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CTG OASOpen access software for Fetal Heart Rate Analysis

www.ctganalysis.com

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CTG-OAS Open access software for Fetal Heart Rate Signal Analysis

Warning

CTG-OAS software is developed for research purpose. It cannot be used commercially. It can be used as a decision support tool.

You can follow developments in CTG-OAS via www.ctganalysis.com

Preface

Cardiotocography (CTG) is a well-known widely used fetal monitoring technique. CTG consists of two biophysical signals which are Fetal Heart Rate (FHR) and Uterine Contraction (UC). Today, CTG has been an integral part of the perinatal health care. However, it has several drawbacks.

Although this monitoring technique has been introduced at the end of the 1960s, there is still no a gold standard for evaluation of FHR recordings. Several guidelines such as FIGO have been published in order to ensure more consistent interpretation. However, the literature points that there is a great variability in interpretation of CTG recordings between inter-observers and even intra-observers. A visual examination is performed by observers in clinical practice, and this subjective evaluation leads to increase in disagreement level among observers.

Many scientists have focused on this live topic. So, the automated CTG analysis has been adopted as the most promising way to tackle problems which CTG has. In this scope, we have developed an open software for FHR signal analysis called CTG-OAS. This software provides all the necessary procedures for automated FHR analysis as a whole and the basic aim of this software is to serve as an useful tool for FHR signal analysis. We believe that CTG-OAS can be a valuable tool for FHR signal analysis due to various useful features provided by the software.

This document is provided to illustrate with necessary examples, the working of this software .

Please visit www.ctganalysis.com to download this software and also to follow the further developments.

Asst. Prof. Zafer CÖMERT On behalf of CTG Research Group

How to Contact CTG Research Group

Web www.ctganalysis.com

Please follow ctganalysis.com for downloading the software and latest news.

Phone +90-506-781-4052

Address Bitlis Eren University

Department of Computer Engineering Bitlis/Turkey

Trademarks

This software has been developed using MATLAB® graphical user interface development environment (GUIDE) for academic purposes and it can not be used commercially. The

software may be used freely for research purpose. Please do not forget to cite the related papers if you prefer to use CTG-OAS.

ˠ Cömert Z and Kocamaz AF (2017) Novel Software for Comprehensive Analysis of

Cardiotocography Signals CTG-OAS, International Conference on Artificial Intelligence

and Data Processing (IDAP17), Malatya: IEEE, pp. 1–6.

Cömert Z and Kocamaz AF (2018) Open Access Software for Analysis of Fetal Heart Rate

Signals, Biomedical Signal Processing and Control, (Status: Major Revision).

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Revision History

Date Printing style Description

2018 April Online only The first prototype of the software is introduced.

Content

Preface ................................................................................................................................................. III

How to Contact CTG Research Group ......................................................................................... IV

Revision History................................................................................................................................... V

Content ................................................................................................................................................ VI

1. Installation of CTG-OAS ............................................................................................................. 1

2. Overview of CTG-OAS ................................................................................................................7

2.1. Main menus ...........................................................................................................................7

2.2. Toolbar menu ....................................................................................................................... 8

2.3. FHR Signal Analysis using CTG-OAS ............................................................................... 9

2.3.1. Load an FHR signal ...................................................................................................... 9

2.3.2. Preprocessing.............................................................................................................. 11

2.3.3. Feature Extraction ..................................................................................................... 13

2.3.4. Feature Export ............................................................................................................ 16

2.3.5. Dataset Analysis .......................................................................................................... 17

a) Create, Load and Save Dataset .................................................................................... 18

b) Outliers detection for selected features ................................................................... 19

c) Probability plot for normal distribution of selected feature(s) ........................... 20

d) Plot histogram for the selected feature(s) ................................................................ 21

e) Pairwise correlation matrix for the selected feature(s) ........................................ 22

f) The distribution of recording using Principal Component Analysis ................. 23

g) Boxplot of selected features considering the distribution of the values of feature(s) for each class ....................................................................................................... 24

2.3.6. Classifiers .................................................................................................................... 25

a) Support Vector Machine ............................................................................................. 25

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b) Artificial Neural Network ............................................................................................. 29

c) k-nearest Neighbor ....................................................................................................... 30

Acknowledgment ............................................................................................................................... 32

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1. Installation of CTG-OAS

CTG-OAS software has been developed using MATLAB® graphical user interface development environment (GUIDE). After the completion of all components of the software, we have used MATLAB CompilerTM which gives an opportunity to developers in order to share their MATLAB® programs as standalone applications.

