Department of Medical Electronics syllabus VII, VIII sem... · To explain concepts of CT,...

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)

Medical Electronics and Engineering

Department of Medical Electronics

VII SEMESTER SCHEME

(2019-20 Scheme)

Sl Sub.Code Name of Subject LH T PR S C

1 ML7T01 Biomedical Digital Signal

Processing 4 0 0 0 4

2 ML7T02 Principles of Medical Imaging 4 0 0 0 4

3 ML7T03 IoT and smart sensors 3 0 0 1 4

4 ML7PE2X Elective II 3 0 0 0 3

5 ML7PE3X Elective III 3 0 0 0 3

6 ML7L01 BMDSP Lab 0 0 3 0 1.5

7 ML7L02 C++ and Python Lab 0 0 3 0 1.5

8 ML7PW01 Project Work 0

8 0 4

Total 18 00 14 01 25

LH=Lecture Hour; T = Tutorial Hour; XX = CV/ ME/ EE/ EC/ CS/ML…;

PR= Practical Hour; OE= Open Elective; S=Self-study Hour; Q = 1/2/3/…;

C= Credit; R = 1/2/3/ ….; L = Laboratory; PW = Project Work.

Elective –II Credits 3-0-0-3

Sub.Code Name of the Subject Sub.Code Name of the Subject

ML7PE21 Artificial Organs and

Biomaterials ML7PE23

Linear Algebra and its

applications in medicine

ML7PE22 Adaptive Signal Processing ML7PE24 Brain Computer Interface

Elective –III Credits 3-0-0-3

Sub.Code Name of the Subject Sub.Code Name of the Subject

ML7PE31 Pattern Recognition in Medicine ML7PE33 Ergonomics and

Rehabilitation Engineering

ML7PE32 Biometrics ML7PE34 Artificial Intelligence



Department of Medical Electronics

VIII SEMESTER SCHEME

(2019-20 Scheme)

Sl.

No.

Sub.Code Name of Subject LH T PR S C

1 ML8T01 Neural Networks 4 0 0 0 4

2 ML8T02 Biomedical Therapeutic Equipments 4 0 0 0 4

3 ML8PE3X Elective IV 3 0 0 0 3

4 ML8PE4X Elective V 3 0 0 0 3

5 ML8PW02 Project Work 2 4 12 0 10

6 ML8TS01 Technical Seminar 0 0 0 1 1

Total 16 04 12 01 25

LH=Lecture Hour; T = Tutorial Hour; XX = CV/ ME/ EE/ EC/ CS/ML…;

PR= Practical Hour; OE= Open Elective; S=Self-study Hour; Q = 1/2/3/…;

C= Credit; R = 1/2/3/ ….; L = Laboratory; PW = Project Work;

TS=Technical Seminar.

Elective –IV Credits 3-0-0-3

Sub.Code Name of the Subject

ML8PE311 Speech Signal Processing

ML8PE312 Machine Learning

ML8PE313 Smart Wearable Systems

ML8PE314 Clinical Data Analytics

Elective –V Credits 3-0-0-3

Sub.Code Name of the Subject

ML8PE411 ARM Processors

ML8PE412 Robotics and Automation

ML8PE413 Medical Device Development

ML8PE414 Virtual BMI



Syllabus for the Academic Year 2019 - 2020

Department: Medical Electronics Semester: VII Subject Name: Biomedical Digital Signal Processing

Subject Code: ML7T01 L-T-P-C: 4-0-0-4

Course Objectives:

Course Outcomes

Sl. No Course Objectives

1

This course helps to understand the nature and difficulties to acquire bio-

signal and its processing concepts for analysis.

2

It also helps to bring out the concepts related Neurological signal processing

and Sleep disorder.

3 Explains the concept of data compression techniques.

4 Emphasizes on Signal averaging, adaptive filers and its applications.

Course outcome

Descriptions

CO1

On completion of the course the student can recall Understand the origin of EEG signals and their characteristics.

CO2 Understand the origin of ECG signals and their characteristics

CO3

Understand the processing techniques required to analyze the bio

medical signals

CO4 Understand data reduction techniques for ECG signal.



UNIT Description Hours

1

Introduction to Biomedical Signals: The nature of biomedical signals, the action potential, objectives of biomedical signal analysis, Difficulties in biomedical signal analysis, computer aided diagnosis. Neurological signal processing: The brain and its potentials, The electrophysiological origin of brain waves, The EEG signal and its characteristics, EEG analysis.

11

2

ECG Signal Processing: ECG data acquisition, ECG lead system, ECG parameters and their estimation, ECG QRS detection techniques: Template matching, differentiation based QRS detection techniques. Estimation of R-R Interval: Finite first difference method. The use of multi-scale analysis for parameter estimation of ECG waveforms, Arrhythmia analysis monitoring, long term continuous ECG recording.

11

3

Sleep EEG: Data acquisition and classification of sleep stages, The Markov model and Markov chains, Dynamics of sleep-wake transitions, Hypnogram model parameters, event history analysis for modeling sleep.

08

4

ECG Data Reduction Techniques: direct data compression techniques, direct ECG data compression techniques: Turing point algorithm, AZTEC algorithm and FAN algorithm, other data compression techniques: data compression by DPCM, data compression method comparison.

10

5

Signal Averaging: Basics of signal averaging, signal averaging as a

digital filter, a typical averager.

Adaptive Filters: Principle of an adaptive filter, the steepest descent algorithm, adaptive noise canceller: (a)cancellation of 60 Hz interference in electrocardiography, (b) Canceling donor-heart

interference in heart-transplant electrocardiography, (c)Cancellation of ECG signal from the electrical activity of the chest muscles, (d)canceling of maternal ECG in fetal ECG, (e)Cancellation of High frequency noise in Electro-surgery.

12



Question paper Pattern:

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMKUR. (A Constituent College of Sri Siddhartha Academy of Higher Education, Agalakote, Tumkur.)

Model Question Paper

ML7T01 : BIOMEDICAL DIGITAL SIGNAL PROCESSING

TIME: 3.00 Hours SEM: VII MAX MARKS: 100

NOTE: Answer any five full questions.

1.a) With block diagram explain computer aided diagnosis and therapy based

upon biomedical signal analysis.

10

b) List and explain the difficulties encountered in Biomedical signal acquisition

and analysis.

10

OR

2.a) With neat diagram of neuron explain function of different parts of neuron. 6

b) What is action potential? Discuss genesis and propagation of action

potential.

8

c) List different types of EEG signals with frequency range. 6

3.a) Explain high speed QRS detection algorithm/Pan Tompkins algorithm. 10

b) Draw neat diagram of ECG. Mention different parts and functions of ECG. 6

c) What is template matching technique of QRS detection? 4

OR

4.a) Discuss ECG lead system in detail. 10

b) Explain various methods used for estimation of R-R interval. 10

5.a) Explain important Characteristics of EEG in various sleep stages. 10

b) Discuss Markov model and Morkov chains with an example. 10

OR

6.a) Discuss Dynamics of sleep wake transitions. 10

b) What is a hypnogram? Explain with an example. Explain how hypnogram

parameters used in sleep EEG analysis.

10

7.a) Explain how ECG data reduction is done by AZTEC algorithm. 10

b) Explain data compression by DPCM technique with neat block diagram. 10

OR

8.a) Explain how ECG data reduction is done by Turning point algorithm. 10



b) With an example explain ECG data compression by FAN algorithm. 10

9.a) Explain the principle of adaptive filters with neat diagram and equations. 10

b) Explain Widrow-Hoff Least-Mean-Sqaure adaptive algorithm in detail. 10

OR

10.a) Explain cancellation of 60 Hz interference in electrocardiography. 10

b) Explain the procedure involved in cancelling of maternal ECG in fetal

electrocardiography.

10

Text Books:

Sl No

Text Book title Author Volume and Year of Edition

1 Biomedical Digital Signal Processing

Willis J. Tompkins PHI.

2 Biomedical Signal Processing principles and techniques D. C. Reddy Tata McGraw-Hill,

2005

3 Biomedical Signal Analysis

Rangaraj M. Rangayyan,

IEEE Press, 2001.

Reference Book:

Sl No


1 Biomedical Signal Processing

Akay M Academic: Press 1994

2 Biomedical Signal Processing Cohen.A Vol. I Time &

Frequency Analysis, CRC Press, 1986.




Department: Medical Electronics Semester: VII Subject Name: Principles of Medical Imaging


Course Objectives:

Sl.No Course Objectives

1

Build the physics background of interaction of radiation with matter,

enabling participants to understand projection radiography,

mammography, and fluoroscopy and train them to assess image

distortions, image attenuation for X-ray radiography systems.

2

Expose students to the developments in X-ray Computed Tomography

leading to modern day multi-slice, helical CT scanners and introduce

the concept of computed tomography reconstruction

3

Divulge the image formation, image quality, and imaging hardware for

ultrasound scanning. Explain the imaging principles and derive the

fundamental equation of MRI.

