier using EMG amplitude features / right side ______ 109

Figure 4.15 Confusion matrix of the KNN classifier using EMG amplitude features / right side _______ 110

Figure 4.16 Evaluation metrics of KNN and SVM classifier using EMG amplitude features/ right side _ 110

Figure 4.17 Confusion matrix of the KNN classifier using the Q-metric and the RP features of the left side

EMG data ________________________________________________________________________ 112

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Figure 4.18 Confusion matrix of the SVM classifier using the Q-metric and the RP features of the left side

EMG data ________________________________________________________________________ 112

Figure 4.19 Confusion matrix of the KNN classifier using the Q-metric and the RP features of the right side

EMG data ________________________________________________________________________ 113

Figure 4.20 Confusion matrix of the SVM classifier using the Q-metric and the RP features of the right side

EMG data ________________________________________________________________________ 113

Figure 4.21 Evaluation metrics of the KNN and the SVM classifier using the RPs and the peak number

features/ left side ___________________________________________________________________ 114

Figure 4.22 Evaluation metrics of the KNN and the SVM classifier using the RPs and the peak number

features/ right side __________________________________________________________________ 115

Figure 4.23 The accuracy curve for the EMG data classification of the left side using the proposed CNN

architecture _______________________________________________________________________ 117

Figure 4.24 The loss curve for the EMG data classification of the left side using the proposed CNN

architecture _______________________________________________________________________ 118

Figure 4.25 The accuracy curve for the EMG data classification of the right side using the proposed CNN

architecture _______________________________________________________________________ 118

Figure 4.26 The loss curve for the EMG data classification of the right side using the proposed CNN

architecture _______________________________________________________________________ 119

Figure 4.27 Evaluation metrics of the CNN and the KNN classifier / left side ____________________ 120

Figure 4.28 Evaluation metrics of the CNN and the KNN classifier / right side ___________________ 120

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List of Tables

Table 2.1 A simple comparison between surface and indwelling electrodes [47] _____________________ 17

Table 2.2 Summary of the most common EMG filtering methods __________________________________ 21

Table 2.3 Most common features of EMG signals [48], [68], [69], [71], [84]–[88] _____________________ 25

Table 2.4 The most common applications of WT analysis in EMG signal field _______________________ 30

Table 2.5 Previous works related to EMG signal and deep learning techniques ______________________ 37

Table 3.1 The frequency ranges of the seven EMG reconstructed sub-bands _______________________ 66

Table 3.2 Parameters used in the equation of the mixed model ____________________________________ 69

Table 3.3 Optimized CNN architecture __________________________________________________________ 78

Table 4.1 The results of the statistical model (lesion effect) of the conventional EMG features for the left

side ________________________________________________________________________________________ 84

Table 4.2 The results of the statistical model (treatment effect) of the conventional EMG features for the

left side _____________________________________________________________________________________ 84

Table 4.3 The results of the statistical model (lesion effect) of the conventional EMG features for the right

side ________________________________________________________________________________________ 84

Table 4.4 The results of the statistical model (treatment effect) of the conventional EMG features for the

right side ____________________________________________________________________________________ 84

Table 4.5 Standardized Q-metric_______________________________________________________________ 87

Table 4.6 The utilized hyperparameters________________________________________________________ 116

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List of Abbreviations

Abbreviation Definition

A6 Reconstructed EMG of Approximate Coefficient/Level 6

ALS Amyotrophic Lateral Sclerosis

AR Auto-regressive

BC Binary Cross-Entropy

Bior Biorthogonal

BPF Band Pass Filter

cA6 Wavelet Approximate Coefficient / Level 6

cD1 Wavelet Detail Coefficient / Level 1






CNN Convolutional Neural Network

Coif Coiflets

Colab Collaboratory

CWT Continuous Wavelet Transform

D1 Reconstructed EMG of Detail Coefficient/Level 1






Db Daubeches

DL Deep Learning

DWT Discrete Wavelet Transform

ECG Electrocardiography

EMG Electromyography

FC Fully Connected

FD Frequency Domain

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FN False Negative

FP False Positive

FT Fourier Transform

GAP Global Average Pooling

HF High Frequency

IP Interference Pattern

KNN K-Nearest Neighbors

LDA Linear Discriminant Analysis

LF Low Frequency

LMN Lower Motor Neuron

LPF Low Pass Filter

MAV Maximum Absolute Value

MFP Muscle Fiber Potential

ML Machine Learning

MLP Multilayer Perceptron

MN Motor Neuron

Morl Morlet

MSPCA Multiscale Principal Component Analysis

MSR Muscle Stretch Reflex

MU Motor Unit

MUD Motor Unit Disease

MUNE Motor Unit Number Estimation

MUP Motor Unit Potential

MUPT Motor Unit Potential Train

MUSIC Multiple Single Classifications

NE Needle Electrode

NHP Non-Human Primate

P2P Peak to Peak

PUK Pearson VII Function-Based Universal Kernel

Q The Suggested Metric of Volitional Limb Movement

RBF Radial Basis Function

ReLUs Rectified Linear Units

RMS Root Mean Square

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RNN Recurrent Neural Network

RP Relative Power

SBP Sub-Band Power

SCI Spinal Cord Injury

SD Standard Deviation

SMOTE Synthetic Minority Oversampling Technique

SVM Support Vector Machine

Sym Symlets

TD Time Domain

TFD Time Frequency Domain

TN True Negative

TP Total Power

TP True Positive

TRH Thyrotropin Releasing Hormone

UMN Upper Motor Neuron

WT Wavelet Transforms

ZC Zero Crossing

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Chapter 1

Introduction

1.1. Spinal Cord Injury

Spinal Cord Injury (SCI) is a serious neurological condition. Worldwide, there are

approximately 930,000 new cases each year [1]–[3]. The most common causes of SCI

are motor vehicle collisions, falls, sports-related activities, and interpersonal violence [4],

[5]. SCI typically damages the motor, sensory and autonomic nerve fiber tracts. Patients

experience a spectrum of clinical abnormalities such as limb paralysis and dysesthesia,

as well as bowel and bladder dysfunction [5]–[7].

SCI has a devastating effect on the physical, psychosocial, and emotional aspects of

patients and caregivers. The burden of this condition is shared by the person affected,

family members, the community, as well as the healthcare system. Long-term survival

and quality of life have improved due to enhanced rehabilitation and medical

interventions; however, it has not been convincingly demonstrated that current

pharmacological treatments have substantially improved spinal cord function. One of the

main challenges for developing a suitable pharmacologic intervention is the absence of a

valid non-human model for SCI [7], [8].

1.2. Problem Formulation

Despite the vast budget allocated for the development of SCI medication, the rate of

successful clinical trials is still low [8], [9]. The main reason behind this low rate is the lack

of a valid non-human primate (NHP) model of SCI and a reliable assessment tool [8], [9].

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Developing such a model would therefore have a significant impact on filling the

translational gap between preclinical and clinical research. As well, it would be an

essential step in the broader plan for the drug development industry, as NHP data from a

proper and accurate design gives a clear idea about possible clinical outcomes. Due to

the importance of the NHP models in translational medicine, investors and the

government health institutions offer great financial support for developing and optimizing

this significant stage in the development of healthcare treatment [9].

1.3. Research Objectives

Conceptually, the overriding goal was to build an NHP model of SCI by characterizing

the injury neurophysiologically using intramuscular electromyography (EMG) signals.

This contribution would help in building a model to test new therapies for SCI and thereby

assist in improving the functionality and quality of life for human beings. In order to

achieve this general objective, three specific objectives were set to be addressed in this

study. These objectives included the following:

1. To investigate a method (metric) by which general volitional muscle activity pre-

and post-SCI could be measured utilizing the amplitude characteristics of

intramuscular EMG signals.

2. To characterize the discharge properties of muscle motor units by analyzing

the frequency content of the intramuscular EMG signal during pre- and post-

SCI for a treatment and a control group, and for an agonist-antagonist pair of

muscles.

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3. To design a classification system for SCI using machine learning and deep

learning techniques.

1.3. Research Challenges

Challenges associated with developing a valid NHP model are:

• Minimizing the number of NHP involved in biomedical experiments due to strict

ethical standards and considerations, especially if the experiment requires any

physiological changes to the animal, such as causing an impairment [9].

• Balancing the limited animal numbers and the statistical power necessary for

rejecting the null hypothesis.

• Collecting and processing the large amount of data needed in order to capture the

full picture of the disease effects, which is costly and time-consuming.

1.4. Non-human Primate Model

Conceptually, the understanding of SCI impairments and recovery can be advanced

through animal models [6], [7], [10]–[13]. NHP models are particularly valuable due to the

anatomical and physiological similarities between NHP and human beings [10], [11].

Millions of human lives have been saved and many medical conditions have been cured

by the critical health advances that have resulted from research with NHPs [14]. NHP

models have been linked to many health advances, such as the development of the polio

vaccine, the development of the first animal model of Parkinson’s disease, advanced

treatment for prostate cancer, and the treatment of many other diseases [14].

4

1.5. Electromyography Signal

EMG is a widely available, cost-effective technique concerned with the recording and

the analysis of the myoelectric signal. An EMG signal is the electrical potential that is

formed when the muscle is electrically or neurologically activated. This technique has

been used in numerous clinical practices, e.g. to study the health condition of a muscle

and its innervated neurons (motor neurons). As well, it has been used as a diagnostic tool

for neuromuscular diseases, and as an assessment tool (biofeedback) for muscular

abnormalities and rehabilitation [15], [16]. EMG techniques are mainly utilized in medical

research (e.g. functional neurology, gait and posture analysis, motor control applications),

rehabilitation (e.g. neurological rehabilitation and post-surgery/accident), and sport

science (biomechanics and movement analysis) [15], [16].

The EMG signal provides information about the activity (electrical signal) of the

corresponding muscle, in which the time of the signal represents the duration of the

muscle activity. Moreover, the EMG amplitude is the level of the detected electrical signal,

which reflects the intensity of the muscle activation. As well, the frequency analysis of the

EMG signal is used to identify some neuromuscular disorders and symptoms, such as

muscle fatigue and dystonia [17]–[19].

1.6. Research Approaches

The collection and analysis of EMG data for this study are unique. Specifically, this

dataset contains EMG data from the tail musculature of NHPs. In addition, the data were

collected longitudinally for two phases: pre- and post-creation of an experimental SCI.

5

Each recording date served as a unique data point. This feature facilitated inferences

related to the trajectory of recovery from SCI. Furthermore, the muscles under

consideration were an agonist-antagonist pair. The statistical power in these experiments

was enhanced by the collection of the pre- and post-lesion EMG data. This inclusion

permitted the normalization of the post-lesion data for each subject, and theoretically,

decreased statistical variances. Consequently, this experimental strategy reduced the

number of required subjects. In this context, no customary or established analysis

paradigms were available.

The methodology of this study consisted of four main sections. The first section was

the collecting and preprocessing steps for the EMG data (recording, filtering, removing

outliers, segmentation). The second section was the characterization of the EMG signal

amplitude using the most common metrics (area, root mean square, turns, zero-crossing).

It also used a newly proposed metric, named the Q-metric, for free volitional muscle

activity. Next, an analysis of the discharge properties (frequency content) for the acquired

EMG signals was performed utilizing wavelet transforms and relative power for each EMG

sub-band. Finally, the SCI classification method was investigated using various EMG

extracted features and by employing different classification methods, including machine

learning (ML) and deep learning (DL) techniques.

1.7. Impact of the Work

Developing a new NHP model of SCI during pre- and post-lesion will have the

following impacts:

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1. It provides a better understanding and characterization for the SCI effect on muscle

volitional activity by using an affordable and applicable neurophysiological

technique (EMG data).

2. It provides a new preliminary model to test the efficacy of suggested treatments

for SCI.

3. It may help in developing a new assessment tool that can help medical

professionals to determine the severity and progression of SCI.

4. It introduces a new application of deep learning for SCI classification using an

intramuscular EMG signal.

In addition, creating such a model and a metric will help to improve the approaches

used by the healthcare system to characterize spinal cord injury. This will occur in

different aspects such as developing new specialized rehabilitation techniques and

providing an easy assessment approach for SCI patients.

1.8. The Structure of the Thesis

The structure of the thesis is as follows: Chapter 2 provides a detailed literature

review of the main components of this work. Chapter 3 describes the proposed

methodology that was applied to the collection of the EMG signals. Chapter 4 presents

and discusses the results of the research work, and finally, the conclusions and

suggestions for future work are provided in Chapter 5.

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Chapter 2

Background and Related Work

2.1. Introduction

This chapter provides a detailed background of the main research topics presented

in this thesis. SCI is also defined; its levels, the leading causes, and the consequences

are presented. The next section explains the NHP model and discusses its importance.

The definition and the physiological background of the EMG signal are also presented in

this chapter. Later in the chapter, an overview is provided of the EMG signal properties,

and the common artifacts contaminating this type of signal. The most common EMG

signal conditioning techniques are presented in this chapter. The SCI assessment tools

in the literature are reviewed in this chapter. The current understanding of the EMG signal

abnormalities associated with SCI in animals and human beings are presented. Chapter

2 also provides specifics regarding state-of-the-art EMG feature-extraction methods. The

applied wavelet analysis is explained as well as its relevant applications. This is presented

in the form of a table within this chapter. Finally, this chapter provides a background for

the employment of machine learning and deep learning techniques, as well as their main

applications in the EMG field.

2.2. Spinal Cord Injury

SCI is damage occurring to any part of the spinal cord. It often results in permanent

perturbations in sensation, strength and the function of the body below the level of the

injury. Typically, SCI is the result of motor vehicle accidents, falls, and violence [4], [5], as

shown in Figure (2.1).

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Figure 2.1 The main causes of SCI [4]

People with SCI have a range of clinical impairments, which can be attributed to

impaired fiber tracts. Based on the location of the SCI along the spinal cord and the

severity of the injury, the ability of limb control and motion is determined. The neurological

level of the SCI is below the level of the injury and the severity depends on whether the

injury is complete or incomplete, as follows:

• Complete SCI: when all sensory and all motor functions are lost below the

neurological level of the injury.

• Incomplete SCI: when there is some residual sensory or motor function below the

neurological level of the injury. There are different degrees of incompleteness.

The paralysis resulting from an SCI may be referred to as [20]:

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• Tetraplegia (Quadriplegia): The paralysis of arms, hands, trunk, legs and pelvic

organs.

• Paraplegia: The paralysis of all or part of the trunk, legs and pelvic organs. Figure

(2.2) illustrates the different types of paralysis resulting from SCI.

Figure 2.2 The different types of paralysis resulted from SCI [19]

2.3. Motor Neuron Disorder

Motor neurons (MN) are a specialized type of neuron located within the spinal cord

and the brain. There are two main subtypes, namely the upper motor neuron (UMN) and

the lower motor neuron (LMN), as shown in Figure (2.3). The UMNs originate from the

10

primary motor cortex in the brain and travel downward. UMNs synapse on LMNs in the

brain stem and form a corticobulbar tract or they connect to the LMNs in the spinal cord

to form a corticospinal tract [21]. Figure (2.4) illustrates the different pathways of UMN

[22], [23]. LMNs originate in the brainstem and the spinal cord and they innervate skeletal

muscles directly [24].

Motor neuron disorders (MNDs) are sporadic, fatal neurologic diseases characterized

by progressive degeneration of the motor neurons. These disorders can affect either or

both types of motor neurons (UMNs, LMNs). The MNDs result from different types of

diseases and lesions such as SCI, stroke, cerebral palsy, and amyotrophic lateral

sclerosis (ALS). The main clinical features of an upper motor neuron disorder (the case

in this study) include:

1. Spasticity: Stiffness or tightness of a muscle, which affects its normal movement

[23].

2. Hyperreflexia: High muscle stretch reflex (MSR), which results from losing the

periodicity of the UMN stimulation on the LMN [22].

3. Clonus: Rhythmic contraction of the antagonist muscles, such as any rapid

movement (pushing or pulling) to any one of antagonist muscles (upper or lower

limb). This leads to an involuntary movement of the limb because each muscle

stimulates the other one [22].

4. Hypertonia: Increased muscle tension and difficulty in muscle stretch ability [22].

5. Extensor Plantar Response/Babinski Sign: “occurs after the sole of the foot has

been firmly stroked, the big toe then moves upward or toward the top surface of

the foot while other toes fan out.” [25].

11

Figure 2.3 The complete course of motor neurons starting from the motor cortex (brain) to the spinal cord[26]

Figure 2.4 The two different pathways of the upper motor neuron [26]

12

2.4. Nonhuman Primate Model

Animal models are essential in biomedical research. In the field of SCI, various

species have been used, including mice, rats, cats, dogs, rabbits, and NHP, as shown in

Figure (2.5). In these models, the experimental spinal cord injury has been created

through diverse methods, some of which require surgical exposure of the spinal cord and

lesion induction by using a sharp instrument [12], [27], [28] or a device that provides static

or dynamic compression [29]–[31]. In other models, the dura remains intact, and

compression is applied by using a circumferential clip [32]. These approaches are compli-

mentary. Some pathophysiologic and clinical features of SCI differ substantially between

humans and other species; for example, despite complete anatomic transaction, dogs

and cats—unlike humans and NHP—may be able to walk [33]–[36]. The tail in NHPs is

analogous to a human limb. The tail is integral to performing functional tasks such as

standing on the hindlimbs as well as reciprocal movements that aid in balance during

ambulation [37]. As described later, the tail exhibits other features analogous to human

beings such as limb dominance or preference. In this context, NHP models have an

essential role in advancing the understanding of SCI as well as identifying candidate

treatments.