Figure 1.1 shows a screenshot of the MATLAB CompilerTM. All applications created with MATLAB Compiler use the MATLAB Runtime, which enables royalty-free deployment to users who do not need MATLAB. We have packaged the runtime without the application. For this reason, the runtime of installation will be downloaded from the internet during the installation.

Figure 1.1 The screenshot of MATLAB Compiler

In order to install CTG-OAS, the installation file must be downloaded from www.ctganalysis.com.

Then, the procedure given below should be followed:

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1. The downloaded zip file must be extracted into a folder on your personal computer.

2. The installation file must be run.

Figure 1.2 shows a downloaded installation file. As mentioned before, the file must be run in order to install the software.

Figure 1.2 The downloaded installation file

When the file is run, a preparing window may be shown, and this window may remain on the screen for a few minutes, as shown in Figure 1.3.

Figure 1.3 Preparing for installation

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Then, CTG-OAS Installer window is opened. Further, the installation steps should be followed.

Figure 1.4 CTG-OAS installation window

In the next step, a folder must be selected as installation folder. In this screen, you can click add a shortcut to the desktop as shown in Figure 1.5.

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Figure 1.5 Choose installation folder

MATLAB runtime is installed as shown in Figure 1.6 in the next step.

Figure 1.6 MATLAB Runtime installation.

Finally, a confirmation screen is shown to users as shown in Figure 1.7.

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Figure 1.7 Confirmation

Figure 1.8 Installation completed

If everything goes well, the installation will be complete and it can be finished as shown in Figure 1.8.

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Figure 1.9 Accessing the software

The software can be run from Windows Start Menu by writing its name as shown in Figure 1.9.

2. Overview of CTG-OAS

After the installation process is completed, CTG-OAS is ready for use. As seen in Figure 2.1, the software provides several tools for analysis of FHR signals. The tools are presented through six main menus which are File, Preprocessing, Feature Extraction, Analysis, Plot, and Settings.

Figure 2.1 The main screen of CTG-OAS

2.1. Main menus

The basic file operations such as open, save, import and export are executed under the File menu.

The preprocessing processes, as well as detection of missing beats, are performed under Preprocessing menu.

Morphological, time-domain, nonlinear, and image-based time-frequency (IBTF) analysis processes are offered under Feature Extraction menu. Also, the extracted features can be exported to a feature set in order to feed classifiers. Furthermore, each of the features that make up the full feature set can be examined individually by using tools provided by the software. It is also emphasized that this part of the software also provides dataset analysis tools. In addition, passing to the classification process is performed through this menu.

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The detailed analysis can be performed using Analysis menu. This menu creates new windows according to the selection of analysis domain.

Plot menu is used in order to plot fetal heart rate signal, morphological events, or spectrograms. The basic settings can be configured via the Setting menu.

2.2. Toolbar menu

Toolbar menu consists of icons representing the operation done by button. The task of each button is given in Table 2.1.

Table 2.1 The tasks of toolbar buttons

Icon Short Description

Save record

Open record

Close program

Zoom-in

Zoom-out

Hand

Rotate

Data cursor

Legend

Detection of missing beats

Preprocessing

Get statistic of FHR signal

Detection of acceleration patterns

Detection of deceleration patterns

Detection of acceleration and deceleration patterns

Baseline analysis

Linear and nonlinear analysis

Image-based time-frequency analysis

Plot FHR signal

Plot morphological events

Plot spectrogram

! The toolbar buttons can be different depending on software version.

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It is important to be aware that the screens presented in the document may differ depending on the development of the

software.

2.3. FHR Signal Analysis using CTG-OAS

In this section, we demonstrate how an FHR signal is analyzed using CTG-OAS. Please follow the described operations to perfomr the analysis.