4

Expose the participants to advanced MR techniques including fast spin

echoes, MR angiography, echo planar imaging, magnetization prepared

sequences, diffusion and perfusion theory and sequences.



Course Outcomes

Course outcome

Descriptions

CO1 On the completion of the course the students shall be able To gain knowledge on X-rays and its generation.

CO2 To understand and distinguish different diagnostic method.

CO3 To explain concepts of CT, Projection functions of CT.

CO4 Understand the principles of Radionuclide imaging and Magnetic resonance imaging.


I

X-rays: Introduction to Electromagnetic Spectrum, Fundamentals of X-Rays, Generation and Detection of X-Rays, X-ray Diagnostic Methods.

11

II

X-Rays: Recent Developments, X-ray Imaging Characteristics, Biological effects of Ionizing radiation.

10

III

Ultrasound: Fundamentals of Acoustic Propagation, Generation and Detection of Ultrasound, Ultrasonic Diagnostics Methods, New Developments, Image Characteristics, Biological effects of Ultrasounds.

10

IV

Radionuclide Imaging: Fundamentals of Radioactivity, Generation and Detection of nuclear emission, Diagnostic methods using radiation detector probes, Radionuclide Imaging Systems, New Radionuclide Imaging methods, Characteristics of Radionuclide Images, Internal radiation dosimeter and biological effects.

11

V

Magnetic Resonance Imaging

Fundamentals of nuclear magnetic resonance, Generation and Detection of NMR signal, Imaging Methods, In vivo NMR Spectroscopy, Characteristics of MRI, Biological Effects of Magnetic Fields.

10




Text Books:

Sl No


1 Principles of Medical Imaging

Shung K. Kirk, Tsui Benjamin, Smith.B.Michael

2 Fundamentals of Medical Imaging

Suetens Paul Cambridge University Press, 2002

Reference Book:

Sl No


1 Handbook of Biomedical

Instrumentation

Khandpur R.S. 2nd Ed., Tata-

McGRaw Hill, 2003.




Department: Semester: VII Subject Name: IoT and SMART SENSORS


Course Objectives:

Course Outcomes


1

Understand the purpose of measurement, the methods of measurements,

errors associated with measurements.

2

Know the principle of transduction, classifications and the characteristics

of different transducers and study its biomedical applications.

Course outcome

Descriptions

CO1

On the completion of this course the student will be able to Explain the basic design and requirement of IoT.

CO2

Identify the importance of different types of protocols and models

used with IoT.

CO3 Analyze the requirements of components of smart sensors.

CO4 Determine the importance of communication protocol and standards that is used with smart sensors and improve the functionality of conventional systems using IoT.




I

Introduction to IoT: Definition & Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT Enabling Technologies, IoT Levels.

08

II

IoT System Management: Introduction, Machine-to-Machine (M2M), Difference between IoT and M2M, SDN and NFV for IoT, Need for IoT System Management, SNMP, Network Operator Requirements, NETCONF, YANG, IoT Systems Management with NETCONF-YANG.

08

III

Domain Specific IoTs: Applications, Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry, health & Lifestyle.

07

IV

Smart Sensors, Signal Conditioning and Control: Introduction, Smart Sensor Model, SLEEPMODETM Operational Amplifiers, Rail – to – Rail Operational Amplifiers, Switched Capacitor Amplifier, 4 – to 20 mA Signal Transmitter, Analog to Digital Converter, MCU control, Modular MCU Design, DSP control.

08

V

Protocols and Standards for Smart Sensors: CAN protocol, CAN Module, Neuron Chips, MCU Protocols, IEEE 1451 working relationship, IEEE 1451.1, IEEE 1451.2, IEEE P1451.3, IEEE P1451.4.

07




Text Books:

Sl No


1 Internet of Things – A hands-on

approach, Arshdeep Bahga and Vijay Madisetti

Universities Press

(India) Private Ltd.,

2015

2 Understanding Smart Sensors

Randy Frank 2nd Edition, Artech House Publications, 2000.

Reference Book:

Sl No


1 Rethinking the Internet of Things: A

Scalable Approach to Connecting

Everything

Francis daCosta and Byron Henderson

Apress Open,

Intel Publication.

2014

2 Learning Internet of Things, Smart Peter Waher, PACKT Publishing,

2015

3 Sensor Systems Gerard Meijer,

John – Wiley and Sons

2008.




Department: Semester: VII Subject Name: Artificial Organs and Biomaterials

Subject Code: ML7PE21 L-T-P-C: 3-0-0-3

Course Objectives:

Course Outcomes


1

Course Objectives: To create awareness to the student with modern artificial organs devices

and methods used to partially support or completely replace pathological

organ

2 Understand the design and working of artificial heart, kidney, and blood.

3 To know about working of heart valve. Design of artificial heart valve

4

Study about biomaterial which is used for design of artificial organ.

Understand the characteristics of polymeric and metallic biomaterial.

Course outcome

Descriptions

CO1

on the completion of this course the student will be able to Understand the need of artificial organs.

CO2

Understand the function of various organs in your body.

CO3

Learn about the design of the various artificial organs using biomaterial.

CO4

Understand the various biomaterials. Learn composite,

biodegradable polymeric and tissue derived materials.




I

ARTIFICIAL ORGANS: INTRODUCTION: Substitutive medicine, outlook for organ replacement, design consideration, evaluation process. ARTIFICIAL HEART AND CIRCULATORY ASSIST DEVICES: Engineering design, Engg design of artificial heart and circulatory assist devices.

07

II

ARTIFICIAL KIDNEY: Functions of the kidneys, kidney disease, renal failure, renal transplantation, artificial kidney, dialyzers, and membranes for haemodialysis, haemodialysis machine, peritoneal dialysis equipment-therapy format, fluid and solute removal. ARTIFICIAL BLOOD: Artificial oxygen carriers, fluorocarbons, hemoglo bin for oxygen carrying plasma expanders, hemoglobin based artificial blood.

07

III

ARTIFICIAL LUNGS: Gas exchange systems, Cardiopulmonary bypass (heart-lung machine)-principle, block diagram and working, artificial lung versus natural lung. CARDIAC VALVE PROSTHESES: Mechanical valves, tissue valves, current types of prostheses, tissue versus mechanical, engineering concerns and hemodynamic assessment of prosthetic heart valves, implications for thrombus deposition, durability, current trends in valve design.

08

IV

CERAMIC BIOMATERIALS: Introduction, non absorbable/relatively bioinert bioceramics, biodegradable/restorable ceramics, bioreactive ceramics, deterioration of ceramics, bioceramic-manufacturing techniques POLYMERIC BIOMATERIALS: Introduction, polymerization and basic structure, polymers used as biomaterials, sterilization, surface

modifications to for improving biocompatibility.

08

V

BIOMATERIALS: Introduction to biomaterials, uses of biomaterials, biomaterials in organs & body systems, materials for use in the body, performance of biomaterials. METALLIC BIOMATERIALS: Introduction, Stainless steel, Cobalt-Chromium alloy, Titanium alloys, Titanium-Nickel alloys, Dental metals, Corrosion of metallic implants, Manufacturing of implants.

09




Text Books:

Sl No


1 Biomedical Engineering Handbook- J.D.Bronzino Volume1(2nd Edition)

(CRC Press / IEEE Press, 2000).

2 Biomedical Engineering Handbook J.D.Bronzino Volume 2 (2nd Edition)

(CRC Press / IEEE Press, 2000)

3 Handbook of Biomedical Instrumentation

R.S.Khandpur (2nd Edition) by (Tata McGraw Hill, 2003)

Reference Books:

Sl No


1

2

3




Department: Medical Electronics Semester: VII Subject Name: Adaptive Signal Processing


Course Objectives:

Course Outcomes


1

2

3

4

Course outcome

Descriptions

CO1 Describe optimal minimum mean square estimators and in particular linear estimators.

CO2 Hypothesize Wiener filters (FIR, non-causal, causal) and evaluate their performance.

CO3 Apply combination of theory and software implementations to solve adaptive signal problems.

CO4 Identify applications in which it would be possible to use the different adaptive filtering approaches.




I

ADAPTIVE SYSTEMS: Definition and characteristics, Areas of application, general properties, open and close loop adaptation, Application closed loop adaptation, examples of adaptive systems. The adaptive linear combiner: General description, input signal and weight vectors, desired response and error, the performance function gradient and minimum mean square error. Example of a performance surface, alternative expression of the gradient, De correlation of error and input components.

08

II

PROPERTIES OF QUADRATIC PERFORMANCE SURFACE: Normal form of input correlation Matrix, Eigen and eigen vectors of the input correlation matrix. An example with two weights, geometrical significance of Eigen vectors and Eigen values.

08

III

SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface. Basic idea of gradient search methods, A simple gradient search algorithm and its solution.