13

Figure 2.5 Animal models of SCI [38]

2.5. Electromyography Signal

An EMG represents the electrical activity of skeletal muscles during the activation

state (contraction), which is generated as a result of physiological changes in the muscle

fibers while they are moving [39]. Scientists have known about the existence of

myoelectrical signals since the 1600s, but the lack of equipment required for detecting

and measuring the electrical signals has limited the research in this field [19], [40];

however, in the early 1900s, the development of electrical components enabled scientists

to invent suitable tools for detecting the EMG signal [41], [42]. During that time, physicians

were analyzing the EMG signals qualitatively, again as a result of the lack of appropriate

electronic equipment. However, around the 1960s, this problem was solved, and the EMG

amplitudes started to be quantified [41], [42]. Step by step, EMG devices and signal

processing techniques have been developed, and this progress has enabled physicians

to make a more effortless quantitative analysis of the EMG signal. Now the EMG signal

14

is considered an important tool in understanding the neuromuscular system, in addition

to its role as a biological signal in discovering underlying disease mechanisms [41], [42].

The EMG signal is a series of voltages that are generated from muscle fibers

during muscle contractions [39]. Skeletal muscle is composed of densely packed muscle

fibers, and these fibers are innervated in groups by motor neurons [41]. The combination

of a group of muscle fibers and the single motor neuron that innervates them is known as

a motor unit (MU), as is seen in Figure (2.6) [41]. Each muscle consists of multiple MUs

which coactivate together to generate a coordinated muscle contraction. Each active

single muscle fiber generates a wave, which results from the depolarization and

repolarization of the muscle fiber membrane. This wave is called the muscle fiber potential

(MFP). The summation of all the MFPs of a single MU is called the motor unit potential

(MUP). To increase or maintain the generated force of a muscle, the MUs fire repeatedly

and generate a motor unit potential train (MUPT). A detected EMG signal is comprised of

the superposition of the generated MUPTs of all fired MUs and any accompanying

interference signal, such as the background noise [Figure (2.7)] [43]–[45].

The EMG signal is an important biological signal, which is used in different fields

such as medical research, rehabilitation, and sport science. EMG signal analysis enables

researchers to look directly into the muscle to reflect on the internal structure and activity.

As well, muscle performance could be evaluated by utilizing the EMG signal. In addition

to the previous benefits, the EMG plays an important role in choosing the desired muscle

training program and a suitable treatment [16].

15

Figure 2.6 Motor unit [41]

Figure 2.7 Schematic representation of the anatomical and the physiological model of EMG signal (adopted from [46])

16

2.6 EMG Signal Acquisition

In order to get an accurate EMG analysis, it is vital to consider the parameters that

affect the quality of an acquired signal. Electrode type, position, level of muscle

contraction, and many other parameters play an important role in the outcomes of the

EMG analysis. As well, the different kinds of noise combining in the EMG signal could be

reduced by using a proper electrode type and a suitable setting [41].

There are a variety of methods used to obtain EMG data from humans and

animals. As a general construct, there are two types of electrodes: a surface, and an

indwelling electrode (needle or wire) [47]. The neurophysiological characteristics and the

interpretation of the data derived are not synonymous. Surface electrodes are placed over

the skin; they can be manufactured from a variety of materials and formed into various

sizes and shapes. Surface recording over the skin is subject to a distortion in the EMG

signal due to the interposition of the fascia and the subcutaneous tissues. With limb

movement, the skin, the subcutaneous tissue and the muscles move asymmetrically. This

contributes further to movement artifacts. These tissues are effectively a low pass filter.

In comparison, needle electrodes are made of metal; subtypes include monopolar,

concentric, single fiber and macroelectrodes. Wire electrodes, in contrast, can vary in

diameter and length. Wire electrodes allow for long term implantation in subjects and are

generally well tolerated. When compared to surface electrodes, there is less cross-talk

[16], [41]. As a conceptual argument, data from wire electrodes aggregates the EMG

signal from muscle fibers in multiple motor units. The signal is potentially affected by

multiple factors, including the proximity of the signal generator to the wire, the phase

17

cancellation, the size of the fibers, and the type of fibers (i.e., fast-twitch or slow-twitch).

As well, there is some factors related to the neural network that controls the agonist-

antagonist pair [41]. Table (2.1) explains the main differences between the two kinds. As

well, the difference in the quality of the acquired EMG signal using these two types can

be noticed in the table, as the signal of the indwelling type is more precise and cleaner

(small area, and less cross-talk) [47].

Table 2.1 A simple comparison between surface and indwelling electrodes [47]

Surface Electrode Indwelling Electrode

Electrode Type Flat disk Needle, fine wire

Position Placed over selected muscle skin Inserted into selected muscle

Pick Up Area Large Small

Cross-talk High rate Low rate

Type of Muscle Superficial muscle Deep muscle

2.7. SCI Assessment Tools

SCI leads to variable functional consequences; therefore, an early and accurate

assessment is vital in determining the neurological deficits, defining the required therapy,

and predicting the prognosis of the injury. An essential step of the muscle functional

neurophysiological assessment entails the acquisition, as well as a qualitative and a

quantitative analysis, of the EMG signals.

The qualitative EMG analysis includes behavioral, visual, and auditory

assessments. In contrast, the quantitative analysis, which is a more accurate and

objective tool, involves neurological measures and biomarkers such as the EMG signal

[48], [49]. Anderson et al. [50] submitted a comparison study of various assessment

18

methods for measuring the effects of cervical hemisection lesions in rats. The results

revealed that with an incomplete cervical hemisection, there was a recovery stage,

whereas a complete hemisection did not demonstrate a recovery stage. The assessment

techniques used to determine recovery in this study included behavioral testing, grip

strength meter (GSM), food pellet reaching tasks, horizontal rope walking, and a dynamic

plantar aesthesiometer. GSM was the most convenient method for assessing the strength

of the muscle. In [12], the researchers utilized rhesus monkeys to build a non-human

model that described natural recovery after a spinal cord injury. This study also

highlighted the vital role of such primates in translational medical research. Then in [51],

Nout et al. explored the use of a behavioral test as an assessment approach for motor

function recovery after impairment. This study examined the sensitivity of the test to the

changes in the motor functionality level. It was revealed that behavioral measurements

represent a reliable method to assess motor function after spinal cord hemisection in

primates. Moreover, such assessment methods play a vital role in testing the efficacy of

suggested therapeutics and help in understanding their positive and negative effects on

the primate body. The assessment methods in these studies mainly depended on

behavioral tests and reaching tasks.

As mentioned earlier, the impairments of the SCI are usually measured by

observing behavior as well as histopathological assessment, as seen in Figure (2.8) [38].

However, the EMG signal confers certain advantages over these other methods. It is

particularly useful for assessing SCI for EMG data collected on a serial basis as this

approach facilitates the characterization of MU activation and recruitment [12]. EMG

signals can be recorded through multiple channels, permitting the simultaneous

19

assessment of multiple muscle groups. This is particularly helpful in evaluating agonist-

antagonist muscle pairs, in which the evaluation of muscle co-contractions is associated

with central nervous system disorders [6], [13]. EMG data can be obtained through

surface recordings or intramuscular electrodes placed into the muscles. There is limited

literature regarding the nature of intramuscular EMG signals after SCI in animals and

humans. The majority of human studies have been based on surface EMG electrodes.

As a general construct, the SCI results in a perturbation in the EMG signal. The next

section reviews the current understanding of EMG abnormalities associated with SCI in

animals and human beings.

Figure 2.8 Different assessment method of animal model of spinal cord injury [38]

Calancie et al. [52] studied the recovery of volitional activity after acute SCI in

human beings utilizing pairs of surface EMG electrodes. This study demonstrated

perturbations in some EMG characteristics including abnormalities in recruitment. Lewko

[53] utilized surface EMG electrodes and noted disturbed behavior in spinal cord

20

conductivity with quiet standing. Nout and Rosenzweig, among others [12], [13]

developed an NHP model of SCI, and the results demonstrated a significant difference in

EMG amplitude and temporal patterns between the healthy and the SCI subjects. As well,

they noted uncoordinated muscle activity during the post-lesion condition. Wiegner et al.

and Shahani et al. studied the recruitment pattern of MUs post-SCI [54] and cortical

pathology [55] using needle EMG signals. The results indicated that MUs fire irregularly

with a low discharge rate post-SCI. As well, the inter-discharge intervals (IDIs) had a

positive serial correlation which resulted from the decreased variability in the length of the

adjacent intervals. Capogrosso et al. [27] developed a brain-spine interface to modulate

the consequences of SCI in NHP. EMG signals during continuous locomotion were

recorded and averaged to calculate the spatiotemporal maps of the motoneuron

activation in monkeys. The results suggested a practical translational pathway for

conceptual analysis studies and investigational applications in humans with SCI.

2.8. EMG Signal Conditioning

Signal processing is a wide field that deals with different scientific aspects, and it

is an essential tool for analyzing any bio-signal and exploring the underlying information.

In the literature, different signal processing techniques were applied to the raw EMG

signal to enhance its quality. In this section, the most common filtering methods are

explored. Table (2.2) summarizes the common methods in the literature for EMG signal

filtering.

21

Table 2.2 Summary of the most common EMG filtering methods

Study Filter Type Frequency Limits

Hz

Sampling Frequency

Hz

[56] Analog/ Band Pass filter (BPF) 5-300 1024

[57] Analog/ BPF 10-500 2000

[58] Digital/ BPF/4th Butterworth 20-500 2000

[59] Analog/ BPF 20-1000 2000

[60] Digital & Analog / BPF /4th Butterworth 100-3000 20000

[61] Analog / BPF & Digital/ Low Pass Filter (LPF) 5hz-10khz & 8khz 20000

[62] Analog/ BPF 20 – 460 Hz 1000

[63] Digital/ BPF / Butterworth 20-500 Hz 1000

[64] Analog/ LPF 450 Hz 1000

An EMG filtering step has been implemented to attenuate the noise signals that

combine in the raw EMG signal on its way from the muscle to the electrode. There are

various types of noise that can affect the EMG shape and characteristics. These types

can be grouped as below:

1. Motion artifact (baseline shifts): This kind of artifact results from changes in the

geometry between the electrode location and the muscle or as a result of a repeated

cable shake. This problem can be solved adequately through the use of proper

electrode fixation and good skin preparation [41], [65]–[67].

2. Muscle cross-talk (physiological cross-talk): The neighboring muscles can generate

some additive EMG signals that are added to the desired raw EMG signal. This type

of noise can be solved by narrowing the measured area or changing the electrode

type; for example, using an intramuscular electrode instead of a surface electrode. An

electrocardiography (ECG) artifact, which is the electrical signal of the heart, is an

22

avoidable biological cross-talk noise that contaminates the EMG signal with ECG

bursts, as shown in Figure (2.9) [65]. This problem occurs when the muscle being

studied is located at the left upper trunk / near the heart. Many signal processing

procedures can be applied to solve this problem without destroying the original EMG

signal [41].

Figure 2.9 EMG raw recording with ECG spikes [65]

3. Power-line noise (interference power line): Any devices or equipment with poor

grounding that are close to the recording area can generate an interference power line

artifact, which is usually between 50 and 60 Hz. To overcome this type of noise, it is

important to ground all devices, especially those equipped with an EMG recording

system, such as training devices, treadmills, etc. It is also important to use a minimum

number of power plug connectors. Figure (2.10) [65] represents an EMG signal

contaminated with a power-line artifact [41], [65], [66].

23

Figure 2.10 EMG raw recording contaminated by power noise [65]

4. Baseline offset: This type of noise has a constant value and it results in shifting the

zero baseline of the original signal to a specific value. This kind of artifact may occur

if any change has been done after setting the calibration. Fortunately, it is easy to

remove the baseline offset noise by utilizing one of the designed offset correction

methods, such as “detrend” [41], [65], [66].

2.9. EMG Feature Extraction

Feature extraction refers to the transformation of raw data into a relevant and more

representative structure, which is known as a feature set. In order to find an informative

feature set, noise or irrelevant data needs to be discarded while highlighting the useful

information [68]. An EMG signal is one of the informative electrophysiological signals;

however, useful and important information is hidden within the complex structure of the

signal. This complexity makes EMG signal usage in medical and engineering applications

24

challenging [69]. During the last three decades, various EMG feature extraction methods

have been developed.

There are different types of EMG features. Generally, these features can be

organized in four basic types: time-domain (TD), frequency domain (FD), time-frequency

domain (TFD), and morphological features. These features have been considered in

many EMG applications and analyses, such as pattern recognition studies, and

neuromuscular disease diagnosis studies [70], [48], [68], [71], [72], [68], [64], [69], [73]–

[77].

TD features represent the most common and efficient computational set for EMG

features and have been adopted in most of the EMG studies. FD and TFD features are

used to understand the frequency contents of an EMG signal. They have been utilized in

different types of EMG analysis but mostly in fatigue of muscle analysis and recruitment

studies of MUs [71]. Morphological features have been used widely in the EMG

quantitative and semi-quantitative analysis for disease diagnosis as they characterize the

extracted MUPs of the active MUs in the studied area [43], [48], [78], [79]. These types of

features generally describe the morphological aspects of MUPs, such as shape, size,

global complexity, and local complexity [48], [80], [81]. This type of analysis characterizes:

1) the pathological effects on the morphology aspects of MUPs, and 2) the stability of

MUPTs (firing rate and recruitment style) of the active MUs in the studied area [48], [82],

[83]. Table (2.3) illustrates some of the features that are utilized in the literature with their

names, definitions, and some references.

25

Table 2.3 Most common features of EMG signals [48], [68], [69], [71], [84]–[88]

Feature

Type Feature Name Feature Definition

Tim

e D

om

ain

Featu

res

Area Integration of the absolute value of each sample of the EMG

signal over its duration.

Turn number (signal

slope changes)

The number of changes in the slope of a line from negative to

positive and vice-versa.

Peak to peak (P2P) The difference between the maximum and minimum peak

values for the EMG signal.

Root mean square

(RMS)

A statistical measure representing the square root of the mean

of the squares of all EMG signal samples.

Maximum absolute

value (MAV) The average of the absolute value of the EMG signal

Zero-crossing (ZC) The number of EMG signals crossing the zero line

Auto-regressive

coefficients (AR) The coefficients of the AR model

Mo

rph

olo

gic

al

Featu

res

Thickness The ratio of area to P2P

Turn-amplitude The ratio of amplitude to turns.

Turn-length The ratio of length to turns

Turn-area The ratio of area to turns

Turn-width The ratio of width to turns

Length “The summation of the absolute amplitude differences for every

two consecutive samples” [47]

Shape-width The ratio of area to length

Phase number The part of EMG that falls above and below the crossing line

Phase-area The ratio of area to phase

Fre

qu

en

cy

Do

main

Peak frequency Maximum frequency.

Mean power “Average power of the EMG power spectrum” [71]

Total power “An aggregate of the EMG power spectrum” [71]

Mean frequency The mean frequency of a power spectrum

Median frequency The median frequency of a power spectrum

Power density drop

ratio The maximum-to-minimum drop in power density ratio

2.10. EMG Decomposition

There are four main approaches to analyze the EMG signal quantitively, including

MUP analysis (decomposition), interference pattern (IP) method, motor unit number

estimation (MUNE), and muscle fiber jitter [89]. The MUP analysis approach, which is

26

tested in this study, analyzes the firing behavior of the active MUs and provides useful

information regarding the MU size and density in the studied area [89].

Each muscle in the skeletal system consists of a large number of muscle fibers,

and these fibers are innervated by α- nerves, which represent the LMN of the motor nerve

system [39]. A motor unit represents the functional unit of the motor neuron system, in

which each α- neuron axon innervating a group of muscle fibers represents one MU [90].

The summation of the action potentials from all the muscle fibers within one MU is called

the MUP, and during muscle contraction, the MU fires a train of these MUPs in order to

keep the stability of the muscle force [65]. The recorded EMG signal contains a

combination of electrical activity from several MUs; in other words, the EMG signal is a

combination of a series of MUPs from the active MUs, which is known as the MUPT [89],

[91]. The EMG signal is a superposition of multiple motor unit activations. Therefore, to

understand the hidden mechanism of muscle fibers and motor neurons, it is important to

decompose the signal and find the activity of constituent functional MUs [41], [92].

Different approaches regarding EMG decomposition have been suggested, such as

manual, semi-automatic, and automatic methods [89]. Figure (2.11) explains the general

idea of EMG decomposition.