2.3.1. Load an FHR signal

When the software is started, an empty screen appears as shown in Figure 2.2. Firstly, an FHR file must be loaded to start the analysis. For this, the File menu can be used. If you prefer, you can also use either CTRL + O shortcut for this command or use toolbox button.

Figure 2.2 Starting of the software

In order to load an FHR signal

a) Select File > Load FHR Signal b) Use shortcut -> CTRL + O c) Toolbox ->

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When Load FHR Signal command is triggered, the software prompts for a record to be selected from your computer as shown in Figure 2.3.

Figure 2.3 Selecting an FHR record from the personal computer

Please note that only files with extensions .mat can be loaded.

After selection of the file, the FHR signal is plotted on the main screen as shown in Figure 2.4. As it can be inferred from the figure, the signal is plotted on a modeled CTG trace. The figure consists of small squares and big rectangles. The small squares correspond to 30 s in the horizontal axes and 10 bpm on vertical axes whereas big rectangles correspond 3 minutes on horizontal axes and 30 bpm on vertical axes. The software assumes that the FHR signals are sampled at 4 Hz.

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Figure 2.4 Loading a raw FHR signal

2.3.2. Preprocessing

After the signal is loaded, it must be preprocessed to forward it the next numerical analysis stage.

Figure 2.5 Missing beats

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As in Figure 2.5 for the sample FHR signal, intrapartum FHR signals frequently include a huge amount of missing beats. For this reason, a robust preprocessing process should be carried out. As seen in Figure 2.5, a grid including the summary of missing beats is presented to users. The user can follow start and stop indices and the length of each missing beats in this grid.

In order to trigger the missing beats command

a) Select Preprocessing > Missing beats

b) Toolbar >

It is well-known that preprocessing is an essential step for almost all biomedical signal processing applications. This process affects not only the values of extracted features but also the classification performance. From this point of view, due to the nature of FHR, it includes several types of noise that occur due to the movements of both mother and fetus, displacements of the transducer, and other factors such as labor-related stress, etc. As a result, FHR signal can be contaminated by these type of factors.

Segment selection, outliers detection, and interpolation constitute the main preprocessing procedures for the software. A basic artifact rejection scheme is employed, so extreme values (≥ 200 bpm and ≤ 50 bpm) are interpolated. The cubic Hermite spline interpolation technique provided by MATLAB® is utilized to fill the missing beats because of producing robust and accurate values for spectral analysis. Furthermore, the long gaps (>15 s) are not included in the subsequent feature extraction process. In the last step of the preprocessing, FHR signals are detrended by using second order polynomial due to the use of nonlinear signal processing techniques.

The signal can be preprocessed by

a) Select Preprocessing > Preprocessing b) Use a shortcut > CTRL + P c) Toolbar button >

When the preprocessing command is triggered, the segment selection, outliers detection, and detrend procedures are carried out, and in this manner, the preprocessed signal is obtained as shown in Figure 2.6.

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Figure 2.6 The preprocessed signal

2.3.3. Feature Extraction

CTG-OAS can ensure several diagnostic features which are obtained from different domains such as morphological, time, nonlinear and image-based time-frequency domains.

In the prototype version of the software, a total of eighteen (18) features are provided for signal representation. Also, these features represented in Table 2.2 are located on the main screen of the software.

The baseline, the number of both acceleration and deceleration patterns are extracted from the morphological domain in agreement with FIGO guidelines.

The morphological features are extended with linear or time-domain features which are Mean (𝜇), Standard deviation (𝜎), Long-term irregularity, (LTI), Delta (∆), Short-term variability (STV), Interval index (II), Mean absolute deviation (MeanAD), and Median absolute deviation (MedianAD).

Recently, the nonlinear time-series analysis has gained an increased popularity in FHR signal analysis. Especially, as new diagnostic indices, Approximation Entropy (ApEn), Sample Entropy (SampEn) and Lempel Ziv Complexity (LZC) from the nonlinear domain have been rather found remarkable. So, these features are utilized in the software, and the tolerance and embedding dimension parameters of both ApEn and SampEn are set to 0.20 and 2, respectively.