08

IV

SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface. Basic idea of gradient search methods, A simple gradient search algorithm and its solution. Stability and rate of convergence, the learning curve, Gradient search by Newton’s method in multi dimensional space, gradient search by the method of steepest descent, comparison of learning curves.

09

V

GRADIENT ESTIMATION AND EFFECTS ON ADAPTATION: Gradient component estimation by derivatives measurements, the performance penalty, derivative measurement and performance penalties with multiple weights.

06




Text Books:

Sl No


1

Adaptive signal Processing

B. Widrow & S D

Streans,

Pearson Education

1985.

Reference Book:

Sl No


1 Adaptive filters C F N Cowan & P M

Grant

Prentice Hall, 1985.




Department: Medical Electronics Semester: VII Subject Name: Linear Algebra and Its Applications In Medicine


Course Objectives:

Course Outcomes


1

Solve systems of linear equations using various methods including

Gaussian and GaussJordan elimination and inverse matrices.

2

Perform matrix algebra, invertibility, and the transpose and understand

vector algebra in Rn .

3

Determine relationship between coefficient matrix invertibility and

solutions to a system of linear equations and the inverse matrices.

4 Find the dimension of spaces such as those associated with matrices and linear transformations.

Course outcome

Descriptions

CO1 Understand LU factorization and elements of vector spaces.

CO2

Learn linear transformations and least square approximations to solve inconsistent systems, Orthonormal

vectors using Gram-Schmidtt process and QR factorization.

CO3 Understand concepts in Eigen spaces and its applications

CO4

Understand the concept of probability, distributions and its application in Biology and medical Science.




I

Linear equations: Fields; system of linear equations, and its solution sets;

elementary row operations and echelon forms Matrix operations; invertible

matrices, LU-factorization.

Vector spaces: Vector spaces; subspaces; bases and dimension; coordinates;

summary of row-equivalence; computations concerning subspaces.

09

II

Linear Transformations: Linear transformations; algebra of linear

transformations; isomorphism; representation of transformations by matrices;

transpose of a linear transformation.

08

III

Canonical Forms: Characteristic values; invariant subspaces; direct-sum

decompositions; invariant direct sums; primary decomposition theorem; cyclic

bases; Jordan canonical form.

07

IV

Inner Product Spaces: Inner products; inner product spaces; orthogonal sets

and projections.

08

V

Gram-Schmidt process; QR-factorization; least-squares problems; unitary

operators Symmetric Matrices and Quadratic Forms: Digitalization; quadratic

forms; constrained Optimization; singular value decomposition.

07




Text Books:

Sl No


1 "Linear Algebra and its Applications",

Gilbert Strang, 4thEdition, Thomson Learning Asia, 2007.

2 "Linear Algebra and its Applications", David C. Lay,

3rd Edition, Pearson Education (Asia) Pvt. Ltd, 2005.

3 "Introductory Linear Algebra with Applications” Bernard Kolman

and David R. Hill, Pearson Education (Asia) Pvt. Ltd, 7th edition, 2003.

Reference Book:

Sl No


1

2




Department: Medical Electronics Semester: VII Subject Name: Brain Computer Interface


Course Objectives:

Course Outcomes


1

This course aims for students to obtain the background to understand brain-computer interaction and human-computer interaction;

2

understand the literature in the field of brain sensing for human-computer

interaction research;

3

Understand the various tools used in brain sensing, with a focus on functional near-infrared spectroscopy (fNIRS) research at Drexel.

4

Understand the steps required to use real-time brain sensing data as input to an interactive system.

5 understand the domains and contexts in which brain-computer interfaces may be effective;

6

Understand the open questions and challenges in brain-computer interaction research today.

Course outcome

Descriptions

CO1

Apply the knowledge of mathematics science and engineering

fundamentals to understand the Brain Organization.

CO2

Apply the knowledge of mathematics science and engineering

fundamentals to understand the brain anatomy and Function.

CO3 Analyze and process the brain signals for artifact reduction.

CO4 Understand types of BCI, principles and its applications which are present state of art in the Neurosciences domain.




I

Basic Neurosciences: Basic Neuroscience: Neurons, Action Potentials or Spikes, Dendrites and Axons, Synapses, Spike Generation, Adapting the Connections: Synaptic Plasticity – (LTP, LTD, STDP, Short-Term Facilitation and Depression), Brain Organization, Anatomy, and Function. Recording and Stimulating the Brain: Recording Signals from the Brain: Invasive Techniques &Noninvasive Techniques. Stimulating the Brain - Invasive Techniques & nonTechniques. Simultaneous Recording and Stimulation: Multi-electrode Arrays, Neurochip.

08

II

Signal Processing for BCI's: Spike Sorting, Frequency Domain Analysis: Fourier analysis, Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Spectral Features, Wavelet Analysis. Time Domain Analysis: Hjorth Parameters , Fractal Dimension , Autoregressive (AR) Modeling, Bayesian Filtering, Kalman Filtering, Particle Filtering), Spatial Filtering : (Bipolar, Laplacian, and Common Average Referencing ,Principal Component Analysis (PCA) ,Independent Component Analysis (ICA) , Common Spatial Patterns (CSP) 73 Artifact Reduction Techniques: Thresholding, Band-Stop and Notch Filtering, Linear Modeling, Principal Component Analysis (PCA), Independent Component Analysis (ICA).

08

III

Building a BCI: Major Types of BCIs: Brain Responses Useful for Building BCIs:Conditioned Responses, Population Activity, Imagined Motor and Cognitive Activity, Stimulus-Evoked Activity. Invasive BCIs: Two Major Paradigms in Invasive Brain-Computer Interfacing: BCIs Based on Operant Conditioning, BCIs Based on Population Decoding.

08

IV

Invasive BCIs in Humans: Cursor and Robotic Control Using a Multielectrode Array Implant, Cognitive BCIs in Humans, Long-Term Use of Invasive BCIs, Long-Term BCI Use and Formation of a Stable

Cortical Representation, Long-Term Use of a Human BCI Implant Semi-Invasive BCIs:Electrocorticographic (ECoG) BCIs -ECoG BCIs in Animals, ECoG BCIs in Humans, BCIs Based on Peripheral Nerve Signals Nerve-Based BCIs, Targeted Muscle Innervations (TMR). Non-Invasive BCIs:Oscillatory Potentials and ERD, Slow Cortical Potentials, Movement Related Potentials, Stimulus Evoked Potentials; BCIs Based on Cognitive Tasks, Error Potentials in BCIs, Co-adaptive BCIs, Hierarchical BCIs. Other Noninvasive BCIs: fMRI, MEG, and fNIR: Functional Magnetic Resonance Imaging Based BCIs, Magneto encephalography Based BCIs, Functional Near Infrared and Optical BCIs. BCIs that Stimulate: Sensory Restoration, Restoring Hearing: Cochlear Implants, Restoring Sight: Cortical and Retinal Implants, Motor Restoration, Deep Brain Stimulation (DBS), Sensory Augmentation.

09



V

Medical Applications: Sensory Restoration, Motor Restoration, Cognitive Restoration, Rehabilitation, Restoring Communication with Menus, Cursors, and Spellers, Brain Controlled Wheelchairs Nonmedical Applications: Web Browsing and Navigating Virtual Worlds, Robotic Avatars, High Throughput Image Search Lie Detection and Applications in Law , Monitoring Alertness, Estimating Cognitive Load, Education and Learning, Security, Identification, and

Authentication, Physical Amplification with Exoskeletons, Mnemonic and Cognitive Amplification , Applications in Space, Gaming and Entertainment, Brain-Controlled Art. Ethics of Brain-Computer Interfacing: Medical, Health, and Safety Issues, Balancing Risks versus Benefits, Informed Consent, Abuse of BCI Technology, BCI Security and Privacy, Legal Issues, Moral and Social-Justice Issues.

06


Text Books:

Sl No


1

Brain-Computer Interfacing

Rajesh P. N. Rao

An Introduction (1

Edition)

2 Brain-ComputerInterfaces

Revolutionizing Human Bernhard Graimann

(Editor), Brendan Z.

Allison (Editor),

GertPfurtscheller

(Editor)

Computer Interaction

(The Frontiers

Collection)

Hardcover – (13 Dec

2010)

Reference Book:

Sl No


1

2




Department: Medical Electronics Semester: VII

Subject Name: Pattern Recognition in Medicine


Course Objectives:

Course Outcomes


1

Pattern recognition techniques are used to design

automated systems that improve their own performance

through experience..

2

This course covers the methodologies, technologies, and

algorithms of statistical pattern recognition from a variety

of perspectives

3

Topics including Bayesian Decision Theory, Estimation

Theory, Linear Discrimination Functions, Nonparametric

Techniques, Decision Trees, and Clustering Algorithms

etc. will be presented.

Course outcome

Descriptions

CO1

CO2

CO3

CO4





I

Introduction: Machine perception, pattern Recognition systems, Design cycles, learning and adaptation. Probability: Random variable, joint distribution and densities, moments of random variable, Estimation of parameters from sample.