27

Figure 2.11 Illustration of EMG signal decomposition [41], [63], [64]

The MUP analysis consists of seven main steps: 1) EMG signal acquisition, 2)

EMG signal pre-processing, 3) detection and segmentation of the MUPs, 4) feature

extraction from the MUPs, 5) clustering and supervised classification of the candidates,

6) resolving superimposed MUPs, and 7) finding the templates of the MUPs and the firing

pattern information of the motor units [43]. In the previous sections, the first two points

have been explained. The main EMG extracted features have also been reviewed earlier.

This type of analysis focuses more on the morphological perturbations of MUPs through

different aspects, such as shape, size, and complexity, which reflect how MUPs are

varying across an MUPT [89], [91].

The segmentation step aims to extract the EMG segments that could have an MUP

generated from an active MU in the measured muscle area. This step is applied using a

threshold crossing approach [93]–[102], and it results in a set of MUP candidates that will

28

be used in the clustering step. The aim of applying the clustering step before the

supervised classification is to get some prior information about the segmented MUPs and

to group them into initial sets of MUPTs that represent the activity of most active MUs.

There are various types of clustering algorithms that have been employed in the literature

regarding MUP analysis, such as single linkage [91], [100], [103]–[105], K-means [93],

[95], [106], [107], and many other approaches. The main challenges that face applying

clustering techniques are as follow: 1) there is no previous information about the data

labels, 2) the challenge of finding the optimal number of clusters, and 3) the different

results that could be obtained from a data set using different similarity measurements and

clustering techniques [43].

2.11. Wavelet Transforms

Wavelet transform (WT) is a well-known mathematical multiresolution

decomposition method, and it maps a function onto time-scale space. Wavelet transform

has time and frequency localizations, unlike the Fourier transform (FT). WT has been

developed to solve the non-stationarity problem of some signals, such as bioelectrical

signals [108]. WT decomposes a signal into a set of basis functions consisting of dilations,

contractions, and translations of a fixed function known as a mother wavelet. The wavelet

function is produced by the shifted mother wavelet through a shifting parameter, b, and

dilated/contracted by a scaling parameter, a, as in Equation (2.1) [109]:

𝜓𝑎,𝑏(𝑡) = 1

√𝑎 ψ (

𝑡 − 𝑏

𝑎) (2.1)

29

where 𝑎 ∈ 𝑅+ and 𝑏 ∈ 𝑅 , are the scaling and the translating parameters. When

the original signal f(t) is projected locally on a scaled and shifted wavelet function 𝜓𝑎,𝑏(𝑡),

the wavelet transforms of function f(t) are produced, as in Equation (2.2) [109]:

𝑊𝑎,𝑏 = ∫ 𝜓𝑎,𝑏(𝑡) 𝑓(𝑡) 𝑑𝑡

∞

−∞

(2.2)

Discrete wavelet transform (DWT) can be obtained by discretizing the value of the

translating parameter (𝑏 = 𝑛 𝑏0𝑎0𝑚) and the scaling parameter (𝑎 = 𝑎0

𝑚), as in Equation

(2.3) [109]:

𝐷𝑊𝑇𝑚,𝑛(𝑓) = 𝑎0

−𝑚2⁄

∫ 𝑓(𝑡) 𝜓(𝑎0−𝑚𝑡 − 𝑛𝑏0)𝑑𝑡 (2.3)

The WT is an efficient mathematical analysis method for temporally nonstationary

and spatially nonhomogeneous bio-signals [108]. As well, this approach has proven its

ability to represent precise measurements and to extract useful information from non-

stationary biomedical signals [110]–[113]. Decomposition, denoising, and pattern

classification are the most common applications of the WT in the EMG field [57], [69],

[99], [110], [114]–[119]. Phinyomark et al. [57] investigated the usefulness of extracting

EMG features from the multi-resolution wavelet decomposition process. The results

showed that the reconstructed EMG of the first and second level (detail coefficients) had

improved the class separability. Yamada et al. [120] introduced a new EMG

decomposition algorithm by adopting the principal component analysis of wavelet

30

coefficients. This method showed a higher decomposition accuracy when compared to

conventional wavelet methods. Fang et al. [114] decomposed EMG signals into their

constituent single motor units using wavelet spectrum matching, and the results were

satisfying. Table (2.4) summarizes the most common applications of WT analysis for

EMG signals.

Table 2.4 The most common applications of WT analysis in EMG signal field

# Study WT Application WT Type, Mother Wavelet

1 [121] Denoising DWT, Sym8,

2 [122] Denoising DWT, Db6

3 [123] Denoising Db4

4 [124] Visualization of the neuropathy and myopathy activity Db2

5 [118] Discrimination of healthy and post-partum CWT

6 [125] Assessment of muscle load and fatigue CWT, Db5

7 [126] A measure of muscle activation CWT

8 [127] The usefulness of the EMG features DWT, Db2

9 [128] Recruitment pattern of slow and fast fibers CWT, Db 4

10 [114] Multi-unit EMG signal decomposition Db

11 [117] Pattern classification DWT, Sym5, Bior6.8

12 [129] Pattern classification Sym4 and Db4

13 [110] Motor control DWT, Coif 5

14 [130] Pattern classification CWT, Morl

15 [131] Pattern classification DWT, Sym 5

16 [132] Muscle fatigue analysis DWT, Sym4 and Sym5

17 [133] EMG control system DWT, Sym4 and Sym5,

18 [69] Pattern classification DWT, Db7, Db8

19 [134] EMG classification Db6

20 [57] Pattern classification DWT, Db7

21 [135] EMG classification CWT, Coif 4

22 [136] EMG M-wave pattern recognition Db4

31

where:

• CWT: Continuous Wavelet Transform

• DWT: Discrete Wavelet Transform

• Db: Daubeches; Sym: Symlets; Morl: Morlet; Coif: Coiflets; Bior: Biorthogonal.

2.12. SCI Classification System

Classification is a supervised learning process, which tries to establish the

relationship between a set of features and a specific target of interest. It is mostly used in

machine learning and statistics. The classification problem could be a binary or a multi-

class problem. Figure (2.12) illustrates the two main steps of the learning process, which

include:

1)Training Step: In this step, using training instances, a model is constructed.

2)Testing Step: In this step, a label is assigned to the unlabeled testing instances using

the model constructed in the training step.

32

Figure 2.12 Classification process: training and testing steps

An EMG signal represents a useful biomarker to assist in understanding many

neuromuscular diseases, such as myopathy and neuropathy disorders [61], [129], [137]–

[142]. An EMG classification analysis is mainly performed for applications regarding

prosthetic motor control and neuromuscular disease assessment [61], [129], [139], [140],

[142]. Developing a reliable EMG classification system would assist in the diagnosis of

these conditions and would facilitate the periodic assessment of a patient. As well, it would

provide an easy system for testing the efficacy of new pharmaceutical therapies.

33

In this study, different EMG features were extracted using numerous methods

including TD, morphological, and WT analysis. The ability of these features to differentiate

between the pre- and post-lesion EMG data were tested using different classifications of

machine learning techniques. As well, a new application of deep learning was

investigated and compared to the previous approaches regarding the SCI classification

system.

2.12.1 Machine Learning Classification Technique

Neuromuscular diseases were identified based on EMG signals using different

machine learning techniques, such as support vector machine (SVM), multilayer

perceptron (MLP), and K-nearest neighbors (KNN). Guler and Koçer [143] proposed

classifying the EMG signals of myopathic, neuropathic, and healthy subjects using SVM

and MLP techniques. A comparison of the accuracy for these two applied classifiers

showed that the SVM had the better accuracy at 92.3% while the accuracy of the MLP

was 91.6%. Kaur et al. [144] classified the EMG data from healthy and diseased

(myopathic and neuropathic) subjects using two SVM classifiers. The first SVM classifier

was used to differentiate between the healthy and the diseased data sets. The diseased

data sets were then classified further with a second SVM into myopathy and neuropathy

signals.

Elamvazuthi et al. [145] used the MLP method to perform a classification task to

differentiate between three classes: myopathy, neuropathy, and a control group. The

results of this work verified the MLP as a useful tool to classify the three different

conditions based on some extracted EMG features. In the study by Oskoei et al. [64], an

34

SVM classifier was used to perform classification tasks on upper limb movements utilizing

EMG signals. The authors suggested a feature set, a data segmentation technique, and

a parameter adjustment approach for improving the SVM classifier. Combining all these

approaches offered a robust performance, exceptional accuracy, and a low computational

load for the SVM compared to that of other classifiers, like linear discriminant analysis

(LDA) and MLP. Artug et al. [88] used a scanning EMG method to build a new dataset;

four new EMG features were then extracted. MLP, SVM, KNN, and radial basis function

(RBF) classifiers were used to investigate the effect of these features. The best classifiers

were the SVM and the KNN algorithm as they obtained the highest classification accuracy

at 97.78%, while the MLP was the second-best classifier with an accuracy of 97.33%. In

Subasi et al. [61], the EMG signals of normal, myopathic, and neurogenic subjects were

classified by an SVM classifier. The results confirmed the SVM as a promising

classification technique. It was important to carefully set the kernel function and its

parameter values while building the SVM model because improper parameter settings

could result in misclassifications.

Gokgoz et al. [146] tested the effect of a multiscale principal component analysis

(MSPCA), a denoising method, and a multiple single classifications (MUSIC) feature

extraction method on classification performance. Using ANN, KNN, and SVM classifiers,

combined with a MUSIC decomposition method with MSPCA denoising, three EMG

signal patterns [normal, myopathic and ALS] were classified. The results showed that the

accuracy of the SVM was improved by 35.55%, the ANN by 39.87% and the KNN by

31.66%, in comparison to results in which the MSPCA denoising and the MUSIC

35

decomposition methods were not used. Bose et al. [147] used the cross-correlation based

feature extraction technique for the classification of neuromuscular diseases employing

the EMG signal to consider myopathy, neuropathy and healthy subjects. The authors

used the KNN and SVM classifiers for this work. The best results (100% accuracy) were

found when they used K=4 for the KNN and a Gaussian radial basis function for the SVM

classifier.

2.12.2 Deep Learning Classification Technique

Deep learning and convolutional neural network (CNN) have recently received increased

attention in several machine learning fields, such as computer vision and speech

recognition [148]. The CNN consists of multiple various building layers, and the three

main types of layers are a convolutional layer, fully connected layer, and output layer

[149], [150].

I. The convolution layer: It is an essential component of CNN, it includes linear and non-

linear operations (convolutional layer and activation function). The convolutional layer

comprises a specific number of filters (kernels) that are applied over the input data.

These filters work as detectors to extract different features of the input data. The filter

convolves with the input data elements (image: pixels, signal: data points) by shifting

it horizontally and vertically in a certain number of steps that is known as a stride. All

the outputs from the coevolution step will be combined in one volume that is named a

feature map. Next, a nonlinear function which is usually rectified linear unit (ReLU) will

be applied to the created feature maps. ReLU function has shown to be useful in

solving the vanishing gradient problem during the backpropagation process [151]

36

which helps in reducing the training time. Then, to reduce the size of the extracted

feature maps, pooling operations are applied using different types of operations (Max,

Sum, Average). Finally, the output of the last convolutional layer pass through a flatten

operation to transform it into one vector to be used later as input to the fully-connected

layer. The CNN is trained in a supervised manner, and the filters (kernels) are learned

automatically during the training process of the CNN. The hyperparameters related to

the kernels such as their size, their numbers, padding and stride need to be designed

before the CNN training process. The difference between the ML classification and

DL classification is the type of the utilized features. The ML classification uses hand-

crafted features that are extracted manually from the input data to perform the

classification task. While the DL classification automatically learns unique

representations of the input data (learned feature maps) that can classify the data into

their related classes.

II. The fully connected layer: It is a fully connected feed-forward neural network that

consists of neurons which are fully connected to all the neurons in the following layer

by learnable weights. It receives the output of the flattening step, which is the last

pooling step of the last convolutional layer.

III. The output layer: It includes an activation function that is selected according to the

type of the classification task (binary classification: Sigmoid function, multiple class

classification: Softmax function, regression-continues values: Linear function)

Despite the growing attention that has been given to deep learning techniques,

including CNNs, autoencoder (AE), and recurrent neural networks (RNNs) in the EMG

37

motor control area, to the best of this author’s knowledge, the EMG-based neuromuscular

disease classification system is still a new application, and has not been investigated

previously. Therefore, it seems reasonable to start investigating this type of learning style

and compare it to the conventional machine learning techniques that have been studied

extensively in the literature. CNN has been successful in various EMG motor control

studies, and it achieved a competitive result compared to other machine learning

classification techniques. Table (2.5) summarizes the most recent applications of deep

learning in the EMG motor control field.

Table 2.5 Previous works related to EMG signal and deep learning techniques

# Month / Year

Study DL Model EMG Feature Application Conclusion

1 June / 2019

[152]

Improved dual parallel channels convolutional neural network (IDPC-CNN).

Spectral features of sEMG

Fall detection using sEMG

IDPC-CNN is significantly better than other applied methods

2 May / 2019

[153] CNN Spectrogram images

Pattern recognition

CNN classification accuracy of 88.04 %

3 April / 2019

[154] Regression CNN Raw EMG signal

Simultaneous EMG control

CNN-based system outperformed SVM-based system

4 Apr / 2019

[154] Compact CNN Raw EMG signal

Gesture classification

CNN outperformed SVM

5 Jan / 2019

[155] CNN + transfer learning

Raw EMG, spectrograms and continuous wavelet transform (CWT)

Gesture classification

Transfer learning enhanced the performance for the CWT-based CNN

6 Oct / 2017

[156] CNN sEMG Gesture recognition

CNN achieved an average accuracy of 97.81%

7 Jul / 2017

[157] CNN sEMG Pattern recognition

CNN showed a better performance compared to SVM

8 Feb / 2017

[158] CNN sEMG images Hand gesture recognition

CNN is better than LDA, SVM, KNN, and RF (state-of-the-art methods)

9 Nov/ 2016

[159] CNN sEMG images Gesture recognition

CNN is better than LDA, SVM, KNN, MLP and RF

38

10 Oct / 2016

[160] CNN sEMG spectrograms

Robotic arm guidance

CNN achieved state-of- the-art results

11 Sep / 2016

[148] CNN Raw sEMG Pattern recognition

CNN produced accurate results with a simple architecture compared to the classical methods

39

Chapter 3

Materials and Methodology

3.1. Introduction

This chapter describes the work methodology, including all the details regarding the

EMG acquisition process. This includes the equipment, the experimental setting, and the

protocol. All of these details are described in the Materials and Experimental Procedures

section of this chapter. A brief description is made of the subjects which were enrolled. A

detailed description is then made of the surgical procedures that were required to implant

the intramuscular EMG electrodes and to create the designed SCI. The monitoring of the

subjects, euthanasia, and postmortem examinations are also explained at the end of this

section. The EMG Analysis Methods section discusses all the applied EMG analyzing

steps and methods that were implemented.

3.2. Materials and Experimental Procedures

3.2.1. Subjects enrolled

The subjects were healthy, research-naïve, adult male cynomolgus macaques

(Macaca fascicularis; n = 6; weight, 6 to 13 kg). The first four subjects received a

combination of pharmacologic treatment; the remaining two subjects did not receive

treatment.

3.2.2. The initial surgical procedure to facilitate the collection of EMG data

On the lower back of each macaque, a midline incision was placed superior to the

proximal tail, and a small pocket was dissected between the underlying muscle and the

subcutaneous fat. Additional small incisions were made on the left and the right side of

40

the tail to expose the flexor cauda longus and brevis muscles; these muscles are an

agonist-antagonist pair. A small telemetry device (PhysioTel D70, Data Sciences

International, Minneapolis, MN) was inserted into this pocket in the lower back. The

telemetry device was attached through a set of intramuscular electrodes, which were

implanted into the left and the right flexor cauda longus and brevis. The EMG signals

were measured using intramuscular electrodes with a 10 mm inter-electrode distance, a

10 mm exposed wire length, a 1000 Hz sampling frequency, and a common ground.

Figure (3.1) illustrates the experimental setup. The tail of Macaca fascicularis can

be considered a fifth limb, which is involved in the performance of functional tasks and

balance [37], [161], [162]. The tail has well-developed sensory and motor areas. EMG

data were collected by a radio frequency link to a computer for 30 days to determine

baseline tail movements. Data collection occurred Monday through Friday (excluding

holidays) for one hour daily. The attached intramuscular electrodes were implanted into

the left and the right flexor cauda longus and brevis muscles (tail muscles) and used to

measure EMG signals. In a NHP, the tail is integral to functional tasks; this includes

reciprocal movements that aid in balance during ambulation [37]. As such, EMG data from

the tail is analogous to EMG data obtained from the limb of a human being with SCI.

Baseline EMG data were collected during voluntary movement of the NHPs within their

home enclosures for 30 days. Intramuscular electrodes allow for long-term implantation

in subjects and are generally well tolerated. When compared to surface electrodes, there

is less cross-talk. As a conceptual argument, data from intramuscular electrodes

aggregates the EMG signal from muscle fibers in multiple motor units.