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Table 2.2 The features provided by the first prototype of CTG-OAS

Features Parameters settings

Morphological

features

Baseline,

Number of accelerations,

Number of decelerations.

After the virtual baseline is

estimated, the parameters

representing upper and lower

limit are adjusted to 8 so as to

remove accelerations and

decelerations [75].

Time domain

(linear)

features

Mean (𝜇),

Standard deviation (𝜎),

Long-term irregularity, (LTI),

Delta (∆),

Short-term variability (STV),

Interval index (II),

Mean absolute deviation (MeanAD),

Median absolute deviation (MedianAD).

Nonlinear

features

Approximation Entropy (ApEn),

Sample Entropy (SampEn),

Lempel Ziv Complexity (LZC).

Embedding dimension

(𝑚 = 2)

Tolerance

(𝑟 = 0.20)

Image-based

time-frequency

features

Contrast,

Correlation,

Energy,

Homogeneity.

Distance (𝛿 = 1)

Angle (𝜃 = 90°)

IBTF features, often also called as texture features have been used successfully in electroencephalography and electromyogram signals analysis. However, the applicability of texture features has not been explored for the FHR signals analysis. The basic information source of IBTF analysis is spectrogram images that are obtained using standard Short Time Fourier Transform (STFT). The spectrogram images are converted into gray level images which are used as input to Gray Level Co-Occurrence Matrix. GLCM can be used to acquire valuable information, which represents the image characteristic, by extracting the contrast, correlation, energy, and homogeneity features. Contrast recognizes the number of local intensity variations. Correlation reveals the gray level linear dependencies in the image. Energy explains the uniformity of the image. Homogeneity points the closeness of the distribution of elements in the co-occurrence matrix to the co-occurrence matrix diagonal.

Lastly, IBTF features are taken into account with distance (𝛿 = 1) and angle (𝜃 = 90°) parameters in CTG-OAS. In addition, CTG-OAS focuses only on the spectrograms of very low-frequency bands (VLF, 0 – 0.03 Hz). Thus, the software ensures only 4 IBTF features called contrast, correlation, energy and homogeneity.

As mentioned before, a total of eighteen features obtained from four different domains are provided by the first prototype version of CTG-OAS. These features can be extracted and analyzed individually or collectively by using Feature Extraction menu as shown in Figure 2.7.

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Figure 2.7 Feature extraction menu

In practice, we frequently prefer to use the CTRL+E shortcut. Thus, all features are extracted automatically. As a result, the morphological events such as baseline calculation, the number of acceleration and deceleration patterns and other diagnostic indices extracted from time, nonlinear and IBTF domains are calculated as shown in Figure 2.8.

Figure 2.8 Result of feature extraction

In Figure 2.8, the solid black line represents an estimated baseline, the orange and green lines stand for acceleration and deceleration patterns.

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2.3.4. Feature Export

When feature extraction is completed, all features can be used to represent FHR signals. For this particular purpose, users can export the features to a feature set, and this feature set can be used to feed classifiers.

To execute this procedure:

a) Select Feature Extraction > Export features

Figure 2.9 Export features

When feature extraction command is triggered, a new window titled as Feature Set Management as shown in Figure 2.9 appears. It is important that before exporting to features to a feature set, the class of the recording must be selected. On the other hand, the number of recordings for each class, the number of features, and the number of the total recordings are shown in this window.

Figure 2.10 Confirmation on feature export

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If everything goes well, the software gives the feedback for users as shown in Figure 2.10.

2.3.5. Dataset Analysis

The Feature Set Management window can also link the users to Dataset Analysis and Classifiers windows as shown in Figure 2.11.

Figure 2.11 Dataset analysis

Dataset analysis window can perform several tasks which are listed below:

a) Create, load or save a dataset b) Outliers detection for selected feature(s). c) Probability plot for normal distribution of selected feature(s). d) Plot histogram for the selected feature(s). e) Pairwise correlation matrix for the selected feature(s). f) The distribution of recording using Principal Component Analysis (PCA) for the

loaded feature set on 2D and 3D axes. g) Boxplot of selected features considering the distribution of the values of feature(s)

for each class. h) Select desired features to feed classifiers. i) Also, users can get information about the dataset.

