जैव-चिकित्सा इंजीनियरिंग केंद्र
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): |
|
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 |
||
|
|
||
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 |
2. B D Chaurasia, Human Anatomy , 7th edition, CBS publisher, 2017
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
|
||||
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:
|
||||
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 |
|
||||
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
|
||||
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 |
![photo](https://api.amu.ac.in/storage/images/empphoto/3111.jpg)