41

EMG data were collected from all six subjects. However, for one subject that

received treatment, the information from one side was not recorded. As such, its raw data

was not included in the work analysis. The resulting analyses represent the data of only

three of the subjects that received the combination therapy (Subjects 1, 2, and 3), and

two subjects who did not receive treatment (Subjects 4 and 5). The EMG data were

collected during the free movement of normal daily activities of the subjects (non-

stimulated manner). Figure (3.2) illustrates a sample of the recorded raw EMG data.

Figure 3.1The main components of the utilized experimental setting [5]

42

Figure 3.2 Graphical representation of the collected EMG signal [163]

3.2.3. The subsequent surgical procedure to create an experimental spinal cord

injury

At 30 days after the implantation of the transmitter, the subjects underwent a

second surgery. The anesthesia and the postsurgical pain management plan was the

same as described earlier. A small laminotomy was performed at the level of the fifth

lumbar vertebra. An epidural balloon catheter was inserted and advanced approximately

10 cm cranial, to the level of the lower thoracic spinal cord. The balloon was inflated

rapidly and remained inflated for 1 min. Conceptually, this procedure corresponds to

human SCI, in that there is a rapid transfer of energy (that is, initial balloon inflation),

followed by residual displacement of tissues such as disk material, boney fragments, or

hematoma (that is, continued balloon inflation for 60 s). The balloon was then deflated.

The catheter was removed, and the surgical incision was closed. The focus of this model

was to create a lesion that mimics human SCI. The SCI was designed to cause only

43

pathological and neurophysiological impairments without any clinical impairments that

could affect the function of the subject’s internal organs.

The radiographic image in Figure (3.3) highlights the location of the experimental

lesion in the lower thoracic vertebra marked by the red arrow. The CT image in Figure

(3.4) demonstrates how the lesion is created and the displacement of the thoracic spinal

cord by the epidural catheter. After the lesion was created, the subject remained

anesthetized for 1 h. This time period is a typical duration between injury and the

availability of emergency medical treatment in humans.

After one hour, four macaques (treatment group) received an intravenous bolus of

thyrotropin releasing hormone (TRH) (dose, 0.2 mg/kg) followed by a continuous

intravenous infusion for one hour of selenium (60 mg, 0.2 mg/kg/h). In addition, vitamin E

(dose, 80 IU) was administered orally once daily starting one day after surgery and

continuing for 90 d. In addition, two macaques (untreated control group) received an

infusion of normal saline for 1 h. These control subjects did not receive any selenium or

vitamin E. All the subjects experienced a spinal cord lesion.

44

Figure 3.3 The CT image of the inflated balloon in the epidural space of the thoracic spine. The balloon is inflated with air, which appears black in the spinal column (red arrow). Note that 60% of the column is occupied by the balloon, and the spinal cord is displaced. This image is from a cadaveric subject [6]

Figure 3.4 Standard radiograph of the catheter inserted into the epidural space via laminotomy in a cadaveric subject. The balloon is not inflated in this image [6]

45

3.2.4. Monitoring of subjects

The investigators and the veterinary staff closely monitored all subjects. The

frequency of assessment was at least twice daily and was more frequent during the

immediate post-surgical periods. The macaques were assessed and monitored for clinical

indicators of illness including limb weakness, vomiting, diarrhea, jaundice, bleeding, and

anorexia. The animals were weighed at the time of the surgical procedure, and prior to

euthanasia. The subjects were monitored for wasting and emaciation, as clinical

indicators of weight loss.

3.2.5. Euthanasia and post-mortem examination

On day 90, the subjects were euthanized by sedation with ketamine (10 to 20 mg/kg

IM) followed by sodium pentobarbital (>50 mg/kg IV). The postmortem examination was

performed immediately after euthanasia. The gross and microscopic examination

included assessment of the brain, spinal cord, heart, liver, spleen, kidneys, and bladder.

Venous blood was obtained immediately prior to euthanasia; cerebrospinal fluid was also

obtained. The experimental SCI resulted in both a physiological and a histopathological

perturbation [6] as shown in Figure (3.5).

46

Figure 3.5 (A) Photomicrograph of the spinal cord at the epicenter of the lesion. The gray and white parenchyma are disrupted by dilated myelin sheaths (spongiosis; arrows), which are widespread. These findings are noted caudal and cephalad to the lesion, in both treated and untreated subjects. Hematoxylin and eosin stain; magnification, 40×. (B) Photomicrograph of the spinal cord (white matter funiculi) at epicenter of experimental injury. There are many dilated myelin sheaths (spongiosis; black arrows), some of which contain degenerating or swollen axons (red arrows). These findings are noted caudal and cephalad to the lesion, in both treated and untreated subjects. Haematoxylin and eosin stain; magnification, 400× [6].

The research involving the first two subjects was completed at the New England

Primate Research Center, where the animals were cared for in accordance with the

National Research Council’s Guide for the Care and Use of Laboratory Animals (8th

47

edition) and the standards of the Harvard Medical School Standing Committee on

Animals. Research on the remaining four subjects was completed at the Wisconsin

National Primate Research Center and was approved by the IACUC at the University of

Wisconsin at Madison. The macaques were under close supervision by the veterinary

staff and were monitored for any adverse effects.

3.3. EMG Analysis Methods

This work represents the only analysis that has been applied using this dataset. The

recorded EMG signal was analyzed using a methodology that consisted of four stages:

EMG preprocessing, general muscle activity metric analysis, motor unit discharge

analysis, and development of an SCI classification system. The first stage included the

filtering, the removal of outliers, and the segmentation steps of EMG signals. The second

stage was performed using the most common EMG features (area, root mean square,

turns, and zero-crossing) as well as a newly proposed metric named Q-metric. The third

stage was performed using a conventional decomposition method, and a proposed

wavelet and relative power method. The fourth stage included developing SCI

classification systems using the different EMG features and classical ML techniques

(KNN and SVM). It also included a new deep learning application for an SCI classification

system using CNN and raw EMG signals. Figure (3.6) illustrates the first three stages with

the main steps of each analysis, while Figure (3.7) represents the fourth stage of the

methodology.

48

Figure 3.6 A general block diagram for the EMG analysis - Part 1

49

Figure 3.7 A general block diagram for the EMG analysis- Part 2

50

3.3.1. EMG signal preprocessing

The EMG signals were processed using three main steps which includes:

a) Removing outliers

EMG data were collected from all six subjects. However, in one subject, the

information from one side was not recorded due to a technical error. As such, the raw

data in this work represents the experience of three subjects that received the

combination therapy, and two subjects who did not receive treatment. As well, due to

some technical issues during the EMG recording process, EMG data from a few days

during the experiment were discarded.

b) Segmentation

The recording session from each day collected between 2 to 4 million EMG data

points, of which 1.5 million points were selected representing approximately 25 minutes

of data. These subsets were chosen to avoid the outliers that often occurred at the

beginning or the end of some of the recording sessions while still maintaining an equal

sample selected from each session. According to the stochastic nature of the EMG signal,

the instantaneous EMG data sample held relatively little information regarding the whole

muscle activity; therefore, another segmentation step was applied using a window of size

(1000 ms) on the EMG data of each experimental day.

c) Filtering

The physiological changes in the muscle fibers produced a myoelectric signal which

is known as the EMG signal [85]. When the EMG signal is obtained from dynamic

activities, it is usually contaminated with unwanted artifacts that might affect the

51

developed signal analysis and its interpretation. It must thus be carefully filtered before

proceeding to any forward processing step. The filtering signal process usually tries to

keep as much of the original signal information as possible, and then reduce or attenuate

the accompanying noise signals [164]. The quality of the EMG signal depends mainly on

the applied processing steps. Filtering represents an important step that eliminates the

combined noises from the raw EMG signal. The raw EMG data, obtained from daily

recordings, was filtered using:

1. Zero offset filter: eliminates the baseline offset noise and is implemented by using one

of the offset correction methods (“detrend”) in MATLAB.

2. Power-line noise filter: a notch filter with 60 Hz was also applied to eliminate the power

line noise.

3. Zero-phase bandpass filter: extracts the most effective band frequency region from

the raw signal by applying a bandpass filter (4th order Butterworth filter with a lower

and an upper cut off frequency of 10 and 450 Hz respectively). A phase shift problem

was solved by processing the input signal in both the forward and the backward

directions.

The EMG conditioning steps have been implemented using MATLAB software

(MathWorks, Natick, MA, USA).

3.3.2. General volitional muscle activity metric

3.3.2.1. Conventional EMG metrics

The goal of this section is to evaluate the ability to use some of the conventional

EMG amplitude metrics to evaluate volitional muscle activity pre- and post-SCI, as well

as while performing dynamic and multilevel muscle contractions. During the muscle

52

contraction, motor units recruit sequentially according to their size starting from the

smallest to the largest ones. First, the earliest MUs that are recruited, which are small in

size, generate a weak tension. Then to increase the level of contraction, the larger MUs

fire progressively until they reach the desired level [165]. A strong muscle contraction

generates a complex EMG signal, which is known as the interference pattern EMG signal.

This type of signal is built up from the activity of multiple motor units, and it results when

the MUs recruit at the same time, their potentials get overlapped, and they interfere with

each other [165].

A conventional EMG signal analysis, using signals detected during constant force

and isometric contractions, is useful in many applications. However, experiments that

represent real-life conditions are important to extend the reliability of these methods

especially for clinical applications [166]. It remains unclear whether existing EMG

features, which are largely used based on signals collected during constant-force

isometric contractions, are still effective for use with this type of EMG signal (dynamic

activity and multilevel muscle contractions).

An EMG signal has a stochastic and complex nature [167]. Therefore, to describe

the overall muscle activity, the averaged/windowed signal is utilized because it is more

informative than instantaneous EMG samples. EMG features are commonly calculated

by using a sliding window over the EMG signal [86], [138], [168]. The window length differs

based on the characterization of the signal (sampling frequency, total length), and the

application. The segmentation step could be applied using either a joint (overlapped) or

a disjoint windowing process [169], [170]. It has been observed that the overlapped

windowing process has a better classification performance compared to the disjoint

53

windowing [169]. The overlapped method does, however, require high computational

costs and time for some of the classification methods.

In this analysis, the filtered EMG signal for each individual day was used, then

followed with a segmentation step using a window of size 1000 ms. Initially, a group of

standard and popular EMG amplitude features were measured including area, RMS, turns

number and zero-crossing number. These features were selected according to their easy

implementation, fast computation, and class discrimination factors [71], [85].

Subsequently, the calculated features were tested in a linear mixed model to check their

ability to differentiate between the two experimental phases (pre- and post-lesion), and to

test the effect of the utilized treatment. The EMG features were calculated as follows:

1) Area is the integration of the absolute value of each EMG sample over the selected

duration and it reflects the quantity of the electrical impulse. It can be expressed as follows

[69], [71]:

𝐴𝑟𝑒𝑎 = ∑|𝑥𝑖| 𝑖 = 1, 2, … , 𝑁

𝑁

𝑖=1

(3.1)

𝑁: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 , 𝑥𝑖 = 𝑡ℎ𝑒 𝑖 − 𝑠𝑎𝑚𝑝𝑙𝑒

2) Root mean square (RMS) is a statistical measure that represents the square root

of the mean of the squares of all the EMG signal samples. The RMS reflects the average

power of the EMG signal, and it is given by the following formula [69], [71]:

54

𝑅𝑀𝑆 = √1

𝑁∑ 𝑥𝑖

2

𝑁

𝑖=1

(3.2)

𝑁: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 , 𝑥𝑖 = 𝑡ℎ𝑒 𝑖 − 𝑠𝑎𝑚𝑝𝑙𝑒

3) Zero-crossing is a simple frequency measure in the time domain which

represents the number of times that the EMG waveform crosses the amplitude zero line.

The zero-crossing counter will increase if two consecutive EMG samples satisfy the

following condition [69], [71]:

{𝑋𝑛 > 0 𝑎𝑛𝑑 𝑋𝑛+1 < 0} 𝑜𝑟 {𝑋𝑛 < 0 𝑎𝑛𝑑 𝑋𝑛+1 > 0}

4) Turns number is another feature that may measure the frequency content of the

signal by counting the number of times that the slope changes its sign from negative to

positive and vice-versa. The turns counter is incremented if three consecutive EMG data

points satisfy the following condition [48]:

{𝑋𝑛 > 𝑋𝑛−1𝑎𝑛𝑑 𝑋𝑛 > 𝑋𝑛+1} 𝑜𝑟 {𝑋𝑛 < 𝑋𝑛−1 𝑎𝑛𝑑 𝑋𝑛 < 𝑋𝑛+1}

3.3.2.2. Proposed peak number method (Q-metric)

According to the results of previous conventional EMG features and the mentioned

experimental settings (longitudinal recording, dynamic activity, and different levels of

contraction), a new general muscle activity metric is needed. The goal in this section is to

55

design a new metric, called Q-metric, to evaluate the general volitional muscle activity. In

calculating Q, the EMG data were processed based on the following fundamental

assumptions:

1. The lesion perturbs the EMG signal that was collected from the tail musculature.

2. The intramuscular electrodes detect all electrical EMG activity from all the motor units

‘close’ to the electrodes. Because motor units contain motor fibers, the EMG activity

also represents the EMG activity of all the motor fibers as well.

3. Insertion of the intramuscular electrode into the muscle may initially disrupt the tissue,

causing associated inflammation that may initially affect the signal variability; this

variability decreases as the inflammation subsides. The wire–tissue interface

therefore ‘matures’ overtime after initial implantation and produces more reliable

signals. Consequently, the EMG signal later in the recording period is less likely to be

affected by artifacts related to surgical implantation.

4. The number of peaks in the aggregate EMG signal acts as a surrogate of MUs

activation. From a clinical perspective, this activation translates into greater tail

movement.

5. The number of peaks or spikes in the interference (aggregated) EMG signal is

considered an important measurement, in which it is the only MU characteristic that

preserves itself in the interference EMG signal [171]–[173].

6. The average number of peaks reflects and estimates the activity of the MUs in the

pickup volume of the used electrode [106].

56

Figures (3.8) and (3.9) summarize the main steps of the proposed algorithm for

calculating the Q-metric which include:

a. Rectification: After the filtering step, the signals are rectified to convert all the

negative values to positive values. This process resolves positive–negative signal

cancellation.

b. Smoothing: A moving average window is applied for a continuous segment

containing 10,000 data points. This information is used to mathematically ‘smooth’

the rectified EMG signals and reduce the effect of artifacts.

c. Thresholding: The peaks with low amplitudes are discarded. This step ensures

that data from the motor units close to the electrode are included in the calculation

of Q.

57

Figure 3.8 Flowchart of the peak number algorithm

58

Figure 3.9 An illustration of the peak number method

The total number of peaks per daily recording session were calculated utilizing the

proposed EMG processing strategy. This value was reduced by the average (arithmetic

mean) number of peaks for pre-lesion period; that is, after the insertion of the transmitter

but before the creation of the spinal cord lesion. Finally, this number was divided by the

standard deviation in the number of peaks in the pre-lesion period. This approach

‘standardized’ the raw data relative to the activity in the pre-lesion period.

As such, for every day of EMG recording, a value of Q was calculated. The values of

Q were calculated separately for the muscles on the left and the right sides of the tail. A

mixed model was used to determine whether the tail movement before the lesion differed

from that afterward, and to ascertain whether combination therapy had an effect,

The first effect tested by using the model relates to the EMG consequences of the

lesion. Stated more explicitly, did the lesion cause a change in Q? The second effect

59

tested relates to whether the combination treatment (TRH, selenium and vitamin E)

resulted in improved Q scores when compared with an absence of treatment. In other

words, was the combination treatment effective?

Due to the longitudinal nature of the study, the rates of each effect were tested. When

the effects were not significant (P > 0.05), they were subsequently removed from the

model. Data from the right and left tail muscles were tested separately. The analyses

were conducted by using SAS 9.1 (SAS Institute, Cary, NC) and MATLAB (Mathworks,

Natick, MA).

3.3.3. Motor units discharge properties

To analyze and understand the discharge properties of the EMG signal, the signal

needed to be decomposed into its main functional components, which are MUPTs. The

MUPTs represents the sequences of MUPs from the active MUs within the EMG

recording area. Many decomposition methods have been developed and applied to

different types of EMG signals in automatic, semiautomatic, and manual ways [43]. The

decomposition task is difficult because it depends on many factors, such as the type of

electrode used and its position, the muscle condition (the clinical state of the

neuromuscular system of the patient), and the type of muscle contraction. An EMG

decomposition technique therefore needs to be able to address all the different factors in

effect and provide a robust result. Most of the decomposition methods require slight to

moderate muscle activity (< 50 % MVC) because the stronger muscle contraction, the

harder it is to analyze [43]. An EMG decomposition analysis helps in the

characterization/semi-quantification of pathological changes in a muscle in terms of

60

morphological changes to the MUPs. It also helps with the recruitment pattern of the MUs

and the discharge rate. This can be achieved by using different EMG features. The

recognition and semi-quantification outcomes belong to the MUs that are within the

volume of the recording of the intramuscular electrode.