08

II

Statistical decision making: Introduction, Baye’s theorem, multiple

features, conditionally independent features, decision bounderies, unequal costs of error, estimation of error rates, characteristic curves, problems. (3.1-3.7, 3.9 from text 1).

08

III

Non parametric Decision making: Introduction, Histograms, kernel and window estimators, nearest neighbor classification techniques, adaptive decision boundaries, adaptive discriminate functions, minimum squared error discriminant functions. (4.1-4.7 text 1)

08

IV

Clustering: Introduction, Hierarchical clustering, partitional clustering, Unsupervised Bayesion learning, Hierarchical clustering, partitional clustering, problems.

07

V

Processing of waveforms and images: Introduction, gray level scaling transformations, equalization, geometric image scaling and interpolation, edge detection, laplacian and sharpening operators, line detection and template matching, logarithmic gray level scaling. (7.1-7.9 text 1)

08



Text Books:

Sl No


1 Pattern Recognition and Image

Analysis Earl Gose, Richard Johnson Baugh and Steve

PHI

Reference Book:

Sl No


1 Pattern classification

Richard O.Duda, Peter E.Herd and David & Stork

john Wiley and sons, Inc 2nd Ed.2001.

2 Pattern Recognition: Statistical Structural and Neural Approaches,

Robert Schlkoff John Wiley and sons, Inc, 1992.




Department: Medical Electronics Semester: VII Subject Name: Biometrics


Course Objectives:

Course Outcomes


1

Course Objectives: To understand the state-of-the-art in biometric technologies;

2 To survey the currently available biometric systems;

3

To explore ways to improve some of the current techniques;

4

To learn and implement some of the biometrics authentication;

5 To explore new techniques

Course outcome

Descriptions

CO1 Understand the fundamentals and the need of biometrics.

CO2

Learn the deployment, strength & weakness of the types of

Biometrics.

CO3 Learn the uncommon biometrics and its usage.

CO4 Understand the applications of Biometrics and learn the risks, standards and testing / Evaluation process of Biometrics.





I

Introduction – Benefits of biometric security – Verification and identification – Basic working of biometric matching – Accuracy – False match rate – False non-match rate – Failure to enroll rate – Derived metrics – Layered biometric solutions.

08

II

Finger scan – Features – Components – Operation (Steps) – Competing finger Scan technologies – Strength and weakness. Types of algorithms used for interpretation. Voice Scan - Features – Components – Operation (Steps) – Competing voice Scan (facial) technologies – Strength and weakness.

08

III

Iris Scan - Features – Components – Operation (Steps) – Competing iris Scan technologies – Strength and weakness. Facial Scan - Features – Components – Operation (Steps) – Competing facial Scan technologies – Strength and weakness.

08

IV

Other physiological biometrics – Hand scan – Retina scan – AFIS (Automatic Finger Print Identification Systems) – Behavioral Biometrics – Signature scan- keystroke scan.

07

V

Biometrics Application – Biometric Solution Matrix – Bio privacy – Comparison of privacy factor in different biometrics technologies – Designing privacy sympathetic biometric systems. Biometric standards – (BioAPI , BAPI) – Biometric middleware. Biometrics for Network Security: Statistical measures of Biometrics. Biometric Transactions.

08



Text Books:

Sl No


1

Biometrics–Identity Verification in a

Networked World

Samir Nanavati,

Michael Thieme, Raj

Nanavati

Wiley India Pvt Ltd,

2002 .

2 Biometrics for Network Security Paul Reid Pearson Education,

2004.

Reference Book:

Sl No


1 Biometrics- The Ultimate Reference John D.

Woodward, Jr. Wiley Dreamtech.

2 Biometric Systems Technology, Design and Performance Evaluation

James Wayman, Anil Jain, Davide Maltoni and Dario Maio

Springer Publications.

3 Personal Identification in Networked Society

Jain, A.K.; R Bolle, Ruud M.; S Pankanti, Sharath

1st ed. 1999.2nd printing, 2006, Springer Publications.

4 Handbook of Biometrics Jain, Anil K.;

Flynn, Patrick; Ross, Arun A

Springer, 2008.




Department: Medical Electronics Semester: VII Subject Name: Ergonomics and Rehabilitation Engineering


Course Objectives:

Course Outcomes


1

This course covers the use of ergonomic principles to recognize, evaluate, and control

workplace conditions that cause or contribute to musculoskeletal and nerve disorders. Course topics include work physiology, anthropometry,

musculoskeletal disorders, use of video display terminals, and risk factors such as vibration,

temperature, material handling, repetition, and lifting and patient transfers in health care.

2

Course emphasis is on industrial case studies

covering analysis and design of work stations and equipment workshops in manual lifting,

and coverage of current OSHA compliance policies and guidelines.

Course outcome

Descriptions

CO1

CO2

CO3

CO4




I

Introduction : Focus of ergonomics & its applications, Body mechanics: Basics, Anatomy of Spine & pelvis related to posture, postural stability & adaptation, Low back pain, risk factors formusculo skeletal disorders in workplaces, Anthropometric principles in workspace: Designing for a population of users, Human variability sources, applied anthropometry in ergonomics & design, anthropometry & personal space.

08

II

Design of Repetitive Tasks: Work related musculoskeletal disorders, injuries to upper body at work, neck disorders, carpal tunnel syndrome, tennis elbow, shoulder disorder, ergonomic interventions. Design of physical environment: human thermoregulation, thermal environment, working in hot & cold climates, skin temperature, protection against extreme climates, comfort & indoor climate, ISO standards.

07

III

Engineering Concepts in Rehabilitation Engineering: Anthropometry: Methods for Static and dynamic Measurements: Area Measurements, Measurement of characteristics and movement, Ergonomic aspects in designating devices: Introduction to Models in Process Control, Design of Information Devices, and Design of Controls Active Prostheses: Active above knee prostheses. Myoelectric hand and arm prostheses- different types block diagram, signal flow diagram and functions. The MARCUS intelligent Hand prostheses.

08

IV

Engineering concepts in sensory rehabilitation engineering: Sensory augmentation and substitution: Visual system: Visual augmentation, Tactual vision substitution, and Auditory vision substitution. Auditory system: Auditory augmentation, Audiometer, Hearing aids, cochlear

implantation, visual auditory substitution, tactual auditory substitution, Tactual system: Tactual augmentation, Tactual substitution.

08

V

Orthopedic Prosthetics and Orthotics in rehabilitation: Engineering concepts in motor rehabilitation, applications. Computer Aided Engineering in Customized Component Design. Intelligent prosthetic knee, A hierarchically controlled prosthetic and A self-aligning orthotic knee joint. Externally powered and controlled Orthotics and Prosthetics. FES systems- Restoration of hand function, restoration of standing and walking, Hybrid Assistive Systems (HAS).

08




Text Books:

Sl No


1 Introduction to Ergonomics

R S Bridger Rout ledge Taylor & Francis group, London,2008

2 Handbook of biomedical engineering.

Bronzino, Joseph 2nd edition, CRC Press, 2000. 24

3 Rehabilitation engineering. Robinson C.J CRC press 1995.

Reference Book:

Sl No


1 Fitting the task to human, A textbook of occupational ergonomics

Taylor &Francis 5th edition, ACGIH publications , 2008

2 Work study & Ergonomics by, DhanpatRai& sons 1992

3 Intelligent systems and technologies in rehabilitation engineering;.

Horia- NocholaiTeodorecu, L.C.Jain

CRC; December 2000

4 Fitting the task to the man, Etienne Grandjean, Harold Oldroyd,

Taylor & Francis,1988.




Department: Medical Electronics Semester: VII

Subject Name: Artificial Intelligence


Course Objectives:

Course Outcomes


1

To create appreciation and understanding of both the achievements of AIand the theory underlying those achievements.

2

To impart basic proficiency in representing real life problems in a state space representation so as to solve them using different AI techniques.

3

To create an understanding of the basic issues of knowledge representation and heuristic search techniques.

Course outcome

Descriptions

CO1 On completion of this course, the students shall be able to Demonstrate the knowledge of building blocks of AI.

CO2 Analyze and formalize the problem as a state space tree, design heuristics and solve using different search techniques.

CO3 Analyze and demonstrate knowledge representation using various techniques.

CO4 Develop AI solutions for a given problem.





I

Introduction: What is Artificial Intelligence?, AI Problems, The underlying Assumption, What is an AI Technique, Problems, problem spaces, and search Defining the problem as a State Space Search, Production Systems, Problem Characteristics, Production System

Characteristics, Issues in the Design of search programs, Additional Problems.

