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Centre for Interdisciplinary Biomedical and Human Factors Engineering

Courses

Program Name:

M. Tech.

Specialisation

Biomedical Engineering

Number of seats:

06

Degree Requirement:

Refer Guide to admission

Course Requirement

Refer Guide to admission

Accreditation

No

Job Prospects:

Multi-national companies, public sector units, academics etc.

Course Structure (2020-21 batch):

S. No.

Course Category

Course No.

Course Title

Contact Period per Week

Credits

L

G

P

Semester I

1

PC

BMC6010

Biostatistics

3

1

0

4

2

PC

BMC6020

Anatomy and Physiology

3

1

0

4

3

PE

BME6110

Bioinstrumentation

3

1

0

4

4

PE

BME6140

Data Acquisition and Analysis

3

1

0

4

5

PE

BME6330

Machine Learning

3

1

0

4

Total Credits

20

Semester II

1

PE

BME6310

Artificial Intelligence and Neural Networks

3

1

0

4

2

PC

BMC6900

Laboratory

0

0

4

2

3

PC

BMC6800

Seminar - I

0

2

0

2

4

PC

BMC6910

Pre-Dissertation

0

3

0

10

Total Credits

18

Semester III

1

PC

BMC7810

Seminar - II

0

2

0

2

2

PC

BMC7920

Dissertation (Phase - I)

0

3

0

14

Total Credits

16

Semester IV

1

PC

BMC7820

Pre-Dissertation Seminar

0

2

0

2

2

PC

BMC7930

Dissertation (Phase - II)

0

3

0

16

Total Credits

18




Course Title

Biostatistics

Course number

BMC6010

Credits

4

Course Category

PC (Program Core)

Pre-requisite

Introductory course on Probability and Statistics

Contact Hours (L-G-P)

3-1-0

Type of Course

Theory

Course Objectives

This course aims at providing the necessary basic concepts in probability, random processes and introductory statistics. Knowledge of fundamentals and applications of phenomena will greatly help in the understanding of topics such as estimation and detection, pattern recognition, signal processing.

Course

Outcomes

At the end of the course the students will be able to

1.      Understand and characterise phenomena which evolve with respect to time in probabilistic manner. Acquire skills in handling situations involving more than one random variable.

2.      Understand the concept of random process and be able to analyse and solve the problems.

3.      Have a basic knowledge of statistics and its usage.

4.      To be able to apply statistical concepts to problems.

Syllabus


Lecture

Unit-I: Probability Distributions and Random Variables


Probability: basic probability model, Bayes rule, Conditional probability, Random Variables: Distribution and density functions, Expectation, Moments, Multiple Random Variables: Joint Distribution and Joint Density Function, Independence, Covariance and Correlation, Central limit theorem

16

Unit-II: Random Processes


Concept of a Random Process - Classification, Ergodicity, Stationarity and Independence, Time averages and Ensemble averages, Correlation Functions and its properties, Gaussian and Poisson Random Process

08

Unit-III: Basic Statistics


Measures of Central tendency: Moments, Skewness and Kurtosis; Probability distributions: Binomial, Poisson and Normal; Correlation and Regression.

12


Unit-IV:
Applied Statistics


Descriptive measures of engineering data, sampling distributions, estimation of mean and variance, confidence intervals, hypothesis testing, linear regression

12


Total No. of Lectures

48

Books*/

References


1.   George R. Cooper and Clare D. McGillem, “Probabilistic Methods of Signal and System Analysis”, 3rd edition, Oxford University Press, 2007

2.   Peyton Z. Peebles Jr., “Probability, Random Variables and random Signal Principles”, 4th edition, McGraw Hill Education, 2017.

3.   R. E. Walpole, R. H. Myers, S. L. Myers, K. E. Ye, “Probability and Statistics for Engineers,” Pearson, 2014.

4.   R. A. Johnson, Probability and Statistics for Engineers, PHI

5.   J H Zar, Biostatistical Analysis, Pearson Education.

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations

15 Marks

Mid Term Examination

25 Marks

Sessional Total

40 Marks

End Semester Examination

60 Marks

Total

100 Marks


Course Title

Anatomy and Physiology

Course number

BMC6020

Credits

4

Course Category

PC (Program Core)

Pre-requisite

-

Contact Hours (L-G-P)

3-1-0

Type of Course

Theory

Course Objectives

This course aims to provide the necessary basic knowledge of Medical Science to students with engineering background. It will help in understanding and working with living system and to apply advanced technology to the complex problems of Medicare

Course

Outcomes

At the end of the course the students will:

1.    have a basic knowledge of terminology, level of organization, tissue of the body and general anatomy of skin, muscles, nerve, vessels, bones and  joints.