In this NHP model, the induced lesion produced an SCI limited to the upper motor

neuron tracts that supply the lower motor neurons of the tail muscles [11]. Lesions of such

a nature resulted in a perturbation in the discharge properties of the MU’s, such as

abnormalities in the inter-discharge interval, the firing rate, and the floating serial

correlation coefficient [54], [55], [174]. These abnormalities have been typically

characterized in non-physiological experimental conditions, such as low force levels and

isometric contraction frequency [54], [55], [95], [96], [175]. According to the goal of this

work, EMG decomposition is a helpful tool to uncover the hidden mechanisms and

changes that result from the created SCI.

3.3.3.1. Conventional EMG decomposition method (MUPs analysis)

During the last three decades, the EMG signal has been broadly used by clinicians

and researchers as a powerful tool for the diagnosis of a variety of neuromuscular

diseases. Generally, EMG data has been analyzed qualitatively; however, more efforts

have recently been spent on quantitative EMG analyses. These analyses have been

extracted from different EMG quantitative features and evaluated according to their

diagnostic abilities. This transition is a result of the fact that qualitative tools depend on

the experience of the clinician, so they are a subjective assessment tool. Moreover,

61

longitudinal studies that were interested in measuring the severity and progression of a

disease required quantitative tools to facilitate the work [176].

The quantitative information regarding MUPs provides important diagnostic

information about neuromuscular disorders [106]. Various EMG quantitative analyses

have been introduced in the literature, including MUP analysis, IP, MUNE, and muscle

fiber jitter method [89]. EMG decomposition is one of the applied MUP analysis methods

[106]. The decomposition process includes three main steps: the detection of MUPs, the

clustering of MUPs, and the classification of MUPs into MUPTs. EMG decomposition is

the procedure that resolves the EMG signal into its constituent MUPTs based on the

morphological changes of MUPs. Each MUPT represents the electrical activity of an

individual MU; therefore, analyzing the activity of MUPTs provides information regarding

the discharge properties (firing rate) of the MUs, and the status of the MUPs that are

generated within a MUPT. In this work, an attempt was made to decompose an EMG into

its constituent functional components/motor units using a method similar to that of

Stashuk [106]. This method was established using the following three steps:

1. Detection and segmentation of MUPs

• Detection of MUPs:

The goal of this step is to isolate the active MUPs that form the MUPTs. It is known

that the active motor unit fibers closest to the electrode generate the highest amplitude in

motor unit potential; therefore, the thresholding technique [43], [94], [99], [106], [177],

[178] (>threshold value) will eliminate the low amplitude MUPs from motor fibers that are

further away. In this step, all of the signal peaks are scanned, and only the peaks that

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exceed the detection threshold are considered to be candidates for MUP positions. There

are different signal features that could be utilized for calculating the threshold value, such

as the RMS, the max absolute, and the average absolute. In this work (1.5 RMS) was

selected [43].

• Segmentation of MUPs

A window with a specific length was applied over the MUP locations that were

detected in the previous step to extract the data that falls within the window. The extracted

segments of the signal were saved as candidate MUPs [179], [180]. The length of the

applied window was chosen based on the quality of the signal; the window length needed

to be large enough to cover the whole candidate MUP waveform, which usually contained

3 to 6 spikes. After a few experiments on the recorded EMG signals, it was found that a

suitable length was 180 data points.

• MUP feature extraction:

After the detection and segmentation of the candidate MUPs, various feature

extraction methods, including time domain, frequency domain, and morphological

domain, were applied to cover the meaningful information from the MUP waveforms.

Many feature combinations were formed and tested in this work, and the results that were

reported represent an example of these combinations. They consist of fifteen features,

including area, RMS, ZC, turn number, peak to peak, thickness, phase, phase complexity,

phase area, shape width, turn amplitude, turn area, length, mean frequency, and median

frequency, which describe the morphological changes, shape, and size of the MUPs.

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Methods for calculating the selected features are explained in Chapter 2, Table (2.3). It

is important to note the size of the feature vector to avoid the curse of dimensionality [43].

2. The MUP clustering step

Clustering is the task of dividing a group of unlabeled objects into logical clusters.

These clusters contain some similar characteristics which describe these objects. The

clustering process aims to group similar objects into one cluster. Each cluster represents

the objects that are as similar as possible, while objects with more differences are divided

into different clusters. Any discriminative criteria therefore need to be able to increase the

similarity within one cluster and decrease it between groups.

After the detection and segmentation step for the MUP is performed, the estimated

number and the template shape of the MUPTs of the significant contributing motor units

need to be determined. These requirements can be achieved by applying the clustering

step. The first objective of this step is to allocate the candidate MUPs into a set of MUPTs

(clusters), in which each MUPT represents the electrical activity of one MU. Furthermore,

the average of all the MUPs that belong to one cluster (MUPT) defines the template shape

of that MUPT. Finally, the clustering step can help in determining the firing pattern of each

MUPT that is extracted.

3. MUP classification step

Decomposition techniques are designed mostly with a clustering step that is

followed by an MUP classification step [46], [181], [182]; however, some of these

techniques depend only on the MUP clustering step [178], [183], [184]. For the first group

of techniques, the thresholds of each MUPT template are determined after acquiring

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some initial information, such as the number of classes (MUPTs), the initial shape

templates of MUPTs, and the MU firing rate statistics. The MUPs are then classified into

MUPTs through a number of iterations. When the MUPTs are stable, the classification

process is terminated. Finally, using the extracted MUPTs, different features are

calculated and based on these features, the status of a muscle is determined [44], [185].

In this work, according to the clustering results, the classification step was not applicable

(see Chapter 4 for more details).

3.3.3.2. The proposed EMG discharge properties analysis utilizing wavelet

transform decomposition and relative power

In this work, the intramuscular EMG data were analyzed using the WT as an EMG

decomposition method, and relative power (RP) as a metric of the active MUs within each

WT sub-band. Specifically, this work addresses the following questions:

1. During the pre-lesion period, do the subjects demonstrate symmetrical volitional tail

activity as measured by EMG activity?

2. In the post-lesion period, is there a change in the EMG activity attributed to the

experimental spinal cord injury and how it could be characterized in terms of

frequency content?

3. What is the difference in the EMG activity between the control and the treatment group

in the post-lesion period (treatment effect)?

65

In the NHP model of this work, the induced lesion produced an SCI limited to the upper

motor neuron tracts that supply the lower motor neurons of the tail muscles [11]. Lesions

of such nature result in a perturbation in the discharge properties of an MU; i.e.,

abnormalities in the inter-discharge interval, firing rate, and floating serial correlation

coefficient [54], [174]. These abnormalities have been typically characterized in non-

physiological experimental conditions, such as low force levels and isometric contraction

frequency [54], [95], [96], [175]. In this work, the EMG data were collected when the

subjects engaged in physiological activities (i.e., locomotion in a cage). The proposed

EMG analysis method consisted of the following two steps:

A. Decomposition step

A decomposition process was applied using wavelet transforms. Each WT sub-band

was assumed to represent the firing rate of a group of MUs. As well, it was assumed that

the RP of each individual sub-band reflected the level of activity for these MUs. Increases

or decreases in the RP may thus characterize the recruitment pattern process of the MUs

throughout different conditions in the experiment. The filtered EMG signals were broken

down into seven frequency sub-bands using the WT. DWT were selected for this work

because they have non-redundant results, and require less computational time and costs

[186], [187]. A Daubechies mother wavelet of fourth-order, ‘db4’, was used due to its

similarity to the triphasic pattern of the motor unit action potential, as seen in Figure (3.10)

[188]. Consistent with the analysis of other bio-signals, DWT decomposition was

performed using six levels [87], [187], [189]–[191]. Figure (3.11) illustrates the wavelet

analysis which was performed in two steps including:

66

a) The EMG signals were decomposed into seven sub-bands, one approximate

coefficient (cA6), and six detailed coefficients (cD1, . . ., cD6).

b) The EMG signal was then reconstructed at each level using an inverse discrete

wavelet transform. Seven EMG reconstructed signals (A6, D1, . . ., D6) were

obtained from their coefficients (cA6, cD1, . . ., cD6). Table (3.1) shows the

frequency ranges of the seven EMG sub-bands.

Figure 3.10 Mother wavelet function (db4)

Table 3.1 The frequency ranges of the seven EMG reconstructed sub-bands

Wavelet

Level Frequency Range/ Hz

Reconstructed EMG Sub-

Bands

1 250 - 500 D1

2 125 - 250 D2

3 62.5 - 125 D3

4 31.25 – 62.5 D4

5 15.63 – 31.25 D5

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6 7.81- 15.63 D6

6 0- 7.81 A6

Figure 3.11 Wavelet analysis; decomposition and reconstruction steps [163]

B. Feature extraction step

To evaluate the changes in EMG sub-bands during different phases of the

experiment, these changes were characterized using the RP. The probabilistic distribution

of the spectral power was quantified by calculating the relative power of each spectral

component [192]. To obtain the RP, the power spectral density was first determined for

68

each reconstructed EMG sub-band signal. The RP for each individual sub-band was then

calculated using the following formula [193]:

𝑅𝑃(%) =𝑆𝐵𝑃

𝑇𝑃, (3.3)

where:

𝑅𝑃: is the relative power of the desired sub-band.

𝑆𝐵𝑃: is the power of the sub-band, such as the power of A6, D1, … or D6.

𝑇𝑃: is the total power of all the sub-bands (A6 + D1, ..., + D6).

C. Statistical analysis

The goal was to address the research questions by analyzing specific subsections

of the data and testing specific effects. To test the lateralization effect (attributed to limb

dominance), only the pre-lesion data were considered for the two sides (left and right).

The effect of the lesion was then tested using the data from both the pre- and the post-

lesion periods, and two separate models were fitted, one for each side. To test the

treatment effect, two separate models (left and right) were fitted using only the post-lesion

data. The data in this work is considered a clustered longitudinal dataset with three levels;

days are nested in frequency sub-bands, which are in turn nested within subjects (day is

Level 1, sub-bands is Level 2, and subject is Level 3). As a result of such a hierarchical

structure, shown in Figure (3.12), a non-independence problem for the observations

occurred, which required an appropriate statistical analysis method. A mixed model is a

statistical method that was developed specifically to address the non-independence

69

(correlated or repeated measurements) issue by including the random effect term in its

model. The developed mixed models controlled for the within-subject correlation by

including a random effect for the subject and the nested sub-bands within each subject,

with a variance-covariance structure and a restricted maximum likelihood estimation. The

models were implemented in the statistical software R using the lme4 package. The

significance level was set to 𝛼 = 0.05. The RP for any given subject at day i for sub-band

j nested within subject k, denoted as RPijk, is represented in Equation (3.4) [see a

summary of the parameters in Table (3.2)]:

𝑅𝑃𝑖𝑗𝑘 = 𝛽0 + 𝛽1 𝐷𝑎𝑦𝑖𝑗𝑘 + 𝛽2 𝐸𝐹𝐹𝐸𝐶𝑇𝑗𝑘 + 𝛽3 𝐸𝐹𝐹𝐸𝐶𝑇𝑗𝑘 𝐷𝑎𝑦𝑖𝑗𝑘

+ 𝛽4𝐹𝑟𝑒𝑞𝑗𝑘 + 𝛽5 𝐹𝑟𝑒𝑞𝑗𝑘 𝐷𝑎𝑦𝑖𝑗𝑘 + 𝛽6 𝐹𝑟𝑒𝑞𝑗𝑘 𝐸𝐹𝐹𝐸𝐶𝑇𝑗𝑘

+ 𝛽7 𝐹𝑟𝑒𝑞𝑗𝑘 𝐸𝐹𝐹𝐸𝐶𝑇𝑗𝑘 𝐷𝑎𝑦𝑖𝑗𝑘 (𝑓𝑖𝑥𝑒𝑑) + 𝛼0𝑘

+ 𝛼1𝑘 𝐷𝑎𝑦𝑖𝑗𝑘 + 𝛼0𝑗𝑘 + 𝛼1𝑗𝑘 𝐷𝑎𝑦𝑖𝑗𝑘 + 𝜀𝑖𝑗𝑘 (𝑟𝑎𝑛𝑑𝑜𝑚)

(3.4)

Table 3.2 Parameters used in the equation of the mixed model

Parameter Definition

𝑹𝑷𝒊𝒋𝒌 response (relative power)

𝑫𝒂𝒚𝒊𝒋𝒌 experiment day

𝑭𝒓𝒆𝒒𝒋𝒌 frequency sub-band (D1, D2, D3, D4, D4, D6, and A6)

EFFECT the main effect in the models, so for:

Model 1: the side effect [categorical variable with two levels (left and right

side)]

Model 2: the lesion effect [categorical variable with two levels (pre and post-

lesion)]

Model 3: the treatment effect [categorical variable with 2 levels (control and

treatment group)]

𝜷𝟎 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐨 𝜷𝟕 the fixed effect associated with the following: intercept, Day, EFFECT,

EFFECT*Day, Freq, Freq*Day, Freq* EFFECT, and Freq* EFFECT*Day

70

𝜶𝟎𝒌, 𝜶𝟏𝒌 the subject random effect associated with the intercept and Day slope,

respectively

𝜶𝟎𝒋𝒌, 𝜶𝟏𝒋𝒌 the random effect of a frequency sub-band nested within a subject associated

with the intercept and Day slope respectively

𝜺𝒊𝒋𝒌 random error

Figure 3.12 Collected Data Structure: The data in this work was collected from the left (L) and the right (R) side of the tail for five subjects throughout the days of the experiment (d1, d2, …, dn) for the pre- and post-lesion period. The data for each day was decomposed into seven frequency sub-bands (D1, D2, D3, …, A6). Three of the subjects received a treatment combining TRH, selenium, and vitamin E post-lesion (treatment group); the remaining two subjects did not receive any treatment post-lesion (control group) [163]

71

3..4 SCI Classification System

3.4.1 SCI classification system using classical ML classification technique

In this analysis, an SCI classification system has been investigated using various EMG

features and ML classification techniques. This analysis has been performed using the

following steps:

1. EMG features extraction and pre-processing

All the features that were measured in the previous sections, including the four EMG

amplitude features (area, RMS, turn number, and ZC number), the Q-metric, and the RP

of the EMG frequency sub-bands (RPA6, RPD6, RPD5, RPD4, RPD3, RPD2, and RPD1),

have been used as input data for the generated SCI classification systems. Two systems

were built; the first system utilized the EMG amplitude features, while the second system

was based on the two newly proposed features.

According to the experimental setting, more post-lesion instances were available than

the pre-lesion class, which generates a class imbalanced dataset. The imbalanced

dataset, in turn, leads to a misclassification problem that reduces the accuracy of a

classifier. The degree of imbalance is usually small, but it appears extensively in certain

applications, such as medical diagnoses, and fraud detection applications. As an

example, the medical dataset often included a higher percentage of ‘normal’ class

samples as compared to the ‘abnormal’ or ‘interesting’ class samples. In this case, the

misclassification rate of considering an ‘abnormal’ instance as a ‘normal’ instance is

higher than vice versa, which results in a poor diagnostic system. To resolve this issue,

there are some pre-processing steps that could be applied to rebalance the classes within

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a dataset. In the literature, the problem of an imbalanced dataset was solved by re-

sampling the data in two ways, either under-sampling the majority class or oversampling

the minority class, such as in the synthetic minority oversampling technique (SMOTE)

[194].

In this work, SMOTE was applied to address the imbalanced class issue. SMOTE is

one of the pre-processing techniques that over-sample the pre-lesion (minority class) and

generate a balanced dataset. The algorithm takes samples of the feature space for the

pre-lesion class and its nearest neighbors and merges them to generate new instances

[194], [195].

2. Classification techniques

In this study, two different machine learning classification algorithms were used.

These classifiers were chosen according to the requirements and the feasibility of the

EMG signal dataset. The classification results were analyzed and compared to find the

most reliable type. The SVM and KNN algorithms were the two classification techniques

that were employed. The SMOTE approach and the classification algorithms were

implemented using a special software (Weka) by the University of Waikato, New Zealand.

The two different machine learning classification algorithms are as follow:

a) Support vector machine

The SVM technique is one of the machine learning algorithms that has been

applied to different biomedical tasks, including classification and regression [196]. The

SVM is a binary classifier that aims to differentiate between two classes by creating an

optimal hyperplane that separates the classes and satisfies the following equation:

73

𝑤. x + b = 0 (3.5)

where 𝑤 is a weight vector, 𝑥 is an input vector, and 𝑏 is a bias term [197]. For the linearly

separable classification problem, the SVM algorithm tries to find the separating

hyperplane with a maximum (margin). Given that the two classes are -1 and +1, the

separability assumption refers to the existence of some values of w and b in which all the

input vectors satisfy the following constraints in Equations (3.6) and (3.7) [198]:

𝑤. x + b ≥ +1 for ∀ y = +1 (3.6)

𝑤. x + b ≥ +1 for ∀ y = −1 (3.7)

These two can be combined into Equation (3.8):

𝑦(𝑤. x + b) − 1 ≥ 0 for ∀ y (3.8)

In this study, the kernel function of the SVM was tested empirically and the optimal

result was obtained using the Pearson VII function-based universal kernel (PUK) [199],

[200]. Figure (3.13) illustrates a general schematic of the SVM classification.