08

II

Heuristic and Search Techniques: Generate-and-Test, Hill Climbing, Best-First Search, Problem Reduction, Constraint satisfaction, Means-Ends Analysis

08

III

Knowledge Representation Issues: Representation and Mappings, Approaches to knowledge Representation, Issues in knowledge Representation, Weak Slot Filler Structures: Semantic Nets, Frames

08

IV

Using Predicate Logic: Representing the simple facts in logic, Representing Instance and ISA Relationships, Computable functions and predicates, Resolution, Natural Deduction

08

V

Strong slot-and-Filter Structures: Conceptual Dependency, Scripts, CYC Expert Systems Representation and Using Domain Knowledge, Expert Systems shells, Explanation, Knowledge Acquisition.

07



Text Books:

Sl No


1 Artificial Intelligence Elaine Rich, Kevin

Knight,Shivashankar B Nair

3rd Edition, Tata

McGraw Hill, 1991.

Reference Book:

Sl No


1 Artificial Intelligence A Modern Approach

Stuart Russel, Peter Norvig

2nd Edition, Pearson Education, 2003.

2 Principles of Artificial Intelligence Nils J. Nilsson Elsevier, 1980.




Department: Medical Electronics Semester: VII Subject Name: Biomedical Digital Signal Processing Lab

Subject Code: ML7L01 L-T-P-C: 0-0-3-1.5

Course Objectives:

Course Outcomes


1 To understand the basic signals in the field of biomedical.

2

To study origins and characteristics of some of the most commonly used biomedical signals, including ECG, EEG, evoked potentials, and EMG.

3 To understand Sources and characteristics of noise and artifacts in bio signals.

4 To understand use of bio signals in diagnosis, patient monitoring and physiological investigation.

Course outcome

Descriptions

CO1 To read and plot different biomedical signals

CO2 To add and eliminate noise in signals.

CO3 Realization of Digital filters.

CO4

Understand and apply various data compression techniques on different

ECG signals.




Text Books:

Sl

No

Text Book title Author Volume and Year

of Edition

1 Bioelectrical Signal Processing in Cardiac & Neurological Applications

Leif SSrnmo , Pablo Laguna - Elsevier -

Academic Press.

Reference Book:

Sl No


1 Biomedical Digital Signal Processing Willis J. Tompkins PHI.

2 Biomedical Signal Processing-principles and techniques by

D. C. Reddy Tata McGraw-Hill, 2005

3 Biomedical Signal Analysis by M.Rangayyan Rangaraj

IEEE Press, 2001.


I

1. Computation of Convolution and Correlation Sequences. 2. Signal Averaging to Improve the SNR 3. Read and plotting of ECG data, spectrum of ECG with 50 HZ

noise. 4. Design of FIR Filter for ECG. 5. Integer filters for ECG

6. QRS detection and Heart rate determination. 7. Correlation and Template matching. 8. Realization of Notch filter for removal of line interference 9. Data Compression Techniques using AZTEC algorithm. 10. Data Compression Techniques using TP algorithm. 11. Data Compression Techniques using FAN algorithm.

Note: The above experiments are to be conducted using Matlab/ Lab VIEW/ “C” language.




Department: Medical Electronics Semester: VII Subject Name: C++ and Python Lab

Subject Code: ML7L02 L-T-P-C: 0-0-3-1.5

Course Objectives:

Course Outcomes


1

2

3

4

Course outcome

Descriptions

CO1 By the completion of this course, the student will be able to: know how to use data types based on the programs and declare variables.

CO2 Learn the concepts and importance of functions, arrays, classes & objects.

CO3 Understand the concept of Operator Overloading and inheritance for effective programming.

CO4 Learn the basic concepts of python.




I

C++ Lab: 1. Write a C++ program to calculate the sum of the series i) 1+x+x2+x3+...+xn ii) -1+2-4+8-16+...1024 2. Write a C++ program to sort the elements of an array using i) Selection sort ii) Bubble sort

3. Write a C++ program to accept two arrays of different lengths. Merge the two accepted arrays. 4. Write a C++ program to accept two 2-dimensional arrays and perform addition, subtraction and multiplication. 5. Write a C++ program to find the LCM and GCD of 2 given numbers using functions. 6. Write a C++ program to find the factorial of a given number using recursive function. 7. Write a C++ program to find the largest, smallest and their averages using functions. 8. Write a C++ program to accept the information about an employee and calculate the following and display using structure. i) Accept the basic salary, name, id_no of an employee. ii) Calculate DA, HRA, PF, LIC, Gross and net salary. DA: 45% of basic salary HRA: If basic is >=2000 and <3000, HRA=800 If basic is >=3000 and <4000, HRA=1000 If basic is >=4000 and <6000, HRA=1200 If basic is >=6000, HRA=1500 PF: 11.5% of basic salary LIC: 17% of basic salary Gross=basic salary+DA+HRA Net salary=Gross-PF-LIC 9. Write a C++ program to find the sum of two complex numbers using classes by overloading operator +.

10. Write a C++ program to multiply two numbers using Multiple Inheritance. Python Lab:

2.Basic programs using python:

i) Display of a word/ sentence.

ii) Performing calculations.

iii) Use of variables and objects.

iv)Use of loops, arrays, functions, plots.




Text Books:

Sl No


1 Object Oriented programming in TURBO C++ ,

Robert Lafore, Galgotia

Publications.2002.

2. Classic Data Structures, Debasis Samanta, Second Edition,

PHI, 2009.

Reference Book:

Sl No


1 Object Oriented Programming with C++

E.Balaguruswamy, third edition, TMH 2006

2 C++ the complete reference, Herbert Schildt, fourth edition, TMH, 2003.




Department: Medical Electronics Semester: VIII Subject Name: Neural Networks


Course Objectives:

Course Outcomes


1 This course gives an introduction to basic neural network architectures and learning rules.

2

Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their application to practical engineering problems in such areas as pattern recognition, signals processing and control systems.

Course outcome

Descriptions

CO1

on the completion of this course the students will be able to The fundamental concepts of artificial neural network.

CO2 Network architectures and its principles.

CO3 Different learning algorithms and its applications.

CO4 Information representation in biological system and its models.





I

Introduction: The classic neuron, Membrane potential, Action potential, Neuronal electrical behavior, Cable Equation, Synaptic Integration. Models of Neuron, Synaptic Electrical Events, slow potential theory of neuron, two state neurons, Feedback.

10

II

Network Architectures: Single layer feed forward networks; Multilayer feed forward networks, Recurrent Networks, Knowledge representation.

11

III

Learning processes: Introduction Error correction learning, Memory based learning, Hebbian Learning, Competitive learning.

10

IV

Learning paradigms: Learning with a teacher, Learning without a teacher, Learning tasks, Memory, Adaptation Artificial intelligence and Neural networks.

10

V

Information representation in biological Systems, Distributed, Map, layered structures, Visual system, Auditory System.

11



Text Books:

Sl No


1 An Introduction to neural networks

James A. Anderson 2e PHI 1995

2 Neural Networks Simon Haykin Pearson education

PHI 2001

Reference Book:

Sl No


1 Fundamentals of Artificial Neural Networks

Mohammad Hasan PHI, 1999

2




Department: Medical Electronics Semester: VIII Subject Name: Biomedical Therapeutic Equipments


Course Objectives:

Course Outcomes


1

The objective of this course is to introduce the students to the application of biomedical instrumentation used in surgery.

2

This course is to familiarize the students with physiotherapy and electrotherapy instruments and various machines used in ICU.

3

It includes brief study of different types of ventilators and how to design a automated drug delivery unit depends on the requirement of patient.

Course outcome

Descriptions

CO1 Learn the working principle of Instruments for surgery and physiotherapy, electrotherapy instruments

CO2 Understand the working of kidney, design of artificial kidney. Advantages and need of anesthesia machine.

CO3 Understand the principles of ventilators, study about different types of ventilators

CO4 Analyzing the concepts of Automated Drug delivery Systems.





I

Instruments for Surgery: Principles of surgical diathermy, surgical diathermy Machine, safety aspects in electro- surgical units, surgical diathermy Analyzer.

10

II

Physiotherapy and Electrotherapy Equipments: High frequency heat therapy, Shortwave diathermy, microwave diathermy, ultrasound therapy unit, Electro diagnostic therapeutic apparatus, pain relief through electrical Stimulation, bladder and cerebella stimulators.

10

III

Haemodialysis Machine: Artificial kidney, dialyzer, Membranes for haemodialysis. Lithotripters: Stone disease problems, lithotripter machine, extra-corporeal Shock wave therapy. Anesthesia Machine: Need for anesthesia, anesthesia Machine

10

IV

Ventilators: Artificial ventilation, ventilators, types of ventilators, ventilators terms, classification of ventilators. Modern ventilators. Humidifiers, Nebulizers and Aspirators

10

V

Automated Drug Delivery Systems: Infusion pumps, components of drugs infusion systems and implantable infusion systems. Closed Loop Control Infusion Pumps.

12



Text Books:

Sl No


1 Handbook of Biomedical Instrumentation

R.S.Khandpur, McGraw Hill, 2003.

2 Biomedical Instrumentation Dr.M. Arumugam- Second Edition-

1994.