2.    be able to apply knowledge of human biology in understanding the design of the instruments

3.    be able to describe and discuss the physiological basis of various mechanisms operating in various organs and systems of the body. 

4.    develop critical reasoning skills in its application to biomedical engineering.

Syllabus


Lecture

Unit-I:

Levels of organization and tissues of the body (cell, tissues of the body), Introduction to skin and fascia, Introduction to Skeletal system(cartilage and bone), Muscular system (general feature, type, structure & function of muscles, innervations of skeletal muscle, Joints (including definition, classification and movements, Nervous system (subdivision of N.S, nervous tissue, spinal cord and spinal segments, receptors, reflex arc, nerve fibre and their myelination, ANS).

12

Unit-II:

Introduction and anatomy of respiratory system; Introduction and anatomy of heart; Introduction and anatomy of GIT tract; Introduction and anatomy of excretory system; Anatomy and classification of endocrine system

12

Unit-III:

Cell Physiology - Functions and transport across cell; Nerve Muscle Physiology – Resting membrane potential and Action potentials in excitable tissue, Types of muscle and their mechanism of contraction, Structure and properties of neuromuscular junctions; Central Nervous System (CNS)- Organization of CNS, Synapse, Motor and sensory system, Maintenance of posture and balance, Basal ganglia; Special Senses- Physiology of vision and hearing

11

Unit-IV:

Respiratory System – Mechanics of breathing, Ventilation -perfusion, Transport of gases, Regulation of respiration; Cardiovascular- Basics of ECG, Cardiac output, Regulation of cardiovascular system; Gastro-intestinal – GI motility, Physiological functions of Liver, gall bladder, intestine and pancreas; Endocrinology – Mechanism of hormone action, Hormones of Pituitary, Thyroid, Parathyroid, Adrenal Gland, Adrenal Cortex; Renal – Mechanism of Urine formation, physiology of Acid- Base Balance

13


Total No. of Lectures

48

Books*/

References

  1. R Drake, A W Vogl, A Mitchell, Gray’s Anatomy for Students, 4th edition, Elsevier 2020

2.     B D Chaurasia,  Human Anatomy , 7th edition, CBS publisher, 2017

  1. B D Chaurasia, Handbook of General Anatomy, CBS publisher, 2009

4.     I Singh, Textbook of Human Histology with color atlas and practical guide, 8th edition, Jaypee Brothers Medical Publishers; 2016.

5.     J E Hall, Guyton and Hall Textbook of Medical Physiology, 13th edition, Elsevier, 2015.

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations

15 Marks

Mid Term Examination

25 Marks

Sessional Total

40 Marks

End Semester Examination

60 Marks

Total

100 Marks


Course Title

Bioinstrumentation

Course number

BME6110

Credits

4

Course Category

PE (Program Elective)

Pre-requisite

-

Contact Hours (L-T-P)

3-1-0

Type of Course

Theory

Course Objectives

The students will know the fundamental concepts of biomedical instrumentation and develop their ability to analyze the signals and solve problems. It also aims to explain the principles of and ways in which to build the instrumentation, including different kinds of sensors.

Course

Outcomes

At the end of the course the students will be able to

  1. Develop a clear knowledge about human physiology system.

  2. Understand various methods of acquiring biosignals.

  3. Analyze and evaluate the principles of various biomedical devices and sensors.

  4. Describe and design the instrumentation for amplifying the bioelectrical signals.

Syllabus


Lecture

Unit- I: Introduction

Physiology: Cell and its structure, Resting and Action Potentials, Propagation of Action Potentials, Nervous system – CNS –PNS – Nerve cell – Synapse, Cardio pulmonary system, Physiology of heart and lungs

12

Unit – II: Virtual Instrumentation:

Bioelectric Potentials – ECG, EEG, EMG, MEG, Electrophysiological measurements: Bio potential Electrodes, Lead systems and recording methods –Typical waveforms, Bioelectric amplifiers; Interference in Biosignals

12

Unit – III: Sensors:

Transducers and Sensors characteristics, Transducers for biomedical applications, Transducers for Body temperature, Blood pressure& respiration rate, Sensor performance characteristics and Intelligent sensors, Classify medical instruments based on different principles

12

Unit – IV: Data Acquisition Methods:

Introduction to Medical Imagining equipment, Characteristics , generation and application of x-ray, Ultrasound and its applications in medical instrumentation, Computer tomography, magnetic resonance imaging, Defibrillator Machine, blood cell counter, blood gas analyzer

12


Total No. of Lectures

48

Books*/

References


1.    R S Khandpur, Hand Book of Bio-Medical instrumentation, Tata McGraw Hill, 2003.

2.    L Cromwell, F J Weibell, E A Pfeiffer, Biomedical Instrumentation and Measurements, 2nd edition, Pearson Education India, 2015.

3.    J. J. Carr, J M Brown, Introduction to Biomedical Equipment Technology, 4th edition, Pearson Education, 2002

4.    J G Webster, Medical Instrumentation, John Wiley & Sons, 2005

5.    NPTEL lectures/notes and MIT open courseware

6.    Relevant journals/ Magazines / Transaction papers.

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations

15 Marks

Mid Term Examination

25 Marks

Sessional Total

40 Marks

End Semester Examination

60 Marks

Total

100 Marks




Course Title

Data Acquisition and Analysis

Course number

BME6140

Credits

4

Course Category

PE (Program Elective)

Pre-requisite

-

Contact Hours (L-G-P)

3-1-0

Type of Course

Theory

Course Objectives

This course aims to develop in the student the ability to evaluate, the most appropriate strategy for acquiring data. Also to develop the understanding of a range of modern time and frequency domain analysis techniques as well as advanced applications such as data fusion.

Course

Outcomes

At the end of the course the students should have

1.      Basic knowledge of LabVIEW and its use for data acquisition and analysis.

2.      The ability to design and implement a data acquisition solution to a problem.

3.      The ability to apply data analysis techniques to the data in both time and frequency domains.

Syllabus


Lecture

Unit-I: Virtual Instrumentation:

Basics of virtual instrumentation. Introduction to LABVIEW: Loops, Data structures, Arrays and Clusters, Graphs and Charts, File Input/Output, Developing VI and sub VIs for data analysis and presentation.


10

Unit-II: Data Acquisition

Sensors, Data acquisition, Signal conditioning and processing techniques, analogue-to-digital converters, Sampling,   Development of acquisition and display systems using the DAQ Assistant to Generate LabVIEW Code.

10

Unit-III: Data Analysis - I

10

Data pre-processing, digital filtering, Data Visualisation (2-D and 3-D), Statistical properties of signals, time and frequency domain, convolution and correlation, Spectral Analysis

Unit-IV:  Data Analysis - II

Curve fitting and data modelling, interpolation and extrapolation, non-stationary signal analysis, Development of analysis systems of biomedical signals.

10

Content beyond syllabus:

Data analysis using Python/Matlab.

08


Total No. of Lectures

48

Books*/

References


1.    B Mihura*, LabVIEW for Data Acquisition, Prentice Hall of India, 2013.

2.    R M. Rangayyan*, Biomedical Signal Analysis, second edition,  IEEE Press, USA, 2015.

3.    J G Webster, Medical Instrumentation: Application and Design, 4th edition, John Wiley & Sons, 2009.

4.    Labview for students, https://www.ni.com/en-in/shop/labview/select-edition/labview-student-edition.html.

5.    Python for informatics by Charles Severance (available online).

6.    Python for Everybody, Exploring Data Using Python 3 by Charles R. Severance (available online)

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations

15 Marks

Mid Term Examination

25 Marks

Sessional Total

40 Marks

End Semester Examination

60 Marks

Total

100 Marks


Course Title

Machine Learning

Course number

BME6330

Credits

4

Course Category

PE (Program Elective)

Course Pre-requisite

-

Contact Hours (L-G-P)

3-1-0

Type of Course

Theory

Course Objectives

This course aims at providing the necessary basic concepts in machine learning with an understanding to the supervised and unsupervised learning methods.