74

Figure 3.13 General schematic illustrating the SVM classification technique

b) K-nearest neighbors

KNN is a simple classification technique; however, it is an effective method that has

been applied to various applications such as healthcare, handwriting detection, and

image recognition [201]. The KNN is a non-parametric and a lazy approach, which refers

to the fact that no assumptions are made, and the structure of the model is based entirely

on the data given to it. This algorithm is popular for its speed and simplicity, and it has

been utilized widely in EMG classification studies [146], [147], [202]. Like any other

machine learning algorithm, KNN has some drawbacks or limitations that include

choosing the appropriate value of K [146], [203]; however, by varying the value of K, the

increase in generalization and accuracy of the KNN classifier can be achieved. Figure

(3.14) illustrates the three main steps of applying the KNN technique. These steps are

listed below [204], so as to predict a label for point (?) in Figure (3.14):

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1. Firstly, calculating the distance between point (?) and all the remaining points by using

distance measuring methods, such as Euclidean distance, Manhattan distance,

Minkowski distance, or Hamming distance.

2. Secondly, finding the closest K neighbors to point (?).

3. Thirdly, classifying point (?) by the majority voting of the K neighbors, so if K=3, it

would be labelled as an orange star, while if K=7, it would be labelled as a green dot.

Figure 3.14 A graphical illustration of the K-nearest neighbor classification technique

3.4.2 SCI classification using DL technique compared to ML technique

A comparison study has been performed between two SCI classification systems

using DL classification (CNN) and ML (KNN), the main steps of the analysis as follows:

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1. EMG data

In this section, the filtered EMG signal for each individual day of the experiment was

segmented into a series of disjoint windows of size 1000 ms. A different number of EMG

segments were next tested as inputs to the classification system. After multiple trials,

53,350 pre-lesion EMG segments and 135,300 post-lesion EMG segments combined for

all the subjects were chosen to be used as classifier inputs. This number was selected as

a compromise between the improvement in the classification accuracy and the time

consumed. According to the nature of the experiment, the collected data was imbalanced

since it was collected for more days during the post-lesion period compared to the pre-

lesion period. An imbalanced dataset leads to inaccurate classification results [195]. To

address this problem, the dataset was corrected by applying a random over-sampling

technique for the pre-lesion class. The number of samples in the pre-lesion class was

increased to be equal to the number of samples in the post-lesion class, so a total of

270,600 instances were used as inputs to the classification system.

2. Classification techniques

Two classification techniques have been used in this comparison. These techniques are

as follows:

i) KNN classification

Using the prepared EMG segments, four of the standard EMG amplitude features

were extracted, which included the area, the RMS, the turns number and the zero-

crossing number. All the features were then standardized and normalized. The created

feature vector was then utilized as an input to the KNN classifier. Different K values were

tested, and the best accuracy was obtained using k=9.

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ii) CNN classification

In this work, the CNN architecture was chosen empirically by running multiple

experiments with different architectures by considering various numbers of layers, and

different numbers of filters with different sizes and numbers of strides. The final selected

network consisted of five blocks, illustrated in Figure (3.15) and Table (3.3). The CNN

was structured as follows:

Input: An EMG segment (1000 x 1)

Block 1 was composed of:

- A convolutional layer with 32 filters having a size 5 x 1 and a stride of 2

- An activation layer using rectified linear units (ReLUs) as activation functions

- A subsampling layer (max-pooling); also, a dropout of 0.2 was applied to reduce

the overfitting.


- A convolutional layer with 32 filters having a size of 5 x 1 and a stride of 2

- An activation layer of ReLUs used as activation functions

- A subsampling layer (max-pooling), and a dropout of 0.2 was applied






78




These four blocks were followed by:

A global average pooling layer (GAP) which computed the average value of each

individual input feature map and yielded a single feature map obtained by concatenating

the computed average values.

A fully connected layer (FC) of size 100 x 1 with ReLUs used as activation functions

and a dropout of 0.3 was also applied.

An output layer: A sigmoid activation function was chosen for the output layer to satisfy

the binary classification requirement. This was done to classify the input data using the

output from the FC layer into either a pre-lesion class (< 0.5) or a post-lesion class (>

0.5).

Table 3.3 Optimized CNN architecture

No Architecture Parameters Output Shape No. of Parameters

1 CONV1: 32 x (5,1), stride: (2) 500 x 32 192

2 Maxpool: (2,1), stride: (2) 250 x 32 0

3 Dropout1: (0.2) 250 x 32 0

4 CONV2: 32 x (5,1), stride: (2) 125 x 32 5152

5 Maxpoo2: (2,1), stride: (2) 63 x 32 0

6 Dropout2: (0.2) 63 x 32 0

7 CONV3: 64 x (3,1), stride: (1) 63 x 64 6208

8 Maxpoo3: (2,1), stride: (2) 32 x 64 0

9 Dropout3: (0.2) 32 x 64 0

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10 CONV4: 128 x (3,1), stride: (1) 32 x 128 24704

11 Maxpoo4: (2,1), stride: (2) 16 x 128 0

12 Dropout4: (0.2) 16 x 128 0

13 Global Average Pooling 128 0

14 FC: 100 (ReLU) 100 12900

15 Dropout1: (0.3) 100 0

16 Output: 1 (Sigmoid) 1 101

Total No of Parameters 49257

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Figure 3.15 Convolutional neural network structures utilized to build the EMG-based SCI classification system

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Chapter 4

Results and Discussions

4.1. Introduction

This chapter describes the detailed results obtained after the implementation of the

proposed methodology in Chapter 3. The current chapter is organized as follows:

• The general volitional muscle activity analysis is illustrated in two sections.

o The first section reports the results of the fitted statistical models for the four

conventional EMG amplitude features.

o The second section reports and illustrates the results of the proposed peak

number method (Q-metric).

• The analysis of motor unit discharge properties is described in another two sections.

o The third section presents and illustrates the results of using a conventional

EMG decomposition method.

o The fourth section reports and illustrates the results of the proposed EMG

discharge analysis approach using a wavelet decomposition technique and

relative power.

• The SCI classification system is described in four separate sections.

o The first section explains and illustrates the definition and the mathematical

formula for the classifier performance evaluation methods which were used.

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o The second section includes the results of the SCI classification system using

the four EMG amplitude features.

o The third section describes the results of the SCI classification system using

the proposed Q-metric and WT-RPs features.

o The fourth section presents and compares the results of the new deep learning

(CNN) application as an SCI classification system to those of the classical KNN

classification technique.

4.2. General Volitional Muscle Activity Metric

4.2.1. Conventional EMG metrics

In this section, the popular features in the analysis of the EMG signal and the most

utilized features that describe the EMG signal amplitude have been calculated. These

features are area, RMS, turns, and ZC, which have been chosen based on easy

implementation, fast computation, and class discrimination factors [71], [85]. Each

individual feature was used as a response variable in a statistical mixed model to test the

effect of the created lesion and the combination of treatments that was utilized.

Tables (4.1) and (4.2) present the results of the statistical models of the left side EMG

signals that related to the effect of the created lesion and the treatment that was used

respectively. In Table (4.1), the results showed a significant difference (p-value <0.05) in

the turns and the ZC value between the pre- and the post-lesion period, with increments

in the post-lesion period of Turn/pre = 0.420, Turn/post = 0.49, ZC/pre = 0.507, and

ZC/post = 0.544. In comparison, the statistical models of the lesion effect using area and

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RMS had nonsignificant differences (p-value>0.05), as seen in Table (4.1). The treatment

effect models showed nonsignificant results using all four of the calculated features (p-

value>0.05), as seen in Table (4.2).

Tables (4.3) and (4.4) present the results of the statistical models of the right side

EMG signals that are related to the effect of the lesion that was created and the treatment

that was used respectively. ZC showed a significant difference between the pre- and post-

lesion values (p-value<0.05), with a reduction in the post-lesion phase of ZC/pre = 0.801

and ZC/post = 0.636, while the area, the RMS, and the turns had a nonsignificant

difference between the two phases. The results related to the treatment effect models

showed nonsignificant results using all four of the calculated features (p-value>0.05), as

seen in Table (4.4).

As can be seen from Tables (4.1) to (4.4), there is no consistency in the results.

On the left side, the Turn and the ZC showed a significant difference with the increments

in their values in the post-lesion period. In comparison, on the right side only the ZC

showed a significant difference with a reduction during the post-lesion period. However,

the significant results in the ZC on both sides may imply a potential effect of the created

lesion on the frequency content of the EMG signals because ZC represents a simple

frequency measure in the time domain. This disturbance or change in the parameters of

the frequency fits the inferences of previous works that referred to an alteration in the

recruitment pattern and firing rate of motor units after the occurrence of a UMN lesion

[54], [55], [205].

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Table 4.1 The results of the statistical model (lesion effect) of the conventional EMG features for the left side

Feature Mean (Pre) Mean (Post) p-value

Area 0.459 0.244 0.964

RMS 0.504 0.315 0.075

Turns 0.420 0.492 < 0.001

ZC 0.507 0.544 < 0.001

Table 4.2 The results of the statistical model (treatment effect) of the conventional EMG features for the left side

Feature Mean (Ctrl) Mean (Tr) p-value

Area 0.365 0.338 0.171

RMS 0.434 0.384 0.230

Turns 0.478 0.435 0.208

ZC 0.450 0.601 0.797

Table 4.3 The results of the statistical model (lesion effect) of the conventional EMG features for the right side

Feature Mean (Pre) Mean (Post) p-value

Area 0.260 0.321 0.576

RMS 0.079 0.328 0.106

Turns 0.765 0.565 0.894

ZC 0.801 0.636 < 0.01

Table 4.4 The results of the statistical model (treatment effect) of the conventional EMG features for the right side

Feature Mean (Ctrl) Mean (Tr) p-value

Area 0.282 0.299 0.589

RMS 0.206 0.202 0.802

Turns 0.718 0.613 0.083

ZC 0.689 0.749 0.438

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4.2.2. The proposed peak number method (Q-metric)

According to the results obtained from the conventional EMG metrics and the unique

dataset (longitudinal data recordings, dynamic activity, and different levels of contraction),

a new muscle activity metric was needed. In the methodology section, a new metric,

called Q-metric, was proposed by applying multiple signal processing steps, which

included filtering, rectifying, smoothing, thresholding, and counting the number of peaks

that satisfied the calculated threshold. Next, a mixed model was fitted to test the effect of

the created lesion and the treatment that was used on the EMG signal represented in the

Q-metric.

Table (4.5) summarizes the Q values at key time points, and Figure (4.1) represents

a fitted linear regression model for the aggregate data of Q before and after the creation

of the lesion. Consistent with a maturation of the wire–muscle interface, the value of Q

for both the left and the right side of the tail increased during the pre-lesion period. Im-

mediately after the lesion, the value of Q decreased on both the left and the right side in

both the treatment and control groups. The Q scores before and after the creation of the

spinal cord lesion were significantly different (left side, P = 0.021; right side, P = 0.01)

with a reduction in the mean value of the Q-metric during the post-lesion period. This

finding suggests that the lesion led to decreased EMG activity, resulting in decreased tail

movement, and supports the construct that Q can be used as a measure of impairment

after experimental SCI. On the left side, the treatment group was associated with a trend

toward higher Q-values when compared with the untreated control group (P = 0.075); this

effect was not noted on the right side (P = 0.519). Post-lesion data was compared to the

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pre-lesion baseline to identify consistent changes across the subjects. Fitted Q values

(Table 4.1) increased with time in both groups, although the values on both sides were

higher after treatment. Overall, the EMG data were insufficient to attribute the effect to

the treatment.

Figure 4.1 The aggregate fitted Q-values, representing tail movement, are plotted for the left (top) and the right (bottom) side. Values are normalized to a range between 0 and 1, with higher values indicating greater muscle activity. Of note, on both the left and the right side, the Q values decreased immediately after the creation of the lesion. On the left side, the effect of the lesion in the treatment group (orange) was attenuated, compared with that of the nontreatment group (grey). On both the left and the right side, the Q-metric improved over time [6]

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Table 4.5 Standardized Q-metric

Subject Day of

implantation

Pre-lesion,

midpoint

Postlesion,

day 1

Post-lesion,

midpoint

Study end

Right side

1 0.548 0.693 0.016 0.322 0.645

2 0.553 0.696 0.016 0.320 0.639

3 0.552 0.696 0.016 0.320 0.641

4 0.604 0.731 0.014 0.283 0.566

5 0.585 0.719 0.015 0.296 0.591

Left side

1 0.855 0.902 0.005 0.103 0.207

2 0.695 0.793 0.011 0.217 0.435

3 0.798 0.863 0.007 0.144 0.288

4 0.836 0.888 0.006 0.117 0.235

5 0.948 0.965 0.002 0.037 0.073

The Q-metric for each subject increased after the time of insertion and reached a

plateau during the pre-lesion period. This pattern reflects the maturation of the wire–

muscle interface. The Q-metric decreased immediately after the creation of the lesion and

gradually increased (improved) over time. The values of Q relative to time are nonlinear.

Data from the pre- and post-lesion period suggest that a change is taking place and can

be measured in a quantifiable method. Given the EMG data from these experiments, Q

can be used to evaluate impairment after TSCI. Specifically, Q decreased immediately

after the experimental SCI and increased over time; however, the evidence was

insufficient to ascribe a treatment-associated effect. Regardless, the results from these

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experiments have provided insights into the statistical variances of Q and will serve as a

guide or future experiments.

4.3. An Analysis of the EMG Discharge Properties

The discharge properties of the collected EMG signals were studied using two

different approaches. First, one of the conventional EMG decomposition techniques was

used. It was similar to that of Stashuk [106], which had been widely used in the literature.

This method was applied to test its efficacy with the unique nature of the EMG data

(dynamic, longitudinal, and multi-levels of contraction) from the current study. Second, a

combination of wavelet transforms, and relative power was used as a decomposition

method. The frequency content of the EMG signals was studied by decomposing the

signal into its constituent frequency sub-bands. The relative power of each sub-band was

then calculated and analyzed statistically using a mixed linear model.

4.3.1. Conventional EMG decomposition

In this section, the results after the implementation of the conventional EMG

decomposition method (isolating MUPs) are presented. This method includes three main

steps: MUP detection and segmentation, MUP clustering, and MUP classification into

MUPTs. The segmentation and MUP detection steps were applied using a thresholding

technique. This step requires choosing a certain threshold level and a specific length for

the segmentation window; in this study, it was 1.5 RMS [43] and 180 ms respectively.

According to this method, the EMG peaks that exceeded the selected threshold level were

then used as the center point for the segmentation window. The EMG data points that fell

within the selected window were finally segmented and saved as candidate MUPs.

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Next, various types of morphological, time, and frequency domain features were

measured for each MUP segment, including area, RMS, ZC, turn number, peak to peak,

thickness, phase, phase complexity, phase area, shape width, turn amplitude, turn area,

length, mean frequency, and median frequency. The feature vector that was calculated

was later used as input for the clustering step. The goal of the clustering step was to find

the initial MUP templates to be used as labels to classify the candidate MUPs, using a

supervised classification technique in the third and final step.

Figure (4.2) illustrates some results from a previous study in the literature [43]; the first

column represents the candidate MUPs, the second shows valid MUPT templates, and

the last column shows the firing pattern for the MUs that was achieved by tracking the

MUP templates within the signal.

Figure 4.2 Example of valid and invalid MUPs, their MUP templates, and firing patterns [43]

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Generally, valid MUP templates should resemble the results shown in the second

column of Figure (4.2). In this work, after applying the first two steps of the method (MUP

detection and segmentation, followed by the clustering step using the K-means method

with different values of K), invalid MUP templates were obtained, as shown in Figure (4.3).

The templates that were obtained confirmed that the applied decomposition method was

not applicable and could require EMG signals with certain characteristics that were not

available in the collected EMG dataset including high sampling frequency and isometric

muscle activity with a low level of contraction. Based on the obtained results (invalid MUP

templates), it was not possible to continue with this method, and another strategy needed

to be investigated.

(1)

(2)

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(3)

(4)

(5)

92

(6)

Figure 4.3 From (1) to (6), different samples of the detected and clustered MUPs using the K-means clustering method

and fifteen various EMG features

4.3.2. The proposed EMG discharge properties analysis utilizing wavelet

transform decomposition and relative power

In this section, the results were presented in the form of answers to the three research

questions that were stated in Chapter 3. These questions are as follows:

1) During the pre-lesion period, do the subjects demonstrate symmetrical volitional tail

activity as measured by EMG activity?

To test the symmetry of the tail muscle activity on both sides, the RP of the EMG

signals during the pre-lesion period were analyzed using a linear mixed model. The

interaction of the frequency sub-band and the side variable in the statistical model

demonstrated a significant effect (p-value < 0.001). Given that the side*frequency

interaction is significant, Tukey’s mean comparisons were generated to identify what

means differed significantly. The results in Figure (4.4) suggest that the side variable had

a significant effect on the RP value of the D1, D2, D4, and D5 sub-bands. The estimated

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mean of the RP for the D1, D2 and D4 sub-bands of the left side was significantly higher

than that of the right side. In contrast, the estimated mean of the RP for the D5 sub-band

on the left side was significantly lower than that of the right side. The estimated mean for

the RP across the different frequency sub-bands for both sides is illustrated in Figure

(4.4). Taken together, these results showed that the left and right tail muscles had

asymmetrical activation.