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a) Create, Load and Save Dataset

In order to create, load or save a dataset, toolbar buttons can be used as shown in Figure 2.12.

Figure 2.12 Create, load or save a dataset

Dataset analysis windows can ensure getting the distribution of the recordings of selected features as shown in Figure 2.13.

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b) Outliers detection for selected features

Figure 2.13 Boxplot of selected features

The outliers in selected features can be detected easily using this command as indicated in Figure 2.14.

Figure 2.14 Detection of outliers in selected feature(s)

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c) Probability plot for normal distribution of selected feature(s)

Similarly, the outliers in selected features can be detected easily using probability plot for normal distribution of selected feature and features as indicated in Figure 2.15 and Figure 2.16, respectively.

Figure 2.15 Detection of outliers in a selected feature

Figure 2.16 Detection of outliers in selected features

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d) Plot histogram for the selected feature(s)

Dataset analysis can ensure getting a histogram of selected feature(s). A sample command is performed for baseline, and the histogram is obtained as shown in Figure 2.17.

Figure 2.17 The histogram of baseline

Similarly, the histogram plot is obtained for acceleration and deceleration pattern as shown in Figure 2.18.

Figure 2.18 The histogram of acceleration and deceleration patterns

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e) Pairwise correlation matrix for the selected feature(s)

Graphical representation of pairwise correlation and pairwise correlation matrix for the selected feature(s) can be reached easily using Dataset Analysis Window as shown in Figure 2.19 and Figure 2.20, respectively.

Figure 2.19 Graphical representation of pairwise correlation matrix for selected features

According to Figure 2.19, light-colored sections show a high correlation whereas dark-colored sections point a low correlation among the selected features.

Figure 2.20 Pairwise correlation matrix

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f) The distribution of recording using Principal Component Analysis

The distribution of the recording of extracted features can be projected on 2D or 3D axes using principal components as illustrated in Figure 2.21 and Figure 2.22.

Figure 2.21 The distribution of feature set using the first two principal components

Figure 2.22 The distribution of feature set using the first three principal components

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g) Boxplot of selected features considering the distribution of the values of feature(s) for each class

In Figure 2.23, the boxplot operation is triggered for baseline feature. 0 and 1 stand for hypoxic and normal classes respectively. As can be inferred from the figure, baseline feature represents a difference for both classes. A similar demonstration for contrast is given in Figure 2.24.

Figure 2.23 Boxplot for baseline

Figure 2.24 Boxplot for contrast feature

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2.3.6. Classifiers

Machine learning methods are rather significant in terms of automated CTG analysis. All features mentioned till date are used for feeding classifiers. As CTG Research Group, we carry out quite extensive experimental studies for the classification of FHR signals using machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (k-NN), radial basis function network (RBFN), extreme learning machine (ELM), decision trees (DT), etc.

As of now, ANN, SVM, and k-NN have been embedded into CTG-OAS for classification as indicated in Figure 2.25

Figure 2.25 The classifiers

a) Support Vector Machine

SVM was introduced in 1995 by Vapnik and admitted relatively as a new machine learning method.

As a machine learning tool, SVM is used in wide range of applications in numerous areas for regression, pattern recognition, and classification, because of its superior generalization ability.

SVM is frequently used for two-group classification problems by finding optimal hyperplane to divide multidimensional data into two classes.

Thus, two linearly separable data can be classified easily. However, the data collected from real life are nonlinear generally. SVM solves this challenge by using kernel-induced feature space which maps the data to a very high-dimension feature space. A linear decision border is searched in this feature space, and special parameters of the decision border assure high generalization ability.

CTG-OAS offers a GUI to the users for setting SVM parameters namely sigma for radial basis function and box-constraint. Also, the preferences for k-fold cross-validation and the number of repetition for experimental study can be changed by users as illustrated in Figure 2.26.

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Figure 2.26 SVM classifier

Correction in figure: box-constraint

The performance results of each testing are given with a table on this GUI. The table includes accuracy (Acc), sensitivity (Se), specificity (Sp), quality index (QI, sometimes called geometric mean), F-measure and AUC values of each testing. The last row of the table gives the average values of the performance metrics.