Course

Outcomes

After completing this course, students should be able to:

  1. Develop an understanding of the basic concepts of Machine Learning.

  2. Understand a wide variety of learning algorithms.

  3. Use recent machine learning approaches and software for solving practical problems. Understand how to evaluate models generated from data.

  4. Apply algorithms and perform independent study and research in machine learning.

Syllabus


Lecture


Unit –I: Introduction

Basic concepts, Machine Learning languages, types, and examples, Challenges and issues in Machine learning, Concept Learning, Decision Tree Learning,

12


Unit –II: Supervised and Unsupervised Learning

Supervised vs. Unsupervised Learning; Artificial Neural Network: Multilayer Network, Back propagation Algorithms. Evaluating Hypothesis. Bayesian Learning: theorem, Concept, Naïve Bayes Classifier, Bayesian Belief Networks, Gradient Descent and Support Vector Machines,

12


Unit –III: Learning Theory

Computational Learning Theory, instance-Based Learning; K-Nearest Neighbour, Genetic Algorithms. Learning Set of Rules, Analytical Learning,

12


Unit –IV: Reinforcement Learning

Introduction to Reinforcement Learning: Reinforcement Learning Basics, Probabilistic inference, Evolutionary Algorithms, Current problems in machine learning: Application to Information Retrieval, NLP

12


Total No. of Lectures

48

Books*/

References


  1. Machine Learning, Tom Mitchell, McGraw Hill, 1997

  2. Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006

  3. Machine Learning in Python Essential Techniques for Predictive Analysis, Michael Bowles, Wiley Publications, 2015

  4. Introduction to Machine Learning, Alex Smola and S.V.N. Vishwanathan, Cambridge University Press, 2008

  5. Relevant journals/ Magazines / Transaction papers.

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations (3 to 4)

15 Marks

Mid Term Examination (1 Hour)

25 Marks

Sessional Total

40 Marks

End Semester Examination (2 Hours)

60 Marks

Total

100 Marks



Course Title

Artificial Intelligence and Neural Networks

Course number

BME6310

Credits

4

Course Category

PE (Program Elective)

Pre-requisite

-

Contact Hours (L-T-P)

3-1-0

Type of Course

Theory

Course Objectives

To introduce the basic concepts of Artificial Intelligence and Neural Networks with illustrations of current applications.

Course

Outcomes

At the end of the course the students will be able to

  1. Understand the concept of Artificial Intelligence, Search techniques and Knowledge representation concerns.
  2. Build awareness of AI facing major challenges and the complexity of typical problems within the field
  3. Understand the fundamentals of ANN, its need, advantages and limitations.
  4. Is able to learn methods to solve problems using ANN.

Syllabus


Lecture

Unit I: Introduction

Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Intelligent Agents, Introduction to Search, Search strategies

12

Unit II: Knowledge Representation

Knowledge Representation and Reasoning: Propositional logic, Forward chaining,  Backward chaining, Resolution, Probabilistic reasoning

12

Unit III: Fundamentals of Artificial Neural Network

Functional anatomy of Neuron, Models of a Neuron, Learning rules, Supervised and Unsupervised Learning, Single Layer Perceptrons, LMS algorithms. Perceptron, convergence theorem, XOR problem,

12

Unit IV: Feedforward network and Design

Multilayer Perceptrons, Back Propagation Algorithm; Design issues: Pre-processing, Structure of networks, Training, Validation and Testing the prototype; Applications of Artificial Neural Networks

12


Total No. of Lectures

48

Books*/

References


1.    *Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd edition, Prentice Hall of India, 2004.

2.    Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 3rd edition, Pearson Education, 2020

3.    Nils J. Nilsson, Artificial Intelligence: A new synthesis, Harcourt Asia PTE, 1998.

4.    *Simon Haykin, Neural Networks: A Comprehensive Foundation, Pearson Education, 1997

5.    Satish Kumar, Neural Networks: A Classroom Approach, 2nd edition, Tata McGraw Hill Education, 2017.

Course Assessment/

Evaluation/

Grading Policy

Sessional

Assignments / Quiz / Presentations

15 Marks

Mid Term Examination

25 Marks

Sessional Total

40 Marks

End Semester Examination

60 Marks

Total

100 Marks