Figure 4.4 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to the creation of SCI for the left and the right side of the tail. Of note for each band, there is a difference in the RP value when compared to the left and the right side of the tail. The D2 sub-band of the left and the right side has the maximum RP, and the significant difference between the two sides is at the D1, D2, D4, and D5 sub-bands. The star indicates a significant difference

[163]

In human beings, the construct of limb dominance or preference is well accepted;

i.e., most people use their right arm for functional tasks, and a minority use the left arm.

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A select few people can use both arms equally well (ambidextrous). The question of

whether monkeys have limb dominance remains a subject of scientific inquiry. In this

analysis, there is asymmetry related to the RP of EMG data derived from the left and the

right tail muscles. These results suggest that the long-tailed macaque (Macaca

fascicularis) exhibits limb preference and/or dominance. This is also consistent with the

observations of the veterinary staff involved with this project. Generally, limb dominance

refers to the preferential use of one limb to perform functional tasks [206]. This asymmetry

in the pre-lesion (normal control) period has implications for any analysis subsequent to

the experimental SCI. Conceptually, volitional motor control involves a two-circuit

pathway; i.e., the upper motor neurons and the lower motor neurons of the corticospinal

tracts. The upper motor neurons originate in the cortex, travel the internal capsule and

pyramids, and terminate into the grey matter of the spinal cord. The lower motor neurons

originate in the grey matter of the spinal cord, exit via the nerve roots and reach the

muscles via the plexus and peripheral nerves. The data suggest that the neural network

that controls the left and right neurophysiological circuit is not symmetrical; therefore, it

stands to reason that experimental SCI will have different effects on the separate circuits.

This is consistent with human disease, to the extent that a lesion in one part of the central

nervous system may have different manifestations related to the limbs. As such, the

effects of the lesion, from a neurological perspective, were analyzed in Model Two

separately for both the left and the right side.

2) In the post-lesion period, is there a change in the EMG activity attributed to the

experimental spinal cord injury and how it could be characterized in terms of frequency

content?

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To answer this question, the effect of the created lesion was analyzed using a linear

mixed model. The effect of the lesion was studied by testing the difference between the

EMG characteristics during the pre- and post-lesion period. The interaction of the

frequency sub-band and the lesion variable in the statistical models of both sides

demonstrated a significant effect (p-value < 0.001). Given that the lesion*frequency

interaction was significant, Tukey’s mean comparisons were generated to identify which

means differed significantly. Figures (4.5) and (4.6) summarized the estimated mean of

the RP values for different frequency sub-bands of the pre- and post-lesion group for the

two sides. On the left side, the estimated mean of the RP for the D4, D6 and A6 sub-

bands was significantly higher in the post-lesion period compared to the pre-lesion period.

On the right side, the estimated mean of the RP for the D4, D6, and A6 sub-band was

also significantly higher in the post-lesion period. In comparison, the estimated mean of

the RP for the D1 and D2 sub-bands was significantly lower in the post-lesion period

compared to the pre-lesion period. The results suggest that the created lesion had a clear

effect on the discharge properties of the MUs, and with this technique, changes in

discharge properties could be detected even when there is no clinical evidence.

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Figure 4.5 The estimated mean of the RP of seven reconstructed EMG sub-bands prior and post to the creation of the SCI for the left side of the tail. Of note, the RP values for the frequency sub-bands (D4, D6, and A6) are significantly higher in the post-lesion period. The star indicates a significant difference [163]

In this analysis, there was a difference in the RP values when comparing the pre-

and the post-lesion data; this was statistically significant. Specifically, the low frequency

(LF) sub-bands (D4, D6, and A6) increased significantly during the post-lesion period on

both sides. As well, the high frequency (HF) sub-bands (D1 and D2) decreased

significantly during the post-lesion period on the right side. These results are consistent

with the literature [54]. As a general construct, neurological disease is associated with

abnormalities in the firing of the motor units and leads to a lower discharge frequency

[54], [55], [205]. This, in turn, can affect the distribution of the high and the low-frequency

components of the EMG signals. This shift in the RP value may be related to the

97

disorganized, spontaneous firing of single or multiple motor units, which affect the

frequency distribution. Speculatively, a component of these abnormalities was related to

spasticity; in SCI, the recruitment of Type I or Type II motor units could be preferentially

affected, which could in turn distort the frequency content. The elucidation of these

relationships requires further research. With this method, a change in the recruitment

behavior post-SCI might be detectable even before there is clinical evidence.

Figure 4.6 The estimated mean of the RP of seven reconstructed EMG sub-bands prior to and post to the creation of the SCI for the right side of the tail. Of note, the RP values for the lower frequency sub-bands (D4, D6, and A6) are significantly higher in the post-lesion period, while the higher frequency sub-bands (D1 and D2) are significantly lower in the post-lesion period. The star indicates a significant difference [163]

3) What is the difference in the EMG activity between the control and the treatment group

in the post-lesion period (treatment effect)?

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The post-lesion data were utilized to generate two separate mixed models, one for

each side. The potential effect of the treatment was analyzed using the RP of the left and

the right sides, incorporating an analysis that compared the treatment and the control

groups. The interaction of the frequency sub-band and the treatment variables in the

models of both sides demonstrated a significant effect (p-value < 0.001). Given that the

treatment*frequency interaction was significant, Tukey’s mean comparisons were

generated to identify what means differed significantly. Figures (4.7) and (4.8) summarize

the estimated mean of the RP values for different sub-bands of the control and treatment

group for the two sides. On the left side, the D1, D2, D3, and D6 sub-bands have a

significant difference, while on the right side the effect was significant in all the sub-bands

except the D2. The results suggest that there is a significant difference in the discharge

properties of the MUs in the treatment and the control groups during the post-lesion

period.

99

Figure 4.7 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the left side of the treatment (Tr) and the control (Ctrl) groups; of note, the RP values for the frequency sub-bands (D1, D2, D3, and D6) are significantly different. Subjectively, the distribution of the RP in the treatment group is similar to the RP distribution

in the pre-lesion period for all the subjects. The star indicates a significant difference [163]

100

Figure 4.8 The estimated mean of the RP of seven reconstructed EMG sub-bands post-lesion for the right side of the treatment (Tr) and the control (Ctrl) group; of note, the RP values for frequency sub-bands (D1, D3, D4, D5, D6, and A6) are significantly different. Subjectively, the distribution of the RP in the treatment group is similar to the RP

distribution in the pre-lesion period for all the subjects. The star indicates a significant difference [163]

The combination of TRH, selenium and vitamin E is associated with a treatment effect.

One of the goals of the current experiments was to gain a preliminary understanding of

the safety and efficacy of the combination of these three agents, which had not been

previously evaluated in animal models or in human studies. In a clinical trial involving

human patients who experienced SCI, TRH was administered as a 0.2-mg/kg bolus,

followed by an infusion of 0.2 mg/kg/h for 6 h [207]. For the current study, a similar weight-

based dosing paradigm was used: macaques in the treatment group received a bolus of

0.2 mg/kg with continuous infusion of 0.2 mg/kg/h for 1 h while anesthetized. Specifically,

three subjects received a combination treatment and two subjects did not receive

101

treatment. In the post-lesion period, there was a significant difference in the RP values

for most of the frequency sub-bands of the treatment and the control groups. These

results suggested there is a clear difference in the discharge properties of the MUs of the

treatment group during the post-lesion period. This finding was present for both the left

and the right sides. However, in the context of a limited number of subjects, this should

be considered as a preliminary inference. The rationale for this combination of treatment

for SCI and the implications thereof are published elsewhere [168].

4.4. SCI Classification System

EMG signals are one of the powerful biomarkers that have been used to detect and

track various neuromuscular abnormalities, such as myopathy and neuropathy disorders.

EMG signal interpretation is typically based on the visual inspection of expert clinicians.

Consequently, the diagnostic process might vary because it depends on the experience

of the clinician; therefore, developing a reliable EMG classification system may offer an

efficient assessment tool. Nonetheless, an analysis of EMG classifications may present

a robust and accurate detection method of the presence of SCI. As well, such an analysis

may expand the understanding of an SCI because a computerized analysis offers a

quantitative measure instead of a subjective visual inspection. In this study, EMG features

have been extracted using different methods, including the conventional amplitude

metrics, the Q-metric, and the RP of the WT sub-bands. All these features have been

utilized to build different classification systems using two artificial intelligence approaches:

classical machine learning techniques, which include KNN and SVM, as well as a new

application of deep learning (CNN), which has been submitted in this study.

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4.4.1 Classification performance evaluation

The Performance of the employed classification techniques has been analyzed using

the following approaches:

1. Confusion matrix

The performance of a classification system can be described in a matrix table known

as a confusion matrix. The typical properties and the ratios that are included in the

confusion matrix for a binary classifier is illustrated in Figure (4.9). Each row of the matrix

represents the instances number in an actual class while each column represents the

instances number in a predicted class. In this study, the positive and negative classes

refer to the post- and pre-lesion data respectively. The confusion matrix consists of four

basic measures, which are as follow:

a. True Positives (TP): the number of correctly classified post-lesion instances.

b. True Negatives (TN): the number of correctly classified pre-lesion instances.

c. False Positives (FP): the number of pre-lesion instances, which have been

incorrectly classified as a post-lesion class.

d. False Negatives (FN): the number of post-lesion instances, which have been

incorrectly classified as a pre-lesion class.

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Predicted Class

Positive Negative

Actu

al

Cla

ss

Po

sit

ive

True Positive

TP

False Negative

FN

Neg

ati

ve

False Positive

FP

True Negative

TN

Figure 4.9 The basic components of a confusion matrix [208], [209]

Figure (4.10) represents the confusion matrix for one of the classifiers that was

employed in this study. This classifier has been used as an example to explain the main

elements of the confusion matrix. The EMG data were labelled into two classes: pre- and

post-lesion. During the training step, the classifier learned how to distinguish between the

two classes (for a binary type) and build a model; later, it used this model to predict the

labels of the unseen instances during the testing step. In Figure (4.10), 15503 out of

24,600 total post-lesion samples were correctly classified as belonging to the post-lesion

class. In comparison, 15854 out of 24,250 total pre-lesion instances were accurately

classified as belonging to the pre-lesion class. As well, the FP = 8396 and the FN =

9097samples, which represent the pre- and post-lesion instances that were misclassified

respectively. The values of FP and FN are important error metrics that need to be reduced

depending on the application itself and cannot be generalized for all applications; for

example, minimizing FN is important when there is a serious life-threatening disease that

can be treated effectively during the early stages. But then again, in the case of high costs

or a high risk of follow-up therapy, minimizing FP is important when the disease is not life-

104

threatening for the patient. Therefore, to assess the performance of the classifiers,

especially for medical applications, the metrics that consider the effect of these two errors

(FP and FN) need to be considered.

Predicted Classes

Post-lesion Pre-lesion

Ac

tual

Cla

sse

s

Post-

lesion TP= 15503 FN= 9097 24600

Pre-

lesion FP= 8396 TN= 15854 24250

Figure 4.10 An example of the confusion matrix results

2. Statistical performance metrics

To evaluate the performance of the applied KNN and CNN classification approaches,

five statistical metrics, including sensitivity (recall), specificity, precision, accuracy, and F-

measure, were used [142]. These metrics were calculated using the four basic measures

that are included in the confusion matrix of a classifier, including TP, TN, FP, and FN. The

five metrics were defined and calculated as follows:

Sensitivity (Recall): This measures the ratio of the actual positives that are correctly

identified.

𝑇𝑃

𝑇𝑃 + 𝐹𝑁𝑥 100%

Specificity: This measures the ratio of the actual negatives that are correctly identified.

105

𝑇𝑁

𝑇𝑁 + 𝐹𝑃𝑥 100%

Precision: This measures the ratio of the predicted instances that were correctly identified

as positive to the total predicted instances as positive.

𝑇𝑃

𝑇𝑃 + 𝐹𝑃𝑥 100%

Accuracy: This measure how well a classifier has classified the classes correctly.

𝑇𝑃 + 𝑇𝑁

𝑇𝑃 + 𝐹𝑁 + 𝑇𝑁 + 𝐹𝑃𝑥 100%

F-measure: This is the harmonic mean of sensitivity and precision.

2𝑇𝑃

2𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃𝑥 100%

4.4.2. SCI classification system using classical ML classification technique

In this work, two different groups of EMG features have been utilized with the classical

ML classification techniques. The results of these different features have been illustrated

as follows:

1. SCI classification using EMG amplitude features

The four amplitude EMG features (area, RMS, turn number, and ZC number)

mentioned in Section (3.3.2.1) were combined to form one feature set. This set was used

as an input for the first SCI classification system that was developed. The most common

machine learning classification techniques in the EMG area were employed, including

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KNN and SVM. Figures (4.11), (4.12), (4.14), and (4.15) demonstrate the confusion

matrices of the classification techniques that were employed to the EMG data regarding

the left and the right EMG sides. The x-axis and the y-axis indicate the class of data (pre-

or post-lesion), and the diagonal cells of these figures show the rate of correctly classified

samples for both classes.

Using the SVM classifier, the post-lesion samples of the left side data were classified

correctly at a higher percentage (81%) compared to the pre-lesion sample (29%), as seen

in Figure (4.11). In comparison, the KNN confusion matrix demonstrated a balanced

prediction performance in terms of the number of correctly classified instances for both

classes (63% and 65%), as seen in Figure (4.12).

Figure 4.11 Confusion matrix of the SVM classifier using EMG amplitude features / left side

107

Figure 4.12 Confusion matrix of the KNN classifier using EMG amplitude features / left side

The performance of the classifiers was also evaluated using the five statistical

metrics. sensitivity, specificity, accuracy, precision, and F-measure values of the left side

are outlined in Figure (4.13). This figure shows that KNN scored the highest values of

specificity, accuracy, and precision metrics across the two classification methods. The

KNN classified the data with an accuracy of 64.1%, and the F-measure, 63.9%; in

contrast, the SVM classified the data with an accuracy of 54.9% and the F-measure,

64.2%. The SVM method with the worst performance was found to be biased when it

classified the pre-lesion samples (negative class) with a low specificity of 28.9%, while

the post-lesion samples (positive class) were classified with a reasonable degree of

sensitivity at 80.5%. According to the findings, the KNN is a more appropriate

classification method for the left side data using the EMG amplitude features.

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Figure 4.13 Evaluation metrics of the KNN and the SVM classifier using EMG amplitude features/ left side

Using the SVM classifier with the right side data, the pre-lesion samples were

correctly classified at a higher percentage (87%) compared to the post-lesion sample

(56%), as seen in Figure (4.14). The KNN technique also demonstrated an imbalanced

prediction performance with a rate of 68% for the post-lesion class and 87% for the pre-

lesion class, as seen in Figure (4.15).

Figure (4.16) represents the sensitivity, specificity, accuracy, precision, and F-

measure values of the SVM and KNN classification using the right side data. The KNN

scored the highest values of all the metrics (except for specificity) across the two

classification methods. The KNN classified the data with an accuracy of 77.4%, and the

F-measure, 75.1%; in contrast, the SVM classified the data with an accuracy of 71.9%

and the F-measure, 66.7%. The SVM classifier also showed some biased behavior with

the right side data when it classified the pre-lesion samples (negative class) with a

specificity of 87.3%, while the post-lesion samples (positive class) were classified with a

80.6

28.9

54.9 53.5

64.363.0 65.4 64.2 64.9 63.9

0

10

20

30

40

50

60

70

80

90

100

Sensitivity Specificity Accuracy Precision F-measure

Pe

rce

nta

ge %

Evaluation Metric

SVM KNN

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sensitivity of 56.4%. According to the performance metrics, the KNN is the more

appropriate classification method for the right side data using the EMG amplitude

features.

Figure 4.14 Confusion matrix of the SVM classifier using EMG amplitude features / right side

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Figure 4.15 Confusion matrix of the KNN classifier using EMG amplitude features / right side

Figure 4.16 Evaluation metrics of KNN and SVM classifier using EMG amplitude features/ right side

56.4

87.3

71.9

81.6

66.768.1

86.8

77.583.8

75.1

0

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40

50

60

70

80

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100


Pe

rce

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Evaluation Metric

SVM KNN

111

2. SCI classification using Q-metric and RP features

In sections (3.3.2.2.) and (3.3.3.2.), the EMG signal was characterized using new

amplitude and frequency-time features. First, the number of peaks was obtained from

each subject as a feature to develop the newly proposed general volitional muscle metric

(Q-metric). In the second feature, the frequency content of the signal was analyzed using

the wavelet transforms, then the RP of the EMG frequency sub-bands (RPA6, RPD6,

RPD5, RPD4, RPD3, RPD2, and RPD1) were calculated. The Q-metric and the RP features

that were obtained were then combined into one dataset. The dataset was then used as

input for the SVM and the KNN classifiers. Figures (4.17), (4.18), (4.19), and (4.20)

represent the confusion matrices of the applied classifiers using the left and the right side

data respectively. According to the figures, the two classification techniques

demonstrated a high and balanced predicting performance for both classes (the dark blue

diagonal cells) with a low error rate (light blue blocks).