Reference Book:

Sl No


1

2




Department: Medical Electronics Semester: VIII Subject Name: SPEECH SIGNAL PROCESSING


Course Objectives:

Course Outcomes


1 To understand the characteristics of speech signal,

2 To apply signal processing concepts to speech signal,

3 To get an insight into a few applications of speech processing.

Course outcome

Descriptions

CO1 On completion of the course the student can recall Properties of speech signal and its production and discrimination system

CO2 Design of filter bank and its implementation, and spectrographic display.

CO3 Digital representation of speech signal using different quantization techniques.

CO4 LPC algorithms and its applications for speech coding and fundamental algorithms for speech synthesis, coding and recognition.




I

Digital Models For Speech Signals: Process of Speech Production,

Lossless tube models, Digital models for Speech signals.

Time Domain Models For Speech Processing: Time dependent

processing of speech, Short time energy and average magnitude, Short

time average zero crossing rate, Speech Vs silence discrimination

using energy and zero crossing.

08

II

Short Time Fourier Analysis: Linear filtering interpretation, Filter

bank summation method, Design of digital filter banks,

Implementation using FFT, Spectrographic displays.

08

III

Digital Representations Of The Speech Waveform: Sampling speech signals, Review of the statistical model for speech, Instantaneous quantization, Adaptive Quantization, General theory of differential quantization, Delta modulation.

08

IV

Linear Predictive Coding Of Speech: Basic principles of linear predictive analysis, Solution of LPC equations, Prediction error signal, Frequency domain interpretation, Relation between the various speech parameters, Applications of LPC parameters.

07

V

Speech Synthesis: Principles of Speech synthesis, Synthesis based

on waveform coding, analysis synthesis method, speech production

mechanism, Synthesis by rule, Text to speech conversion. Speech Recognition: Principles of Speech recognition, Speech period detection, Spectral distance measures, Structure of word recognition systems, Dynamic time warping (DTW), Word recognition using phoneme units.

08




SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMKUR (An autonomous institution under Visvesvaraya Technological University, Belgaum)

ML8PE311: Speech Signal Processing Model Question Paper

TIME: 3 HOURS SEM: 8TH

MAX MARKS: 100

Answer any five full questions:

1. a) Explain different classes of phonemes. 12M

b) Explain the transfer function of the lossless tube model. 08M

OR

2. a) With the relevant block diagram explain the general discrete-time model for speech

roduction. 10M

b) Explain mechanism of speech production with relevant diagram 10M

3. a) What are the effects of filter bank summation method to the short time spectrum on

the resulting synthesis? 08M

b) Write a note on spectrographic displays. 07M

c) Find the cutoff frequency for all the low pass filters when the input sampling rate is

9.6 kHz and we wish to design a filter bank of 15 equally spaced filters that covers the

range 200Hz to 3200 Hz? 05M

OR

4. a) Explain the methods for implementing synthesis of a single channel in terms of

linear filtering. 10M

b) Explain the implementation of FBS method using FFT by briefing both analysis and

synthesis techniques. 10M

5. a) Briefly explain the 2 common uniform quantizer characteristics. 06M

b) Explain DPCM with adaptive quantization. 08M

c) Explain briefly the adaptive quantization with variable step size and variable gain

representation. 06M

OR



6. a) Explain the following:

i) Feed forward adaptive quantizer.

ii) Feedback adaptive quantizer. 12M

b) Explain linear delta modulation. 08M

7. a) Explain the autocorrelation method of linear prediction 08M

b) Explain frequency domain interpretations of-

i) Linear predictive analysis

ii) Mean squared prediction error. 12M

OR

8. a) Explain Durbin’s recursive solution for autocorrelation equations 08M

b) Explain the applications of LPC parameters. 12M

9. a) Give the comparison of the features of three speech synthesis methods along with

the figure of their basic principles. 06M

b) Write a note on Synthesis based on waveform coding 07M

c) Explain briefly Text to speech conversion 07M

OR

10. a) Explain the difficulties in speech recognition. 06M

b) Explain the structure of word recognition systems. 07M

c) Write short note on Word recognition using phoneme units. 07M



Text Books:

Sl No


1 Digital Processing of Speech Signals L R Rabiner and R

W Schafer, Pearson Education 2004.

2 Digital Speech Processing-

Synthesis and Recognition,.

Sadoaki Furui,

2nd Edition,

Mercel Dekker 2002.

Reference Book:

Sl No


1 Introduction to Data Compression Khalid Sayood 3rd Edition, Elsivier Publications

2 Digital Speech A M Kondoz, 2nd Edition, Wiley Publications




Department: Medical Electronics Semester: VIII Subject Name: Machine Learning


Course Objectives:

Course Outcomes


1

The main goal of this course is to help students learn, understand, and practice big data analytics and machine learning approaches, which include the study of modern computing big data technologies and scaling up machine learning techniques focusing on industry applications.

2

Mainly the course objectives are: conceptualization and summarization of big data and machine learning, trivial data versus big data, big data computing technologies, machine learning techniques, and scaling up machine learning approaches.

Course outcome

Descriptions

CO1

Apply the knowledge of mathematics science and engineering fundamentals in the understanding of fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.

CO2 Analyze the strengths and weaknesses of many popular machine learning approaches.

CO3

Comprehend the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.

CO4 Design and implement various machine learning algorithms in a range of real-world applications.





I

Introduction: Introduction to machine learning, Examples of Machine Learning Applications. Parametric regression: linear regression, polynomial regression, locally weighted regression, numerical optimization, gradient descent, kernel methods.

08

II

Generative learning: Gaussian parameter estimation, maximum

likelihood estimation, MAP estimation, Bayesian estimation, bias and variance of estimators, missing and noisy features, nonparametric density estimation, Gaussian discriminant analysis, naive Bayes. Discriminative learning: linear discrimination, logistic regression, logit and logistic functions, generalized linear models, softmax regression.

08

III

Neural networks: the perceptron algorithm, multilayer perceptrons, backpropagation, nonlinear regression, multiclass discrimination, training procedures, localized network structure, dimensionality reduction interpretation. Support vector machines: functional and geometric margins, optimum margin classifier, constrained optimization, Lagrange multipliers, primal/dual problems, KKT conditions, dual of the optimum margin classifier, soft margins, kernels, quadratic programming, SMO algorithm.

08

IV

Graphical and sequential models: Bayesian networks, conditional independence, Markov random fields, inference in graphical models, belief propagation, Markov models, hidden Markov models, decoding states from observations, learning HMM parameters.

07

V

Unsupervised learning: K-means clustering, expectation maximization, Gaussian mixture density estimation, mixture of naive Bayes, model selection. Dimensionality reduction: feature selection, principal component analysis, linear discriminant analysis, factor analysis, independent component analysis, multidimensional scaling,

and manifold learning.

08



Text Books:

Sl No


1 Elements of Statistical Learning, T. Hastie,

R. Tibshirani and J. Friedman,

Springer, 2001.

2 Machine Learning, EthemAlpaydin, MIT Press, 2010.

Reference Book:

Sl No


1 Pattern Recognition and Machine Learning,

C. Bishop, Springer, 2006.

2 Machine Learning: A Probabilistic Perspective,

K. Murphy, MIT Press, 2012.

3 Pattern Classification, R. Duda, E. Hart, and D. Stork,

Wiley-Inter science, 2000.

4 Machine Learning T. Mitchell, McGraw-Hill, 1997.




Department: Medical Electronics Semester: VIII Subject Name: SMART WEARABLE SYSTEMS


Course Objectives:

Course Outcomes


1

Extensive efforts have been made in both academia and industry in the research

and development of smart wearable systems (SWS) for health monitoring (HM).

Primarily influenced by skyrocketing healthcare costs and supported by recent

technological advances in micro- and nanotechnologies, miniaturisation of

sensors, and smart fabrics, the continuous advances in SWS will progressively

change the landscape of healthcare by allowing individual management and

continuous monitoring of a patient’s health status.

2

Consisting of various components and devices, ranging from sensors and

actuators to multimedia devices, these systems support complex healthcare

applications and enable low-cost wearable, non-invasive alternatives for

continuous 24-h monitoring of health, activity, mobility, and mental status, both

indoors and outdoors. Our objective has been to examine the current research in

wearable to serve as references for researchers and provide perspectives for future

research

Course outcome

Descriptions

CO1 Understand the basic foundations on biological and artificial neural network and the importance of neuron models for pattern classification

CO2 Demonstrate the process of forming association between related patterns through associative networks

CO3 Apply the principles of back propagation supervised learning for error minimization

CO4

Understand and analyze the various competition based learning algorithms and importance of resonance based network learning algorithms.




I

Introduction : What is Wearable Systems, Need for Wearable

Systems, Drawbacks of Conventional Systems for Wearable

Monitoring, Applications of Wearable Systems, Recent developments –

Global and Indian Scenario, Types of Wearable Systems, Components

of wearable Systems, Physiological Parameters commonly monitored

in wearable applications, Smart textiles, & textiles sensors, Wearable

Systems for Disaster management, Home Health care, Astronauts,

Soldiers in battle field, athletes, SIDS, Sleep Apnea Monitoring.