The users can examine each testing result by clicking the table given in GUI. When a row is selected, the related confusion matrix and ROC curve are shown automatically by the software as shown in Figure 2.27.

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Figure 2.27 Confusion matrix for each testing

Furthermore, it is possible to export all performance metrics by the right click (a context menu appears) on the table as indicated in Figure 2.28.

Figure 2.28 Exporting performance metrics

Likewise, the values of ROC curve can be extracted using context menu as mentioned in performance metrics. Please look at Figure 2.29.

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Figure 2.29 Exporting the values of ROC curve

CTG-OAS can give aggregate confusion matrix and average ROC curve using toolbar buttons. The aggregate confusion matrix and average ROC curve are shown in Figure 2.30 and Figure 2.31.

a) Click -> Aggregated confusion matrix b) Click -> Average ROC curve

Figure 2.30 Aggregated confusion matrix

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Figure 2.31 Average ROC curve

Numerous experimental studies can be performed via Dataset Analysis and Classification GUIs. The impact of combinations of different feature sets on classifiers can be tested quickly. The selected features are added or isolated to feature set easily. Also, the obtained results can be moved to different platforms. In this manner, CTG-OAS will be a valuable tool for numerical analysis of FHR signals.

b) Artificial Neural Network

ANNs are flexible computing structures inspired by the human nervous system and comprise of interconnected processing components called as neurons.

The interconnected neurons with various weights provide the transmission of information across the networks. The networks are trained by adjusting the weights of the interconnections using the input data.

Training algorithms follow various specific ways in order to calculate the weights. It should be remembered that the lacking or the extreme number of neurons in the hidden layer may lead to drawbacks with generalization and overfitting.

CTG-OAS ensures a graphical interface for configuration of ANN network. In this window, training algorithm, network topology, k-fold cross-validation, epoch, the number of repetition can be determined by users as shown in Figure 2.32.

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Figure 2.32 ANN classifier

As mentioned before for SVM classifier, after the all training and testing processes are completed, the user can check the confusion matrix and ROC curve of the network for each testing processes.

c) k-nearest Neighbor

𝑘-NN algorithm storing all available cases and classes is a simple and efficient machine learning tool and it is used for regression/classification tasks. 𝑘-NN algorithm performs predictions based on a similarity measure. Predictions for a new instance are performed by searching in the entire training set by considering 𝑘 neighbors. For this particular purpose, a distance measure Euclidean is used generally.

It should be noted that the best distance metric can be changed depending on the properties of data, the computational complexity of 𝑘-NN enlarges with the size of the training dataset, and 𝑘-NN produces more efficient results when the dimension of inputs is small. Furthermore, normalization of features, elimination of missing data problem and lower dimensionality will lead to better results for the 𝑘-NN. However, the issue with k-NN algoritm is that the value of k is selected by finding a best value of k based on the performance by running the it many times with different values of k.

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The distance, the number of k-neighbor, k-fold cross-validation, and the number of repetition can be set by users in kNN classification GUI as shown in Figure 2.33.

Figure 2.33 kNN classifier

As in other classifiers, all performance metrics are calculated and tabulated. Also, the results of each testing can be obtained by clicking on the table given in the GUI. When any row of the table is clicked, the confusion matrix and ROC curve appear automatically.

Acknowledgment

Thanks the members of CTG Resource Group for their valuable contributions.

Dr. Zafer CÖMERT Bitlis Eren University, Bitlis, Turkey

Dr. Adnan Fatih KOCAMAZ İnönü University, Malatya, Turkey

Dr. Laura Burattini Polytechnic University, Ancona, Italy

Opt. Dr. Sami GÜNGÖR Medical park Hospital, Elazığ, Turkey

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Dr. Subha Velappan Manonmaniam Sundaranar University,

India

Dr. Manivanna Boopathi Arumugam Bahrain Training Institute, Kingdom of

Bahrain.

Ph.D. Candidate Zhang Yang Hangzhou Dianzi University, China

Ph.D. Candidate Agnese Sbrollini Università Politecnica delle Marche, Italy

CTG OASOpen access software for Fetal Heart Rate Analysis

www.ctganalysis.com