112

Figure 4.17 Confusion matrix of the KNN classifier using the Q-metric and the RP features of the left side EMG data

Figure 4.18 Confusion matrix of the SVM classifier using the Q-metric and the RP features of the left side EMG data

113

Figure 4.19 Confusion matrix of the KNN classifier using the Q-metric and the RP features of the right side EMG data

Figure 4.20 Confusion matrix of the SVM classifier using the Q-metric and the RP features of the right side EMG data

114

According to the results, the two methods achieved a close performance using the

left side data, as seen in Figure (4.21). The accuracy was 80.9% and 80.3% for the SVM

and the KNN method respectively. Despite the fact that the two methods had similar

values for the accuracy, the KNN outperformed the SVM in terms of the F-measure, the

precision, and the sensitivity.

Figure (4.22) shows a comparison of the classification performance metrics

between the SVM and the KNN for right side data. It can be observed that the KNN

method outperformed the SVM method by having an accuracy of 91.7% while the

classification accuracy of the SVM was only 81.6%.

Figure 4.21 Evaluation metrics of the KNN and the SVM classifier using the RPs and the peak number features/ left side

80.4 81.5 80.9 81.4 80.975.5

87.180.3

89.381.8

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70

80

90

100

Sensitivity Specificity Accuracy Precision F-Measure

Pe

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nta

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Evaluation Metric

SVM KNN

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Figure 4.22 Evaluation metrics of the KNN and the SVM classifier using the RPs and the peak number features/ right side

4.4.3. SCI classification using DL technique compared to ML technique

The results of the classification comparison analysis have been presented as follows:

1. CNN hyperparameters

To design a neural network, there is a group of variables that need to be set before

optimizing the weights of the network. These variables are known as network

hyperparameters, and they include variables such as the number of layers, the number

of nodes, the learning rate, the batch size, and the number of epochs. In this study, a

manual search method was applied to find a combination of the hyperparameters that

gives the best classification performance. Various combinations of the hyperparameters

were evaluated, and the best combination was selected to perform the classification task.

The hyperparameters were optimized using training and test sets and final results have

been reported using 5-fold cross validation. The same procedure was applied to the EMG

85.678.4 81.6

76.080.5

95.188.8 91.7

88.091.4

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80

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100

Sensitivity Specificity Accuracy Precision F-Measure

Pe

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Evaluation Metric

SVM KNN

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data of the right and the left side. Table (4.11) summarizes the hyperparameters that were

selected.

Table 4.6 The utilized hyperparameters

Optimizer Learning Rate Batch Size Epoch Loss Function

Adam 0.001 128 100 BC

The network was trained using the extracted EMG segments along with their

corresponding class labels (pre-lesion = Class 0, post-lesion = Class 1). The loss function

of binary cross-entropy (BC) was used as shown in Equation (4.1):

BC = −1

𝑛∑ 𝑥𝑖 . 𝑙𝑜𝑔(𝑦𝑖) + (1 − 𝑥𝑖). log (1 − 𝑦𝑖) 𝑛

𝑖=1

where yi is the predicted probability, xi is the actual probability (0 or 1), and n is the total

number of instances. All the experiments were conducted using a free cloud service

based on Jupyter Notebooks known as Google Colaboratory (Colab), which was

approved as an effective tool for deep learning in [210]. The CNN was implemented in

Keras (a Python front-end for deep learning) [211], and the Python Tensorflow library

[212] was used for the computational implementations.

2. CNN overfitting

Overfitting is a common problem in machine learning that happens when the model is

too complex and fits the training data very well (memorizing the data). To test the

performance of the proposed CNN classifier, the loss and the accuracy curves were

visualized for both sides when it was trained using 80% of the EMG segments and tested

117

using 20% of the segments. Figures (4.23), (4.24), (4.25), and (4.26) show the accuracy

and the loss curves of the CNN of the left and the right side respectively. In these figures,

both the training and the testing loss decreased continuously, and they were close

throughout the training process, which indicated that the proposed CNN did not face an

overfitting problem.

Figure 4.23 The accuracy curve for the EMG data classification of the left side using the proposed CNN architecture

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Figure 4.24 The loss curve for the EMG data classification of the left side using the proposed CNN architecture

Figure 4.25 The accuracy curve for the EMG data classification of the right side using the proposed CNN architecture

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Figure 4.26 The loss curve for the EMG data classification of the right side using the proposed CNN architecture

3. Performance metrics of KNN and CNN classification

Figures (4.27) and (4.28) show the five statistical metrics that were calculated for

the KNN and the CNN classifications of the EMG data for the muscles of the left and the

right side respectively. According to Figures (4.27) and (4.28), the KNN classified the

EMG data with an accuracy of 90.1% for the left side and 92.6% for the right side. As

well, the simple proposed architecture of the CNN performed reliably as it achieved 83.5%

and 87.1% for the left and the right side respectively. The CNN model had the most

competitive result for all of the evaluation metrics for both sides. This can be explained

by the advantage that the CNN has in its ability to use the temporal correlation existing

inside the EMG segments. As well, these results might indicate that the CNN has learned

the required information from the EMG signal that reflects the effect of the created lesions

on the muscle activity. These unique EMG features were learned during the successive

convolutional layers.

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Figure 4.27 Evaluation metrics of the CNN and the KNN classifier / left side

Figure 4.28 Evaluation metrics of the CNN and the KNN classifier / right side

93.4

87.3

90.1

86.3

89.7

81.6

85.783.5

86.5

84.0

70.0

75.0

80.0

85.0

90.0

95.0

100.0


Pe

rce

nta

ge %

Evaluation Metric

KNN CNN

92.293.2 92.6 93.2 92.7

88.7

85.787.1

85.186.9

70.0

75.0

80.0

85.0

90.0

95.0

100.0


Pe

rce

nta

ge %

Evaluation Metric

KNN CNN

121

The main CNN power is its automated feature learning ability, which may play an

important role in the EMG signal field according to its complexity and nonstationary

nature; therefore, such an automated feature learning system might help in encoding the

hidden message of the motor control system to the skeletal muscles. As well, it may help

in characterizing the neuromuscular abnormalities (perturbations in the electrical activity

of the muscle) that occur as a result of neuromuscular diseases such as SCI.

Electromyography is a reliable and cost-efficient muscle activity assessment tool.

However, due to the complex process of generating this signal, analyzing and

understanding such a complicated signal is a challenging task. In this project particularly,

the EMG signal has more complexity because it was collected while the subjects were

performing their daily activities without any restrictions (freestyle movement). Even

though the CNN classification did not outperform the classical KNN method, it can still be

considered a reasonable method of preliminary classification. In future work, the SCI

classification system could be applied using a CNN system with a more advanced

structure.

4.5. Summary

Chapter 4 has presented a comprehensive intramuscular EMG analysis of the

NHP model of SCI. In order to examine the reliability of using dynamic EMG signals to

describe the SCI effects, this analysis considered the main characterization aspects of

EMG signals in terms of signal amplitude features, frequency content (pattern of

recruitment), and SCI classification ability using various techniques. Of note was the

challenge of using conventional EMG features to describe the SCI effects. As well, this

122

study submitted a preliminary volitional muscle activity metric (Q-metric) utilizing the peak

number of the EMG signal over a period of time. This metric could be advanced in the

future to be used as an SCI assessment tool. Moreover, this work proposed an EMG

decomposition method that included wavelet analysis and relative power calculations.

This method was able to detect the expected perturbations in the frequency components

of the EMG signals post-SCI. As well, it provided a preliminary inference that treatment

with a combination of TRH, selenium and vitamin E may improve SCI recovery. Together,

these results confirmed the reliability of using EMG signals as a measure of volitional

muscle activity for an NHP model.

An SCI diagnosis is currently based on a subjective visual assessment of different

biomarkers by specialists. The development of an SCI classification system would be an

essential means of advancing the diagnostic process because such a system would offer

an objective assessment tool using EMG signals. In chapter 3, different SCI classification

systems were developed using four selected EMG amplitude features, RP-WT features,

and Q-metric values, along with two different classification techniques (KNN and SVM).

A comparison study was performed to investigate the reliability of using a deep

learning (CNN) classifier with a raw EMG signal as an SCI classification system. The

results of the comparison indicate that a CNN with a simple architecture can provide a

level of performance close to the classical ML classification technique (KNN) for the

purpose of neuromuscular disease (SCI) classification. CNN has an advantage as it is

easier to implement in terms of the input data since it uses raw data. As well, throughout

123

the training process, it learns some representations (extracts some features

automatically) from the input data; therefore, it reduces the effort required for preparing

the engineered features.

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Chapter 5

Conclusions and Future Work

5.1. Conclusions

• This chapter summarizes the main outcomes of the current work. The overriding goal

of this research is to build a valid NHP model for SCI with a reliable assessment tool

to characterize the injury neurophysiologically using intramuscular EMG signals. This

contribution could facilitate the development of a model to test new treatments for SCI,

and thereby assist in improving the functionality and quality of life for human beings.

• The work presents the results of experiments involving Macaca fascicularis, in which

a model of spinal cord injury (SCI) was created by using a balloon catheter inserted

into the epidural space. Prior to the creation of the lesion, an EMG telemetry device

was inserted to facilitate the measurement of tail movement and muscle activity before

and after SCI. The telemetry device was attached to two pairs of wire electrodes,

which were tunneled into the tail. This model is unique in that the impairment was

limited to the tail: the subjects did not experience limb weakness, bladder impairment,

or bowel dysfunction. The focus of the SCI model was to create a lesion that mimics

human SCI without clinical impairments.

• The tail of Macaca fascicularis can be considered a fifth limb, which is involved in the

performance of functional tasks and balance. The tail has well-developed sensory and

motor areas.

125

• The intramuscular recordings from this model are unique. Specifically, the EMG data

were obtained prior to and subsequent to SCI. Additionally, the data were obtained

while the subjects were moving freely (dynamic movement), over an extended period

of time (longitudinal).

• Some potential limitations included the limited numbers of subjects; this is, in part

related to the cost. As well, the longitudinal nature of the collection precluded a very

high sampling rate.

• Different analysis techniques were used to develop reliable EMG markers for the

detection and the classification of SCI.

• In this study, three specific objectives were set, and they were achieved using the

methodology suggested in Chapter 3. The results are summarized in the following:

1. Regarding the development of a metric for general volitional muscle activity, two

methods were examined, which included the following:

• First, four main EMG amplitude features (area, RMS, turn, and ZC) were selected

to be used as a metric of muscle activity. Based on the statistical analysis of the

results, the selected features showed inconsistent behavior regarding the effect of

the lesion that had been created and the treatment that was utilized. This could

indicate that conventional EMG metrics are overwhelmed if they are used as

characterization metrics for EMG signals that are collected in a dynamic fashion

and with different levels of contractions. A more specialized metric was therefore

needed to overcome the additional complexity of this type of EMG signal.

126

• The alternative EMG analysis paradigm that was proposed focused on the number

of peaks in the continuous signal of the EMG and it was called the Q-metric. Given

the EMG data from these experiments, the Q-metric could be used to evaluate

impairment after SCI. Specifically, the Q-metric decreased immediately after the

experimental SCI was implemented, and then increased over time. Evidence was,

however, insufficient to ascribe the changes to a treatment-associated effect.

2. In terms of the characterization of the discharge properties of the muscle motor units

during pre- and post-SCI for the treatment and the control group, and for agonist-

antagonist pairs of muscles, the following methods were examined:

• First, the most common EMG decomposition technique found in the literature was

applied to test its efficacy with the unique nature of the EMG signal in this study.

According to the acquired invalid results, this method was not applicable for such

a unique type of EMG data; therefore, another technique needed to be

investigated.

• Wavelet analysis and the relative power of EMG frequency sub-bands were

proposed as alternative decomposition methods. The RP of these sub-bands was

used as a response variable in various mixed models to analyze the effect of the

muscle side, the induced lesion, and the treatment that was used, on the collected

EMG data. In the absence of the SCI, the EMG data demonstrated an

asymmetrical activation of the two sides; this is consistent with the human

phenomenon of limb preference and/or dominance. Perturbation with an

experimental lesion resulted in a clear EMG consequence. Of note, the effect of

127

the lesion was different on the left and the right side. As well, there was a

preliminary inference that treatment with a combination of TRH, selenium and

vitamin E could improve recovery. The results indicated that the RP of the

decomposed EMG data added a new dimension to the evaluation of impairment

and recovery in this NHP model of experimental SCI. This analysis could lead to a

new assessment index for motor unit activity and progression of recovery from

SCI.

3. In terms of designing the SCI classification system:

• All the extracted EMG features in this work, including EMG amplitude features, Q-

metric, and the RP of the WT sub-bands, were utilized to develop the SCI

classification system using machine learning techniques that included SVM and

KNN.

• Using the EMG amplitude features, KNN and SVM techniques generally classified

the data with a low performance. The KNN outperformed the SVM with an F-

measure of 63.9.0% and 75.1% for the left and the right side respectively.

• In contrast, these two classification techniques showed a higher performance

using the Q-metric combined with the RP of the WT sub-bands features. Using

these features, the KNN technique outperformed the SVM method with an F-

measure of 81.8% and 91.4% for the left and the right side respectively.

• This study also presented a new application of deep learning (CNN) as an SCI

classification system using raw EMG segments as inputs and compared the results

to the classical machine learning classification (KNN).

128

• The performance of the two classifiers was measured and compared using five

performance metrics. The KNN technique outperformed the CNN methods with an

F-measure of 89.7% and 92.7% for the left and the right side respectively. The

new application of combining CNN and a raw EMG signal for neuromuscular

disease classification achieved a competitive degree of classification accuracy

when compared to the classical machine learning techniques. CNN classified the

data with an F-measure of 84.0% and 86.9%. for the left and the right side, while

the KNN, using hand-crafted EMG features, achieved an F-measure of 89.7% and

92.7% for the left and the right side respectively. As the CNN technique is still a

new application, more investigations need to be performed in order to build a clear

understanding of the ability and the reliability of using this deep learning technique

for SCI detection and characterization using the automated learned features.

• In conclusion, the EMG and the histopathologic data from this study have

advanced the understanding of SCI. The additional research required may

translate into novel treatments for people and animals with spinal cord injuries.

5.2. Future Work

• Several potential alternative approaches to analyzing EMG data were found. The

EMG analysis paradigms that were used in the current study focused on hand-crafted

EMG features. An alternative approach is to employ automated learned feature

methods, such as CNN, AE, and RNN, and check their efficacy in translating the

hidden changes in the EMG signal as a result of an SCI. In this study, a preliminary

application of CNN showed reasonable results. The automated learned features

129

approach could also help to make other conservative EMG analysis methods more

applicable for dynamic EMG data; e.g. it could help to overcome the problem of

applying the conventional decomposition method (MUPT method).

130

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List of Publications Some of the work described in this thesis previously appeared in:

1- Masood, F., Abdullah, H.A., Seth, N., Simmons, H., Brunner, K., Sejdic, E., & Schalk, D.R., et al. "Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes." Sensors 19, no. 15 (2019): 3303.

2- Seth, N., Simmons, H.A., Masood, F., Graham, W.A., Rosene, D.L., Westmoreland, S.V., & Cummings, S.M. et al. "Model of Traumatic Spinal Cord Injury for Evaluating Pharmacologic Treatments in Cynomolgus Macaques (Macaca fasicularis)." Comparative Medicine 68, no. 1 (2018): 63-73.

3- Masood, F., Farzana, M., Nesathurai, S., & Abdullah, H., "Comparison Study of Classification Methods of Intramuscular EMG Data for Non-Human Primate Model of Traumatic Spinal Cord Injury." Journal of Engineering in Medicine (under review)

4- Masood, F., Abdullah, H. A., Sharma, M., Mand, D., Simmons, H., Brunner, K., Schalk, D., Sledge, J., & Nesathurai, S., “A Novel Application of Deep Learning (Convolutional Neural Network) for Spinal Cord Injury Classification Using Raw EMG Signal.” (under review)

5- Seth, N., Masood, F., Sledge, J.B., Graham, W.A., Rosene, D.L., Westmoreland, S., & Abdullah, H.A. “Humane Non-human Primate Model of Traumatic Spinal Cord Injury: Quantitative Analysis of Electromyographic Data.” Open Journal of Veterinary Medicine, 5(07), (2015): p.161.

6- Gwardjan, B., Nitin, S., Masood, F., Sledge, J., Rosene, D., Westmoreland, S., & Abdullah, H. “Treating Acute Traumatic Spinal Cord Injury with Combination Therapy of Thyrotropin Releasing Hormone, Selenium and Vitamin E.” Journal of Neuropathology and Experimental Neurology Vol. 75, No. (6), (2016, June): pp. 605-606.

7- Fayez, O., Johnson, K., Simmons, H., Brunner, K., Schalk, D., Mensinkai, A., & Sedjic, E. “Pathological and Radiological Features of Traumatic Spinal Cord Injury”. In Journal of Neuropathology and Experimental Neurology 78(6), (2019, June): pp. 579-579.

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