08

II

Smart Sensors& Vital Parameters : Vital parameters monitored and

their significances, Bio-potential signal recordings (ECG, EEG, EMG),

Dry Electrodes design and fabrication methods, Smart Sensors –

textile electrodes, polymer electrodes, non-contact electrodes, MEMS

and Nano Electrode Arrays, Cuff-less Blood Pressure Measurement,

PPG, Galvanic Skin Response (GSR), Body Temperature

Measurements, Activity Monitoring for Energy Expenditure,

Respiratory parameters.

08

III

Wearable Computers : Flexible Electronics, Wearable Computers,

Signal Processors, Signal Conditioning circuits design, Power

Requirements, Wearable Systems Packaging, Batteries and charging,

Wireless Communication Technologies and Protocols, Receiver

Systems, Mobile Applications based devices.

08

IV

Wireless Body Area Networks: Wireless Body Area Networks –

Introduction, Personal Area Networks (PAN), Application in Vital

Physiological Parameter monitoring, Design of Sensor & Sink Nodes,

Architecture, Communication & Routing Protocols, Security, Power

and Energy Harvesting.

07

V

Data Processing And Validation : Classification Algorithms, Data

Mining and Data Fusion, Signal Processing Algorithms in wearable

Applications, Issues of wearable physiological monitoring systems,

Statistical Validation of Parameters, Certifications of Medical Devices

and Patenting.

08




Text Books:

Sl No


1 Wearable Monitoring Systems, Annalisa Bonfiglo,

Danilo De Rossi, Springer, 2011

2 Wearable Sensors: Fundamentals,

Implementation and Applications,

Edward Sazonov, Micheal R Neuman,

Elseiver, 2014.

Reference Book:

Sl No


1 Wearable Electronics: Design, Prototype

and wear your own interactive garments,

Kate Hartman, Make Maker Media

2 , Wearable Technology, Elijah Hunter Kindle Edition

3 Body Sensor Networks, Guang Zhong Yang, Springer .




Department: Medical Electronics Semester: VIII Subject Name: Clinical Data Analytics


Course Objectives:

Course Outcomes


1 Identify key tools and approaches to improve analytics capabilities in clinical settings.

2

Describe different governance and operations strategies in analytics in clinical settings.

3 Discuss value-based payment systems and the role of data analytics in achieving their potential.

4 Analyze data used in population management and value-based care systems

Course outcome

Descriptions

CO1

Ability to apply knowledge of mathematics, science and

Engineering to develop the solution using biostatistical concepts.

CO2 Ability to analyse a problem and formulate appropriate solution for biostatistical concepts application.

CO3 An ability to design and perform statistical test and interpret results

CO4 Ability to implement and demonstrate statistical analysis using modern tool usage.




I

Introduction to Biostatistics: Introduction, Some basic concepts, Measurement and Measurement Scales, Simple random sample, Computers and biostatistician analysis. Descriptive Statistics: Introduction, ordered array, grouped data-frequency distribution, descriptive statistics – measure of central tendency, measure of dispersion, measure of central tendency probability distributions of

discrete variables, binomial distribution, Poisson distribution, continuous probability distribution, normal distribution.

08

II

Sampling distributions: distribution of sample mean, distribution of the difference between two sample means, distribution of sample proportion, distribution of the difference between two sample proportions, Estimation: confidence interval for a population mean, t-distribution, confidence interval for differences between two population means, confidence interval for a population proportion, confidence interval for difference between two populations determination of sample size for estimating means, for estimating proportions , confidence interval for the variance of normally distributed population, confidence interval for ratio of variances of two normally distributed populations.

08

III

Hypothesis Testing : Introduction, hypothesis testing – single population mean, difference between two population means, paired comparisons, hypothesis testing-single population proportion, difference between two population proportions, single population variance, ratio of two population variances.

07

IV

Analysis of Variance (ANOVA): Introduction, completely randomized design, randomized complete block design, repeated measures design, factorial experiment UNIT-5 8 hours Linear Regression and Correlation: the regression model, sample regression equation, evaluating and using regression equation, correlation model correlation coefficient Multiple linear regression model, obtaining multiple regression equation, evaluating multiple regression equation, using the multiple regression equation, multiple correlation model, mathematical properties of Chisquare distribution.

08

V

Linear Regression and Correlation: the regression model, sample regression equation, evaluating and using regression equation, correlation model correlation coefficient Multiple linear regression model, obtaining multiple regression equation, evaluating multiple regression equation, using the multiple regression equation, multiple correlation model, mathematical properties of Chi-square distribution.

08




Text Books:

Sl No


1 1. .“Biostatistics-A Foundation for

Analysis in the Health Sciences”

2. Wayne W. Daniel,

John Wiley & Sons Publication, 6th Edition

2 Fundamentals of Biiostatistics khan and

khanum, Ukaaz publications, 2nd revise edition

3 “An introduction to statistical Method and data analysis”,

R.Lyman ott..

Reference Book:

Sl No


1

2

3




Department: Medical Electronics Semester: VIII Subject Name: ARM PROCESSORS


Course Objectives:

Course Outcomes


1

This course introduces the concept of architecture and programming of advanced embedded microcontrollers i.eARMfamily of microcontrollers that are widely used in design of real time sophisticated embedded systems like tablets, hand held devices, automation and industrial control systems.

2

It also covers writing Embedded C programming of LPC2148 for GPIO,ADC,DAC, UART, LCD, Timers and etc.

3 It also explains the concepts of embedded system and its components

Course

outcome

Descriptions

CO1 Describe the ARM processor architecture and its family.

CO2 Develop assembly language programs to perform specific tasks using ARM instructions.

CO3 Develop ARM microcontroller applications using Embedded C language.

CO4 Design and develop program to interface external hardware with LPC214x microcontroller.





I

ARM EMBEDDED SYSTEMS The RISC Design Philosophy, The ARM Design Philosophy, Embedded System Hardware, Embedded System Software. ARM PROCESSOR FUNDAMENTALS Registers, Current Program Status Register, Pipeline, Exceptions, Interrupts, and Vector Table, Core Extensions, Architecture Revisions,

ARM Processor Families, LPC2148 Microcontroller Architecture, Memory Mapping, Register Description.

08

II

INTRODUCTION TO THE ARM INSTRUCTIONS SET Data Processing Instructions, Branch Instructions, Load-Store Instructions, Software Interrupt Instructions, Program Status Register Instruction, Example Programs.

07

III

INTRODUCTION TO THE ARM INSTRUCTIONS SET contd…. Loading Constants, ARMv5E Extensions, Conditional Execution, and Example Programs. EFFICIENT C PROGRAMMING Overview of C Compilers and Optimization, Basic C Data Types, C Looping Structures, Register Allocation, Function Calls, Pointer Aliasing, Structure Arrangement, Bit-fields, Unaligned Data and Endianness, Division, Floating Point, Inline Functions and Inline Assembly.

08

IV

Interfacing Sensors, Actuators, GPIO, LED, 7 segment display, stepper motor, Keyboard, Push button switch, Data Conversions (ADC, DAC), Timers, Communication Protocols: UART, I2C, SPI, CAN(onboard), Programs using C.

08

V

Embedded System Components Embedded v/s General computing system, Classification of Embedded systems, Major applications and purpose of Embedded systems. Core of an Embedded System including all types of processor/controller, Memory.

08



Text Books:

Sl No


1 ARM Systems Developer's Guide Designing and Optimizing System Software,

Andrew N. Sloss,

Dominic Symes, Chris Wright,

Morgan Kaufmann

Publishers, ElseveirInc, 2004.(Chapters 1, 2, 3, 5)

2 Introduction to Embedded Systems, Shibu K V, Secondedition,

Tata McGraw Hill Education Private Limited, 2017. (Chapters 1 and 2 selected topics)

3 LPC214x User Manual –

http://www.keil.com/dd/docs/datashts/philips/user_manual_lpc214x.pdf

(LPC2148, GPIO, Registers, Embedded

components selected)

Reference Book:

Sl No


1 ARM System On Chip Architecture Steve Furber, Second Edition, Pearson Education

Limited, 2000.

2 ARM ASSEMBLY LANGUAGE Fundamentals and Techniques

WilliamHohl, Christopher

Hinds, Second Edition, CRC Press, 2015.

3 ARM Assembly Language An Introduction

Gibson, Second Edition, 2007.






Department: Medical Electronics Semester: VIII Subject Name: Robotics And Automation


Course Objectives:

Course Outcomes


1 This course introduces fundamental concepts in robotics.

2

The objective of the course is to provide an introductory

understanding of robotics. Students will be exposed to a

broad range of topics in robotics with emphasis on basics

of manipulators, coordinate transformation and kinematics,

trajectory planning, control techniques, sensors and

devices, robot applications and economics analysis.

Course outcome

Descriptions

CO1 Understand the fundamental concepts of robot

CO2 Calculate the forward kinematics and inverse kinematics of serial and parallel robots.

CO3 Be able to calculate the Jacobian for serial and parallel robot.

CO4 Be able to do the path planning for a robotic system.





I

BASIC CONCEPTS Automation and Robotics – An over view of Robotics – present and future applications – classification by coordinate system and control system, Hydraulic, Pneumatic and electric drivers – Determination HP of motor and gearing ratio.

08

II

MANIPULATORS: Construction of Manipulators, Manipulator

Dynamic and Force Control, Electronic and Pneumatic manupulators. ACTUATORS AND GRIPPERS Pneumatic, Hydraulic Actuators, Stepper Motor Control Circuits, End Effecter, Various types of Grippers.

08

III

TRANSFORMATION AND DYNAMICS Differential transformation and manipulators, Jacobians – problems.Dynamics: Lagrange – Euler and Newton – Euler formations.

07

IV

KINEMATICS Forward and Inverse Kinematic Problems, Solutions of Inverse Kinematic problems,Multiple Solution, Jacobian Work Envelop – Hill Climbing Techniques.

08

V

PATH PLANNING Trajectory planning and avoidance of obstacles, path planning, skew motion, joint integrated motion – straight-line motion.

08



Text Books:

Sl No


1 Industrial Robotics Groover M P Pearson Edu.

2 Robotics control, Sensing, Vision and Intelligence,

Fu, K.S., Gonzalez, R.C., and Lee, C.S.G.,

McGraw-Hill Publishing company, New Delhi, 2003.

3 Robot Engineering-An Integrated Approach,

Klafter, R.D., Chmielewski, T.A., and Negin. M,

Prentice Hall of India, New Delhi, 2002.

4 Introduction to Robotics Mechanics and Control,

Craig, J.J., Addison Wesley, 1999.

Reference Book:

Sl No


1 Robotics, CSP Rao and V.V. Reddy,

Pearson Publications (In press)

2 An Introduction to Robot Technology,

P. Coiffet and M. Chaironze Kogam

3 Robot Analysis and Intelligence Asada and Slow time Wiley Inter-Science.

4 Robot Dynamics and Control Mark W. Spong and M. Vidyasagar,

JohnPage Ltd. 1983 London.Wiley & Sons..




Department: Medical Electronics Semester: VIII Subject Name: Medical Device Development


Course Objectives:

Course Outcomes


1 Understand the processes for medical device development after “design freeze”

2 Become familiar with the European regulatory framework for medical devices

3 Gain an understanding of manufacturing process validation

4 Build on the student’s current understanding of the Quality Management System

5 Understand key aspects of Product Management both during and after product launch

6 Discuss Good Clinical Practices and regulations surrounding management of clinical trials

Course outcome

Descriptions

CO1 Identify and analyse unmet clinical need and its requirements to solve it.

CO2 Search, analyse and document clinical practice, engineering science and relevant literature in order to determine the need for further research and development in a chosen clinical area.

CO3 Develop a sustainable business plan, including market overview, regulation strategies for health & safety of individuals and intellectual property (IP) strategies.

CO4 . Understand medical device design engineering and manufacturing process by avoiding common quality pitfalls in turn learning project management.





I

MedTech Invention: Needs finding through Observation and Problem Identification. Need Statement Development. Need Screening & Selection through Stakeholder Analysis, Market Analysis & Needs Filtering. Concept Generation, Screening and selection.

10

II

Product Requirements: Define MedTech Device. Classification of

Device. Role of Requirements in MedTech Product Development. Market Requirements, Customer Requirements, Clinical Workflow. Design Input. ISO 13485. Intended use, Functional / performance requirements, safety, usability requirements etc.....

07

III

Design Engineering: Design and Development Plan. Design Process. Design Outputs, Intermediate deliverables - System Architecture, Subsystem requirements, Prototype, System Integration. Design Review. Design Verification.

08

IV

Validation: System Validation. Usability Validation. Safety Validation. Clinical Validation, Regulatory Submission UNIT V [6 hours] Program Management: Program Planning, Stage Gate Process, Milestones. Budgeting, Development Strategy, Risk identification and Mitigation process.

07

V

Program Management: Program Planning, Stage Gate Process, Milestones. Budgeting, Development Strategy, Risk identification and Mitigation process.

06



Text Books:

Sl No


1 “Biodesign: The Process of Innovating

Medical Technologies”,

Stefanos Zenios ,

Josh Makower,

Paul Yock, Todd J.

Brinton, Uday N.

Kumar, Lyn

Denend, Thomas

M. Krummel

Cambridge

University Press; 2nd

edition.

Reference Book:

Sl No


1 “Inventing medical devices: A perspective from India”

Dr Jagdish Chaturvedi

CreateSpace Independent Publishing Platform; 1st edition,2015.

2 “The Medical Device R&D Handbook”

Theodore R. Kucklick

Second Edition, CRC Press, 2012.




Department: Medical Electronics Semester: VIII Subject Name: Virtual BMI


Course Objectives:

Course Outcomes


1

The main goal of this course is for students to learn

applications of programming, signal transduction, data

acquisition, data analysis, and signal processing used in the

design of medical and laboratory instrumentation.

2

The software package LabVIEW has become a standard in

academic and industrial environments for data acquisition,

interfacing of instruments and instrumentation control.

3

Students will learn LabVIEW as a tool for the design of

computer-based virtual instruments, which add software-

based intelligence to sensors and basic laboratory bench

devices.

Course

outcome

Descriptions

CO1 Describe the Graphical System Design approach & basic features and techniques of Lab VIEW.

CO2 Use the Modular Programming concepts for creation of VIs & employ DAQ assistant for configuration of hardware devices.

CO3 Describe the Lab VIEW and BioBench software for EMG, ECG, and Cardiopulmonary system analysis.

CO4 Explain the Medical Device Development Applications for Surgical Video Systems and Healthcare Information Management Systems using Information Science and Technology.




I

Graphical System Design (GSD): Introduction, GSD model, Design flow with GSD, Virtual Instrumentation, Virtual Instrumentation and traditional instrumentation, Hardware and software in virtual instrumentation, Virtual Instrumentation for test, control and design, GSD using LabVIEW, Graphical programming and textural programming. Introduction to LabVIEW: Introduction, Advantages of

LabVIEW, Advantages of LabVIEW, Software environment, Creating and saving a VI, Front panel toolbar, Block diagram toolbar, Palettes, Shortcut menus, Property dialog boxes, Front panel controls and indicators, Block diagram, Data types, Data flow program, LabVIEW documentation resources, Keyword shortcuts.

07

II

Modular Programming: Introduction, Modular Programming in LabVIEW, Build a VI front panel and block diagram, ICON and connector pane, Creating an icon, Building a connector pane, Displaying subVIs and express Vis as icons or expandable nodes, Creating subVIs from sections of a VI, Opening and editing subVIs, Placing subVIs on block diagrams, Saving subVIs, Creating a stand-alone application. Data Acquisition: DAQ software architecture, DAQ assistant, Channels and task configurations, Selecting and configuring a data acquisition device, Components of computer based measurement system.

08

III

General Goals of Virtual Bio-Instrumentation (VBI): Definition of VBI and importance, General Goals of VBI applications. Basic Concepts: DAQ basics, LabVIEW basics, BioBench basics. Neuromuscular Electrophysiology (Electromyography): Physiological basis, Experiment set up, Experiment descriptions, Trouble shooting the nerve –Muscle Preparation. Cardiac Electrophysiology (Electrocardiology):Physiological basis, Experiment descriptions. Cardiopulmonary Applications: Cardiopulmonary measurement system, Hiw the Cardiopulmonary measurement system works, Clinical Significance.

08

IV

Medical Device Development Applications: The Endotester – A Virtual Instrument –Based Quality control and Technology, Assessment System for surgicalvideoSystems: Introduction, Materials and Methods, Endoscope Tests, Results, Discussion. Fluid Sense Innovative IV Pump Testing: Introduction, The test System, Training Emulator.

08

V

Healthcare Information management Systems: Medical Informatics: Defining medical informatics, Computers in medicine, Electronic Medical record, Computerized physician order entry, Decision support. Information Retrieval, Medical Imaging, Patient Monitoring, Medical Education, Medical Simulation. Managing Disparate Information: ActiveX, ActiveX Data Objects(ADO), Dynamic Link Libraries, Database Connectivity, Integrated Dashboards.

08




Text Books:

Sl No


1 Virtual Instrumentation using

LabVIEW

Jovitha Jerome PHI Learning Private

Limited 2010. (Module 1

& 2)

2 “Virtual Bio-Instrumentation”

Biomedical Clinical, and Healthcare

Applications in Lab VIEW.

Jon B. Olansen

and Eric Rosow,

Prentice Hall Publication,

2002.

Reference Book:

Sl No


1

2

3

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