Biomedical signal processing syllabus
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This document outlines the syllabus for the course EES6004-Biomedical Signal Processing. The course will cover topics related to biomedical signals including anatomy, physiology, signal acquisition, analysis in time and frequency domains, modeling, classification, and applications. Assessment will include mid and end semester exams worth 30% and 50% respectively, along with a 15% project and 5% quizzes. Lectures will be led by Dr. M. Sabarimalai Manikandan and Dr. Debi Prosad Dogra and will cover various biomedical signals and processing techniques. MATLAB will be used for programming assignments.
![• Affective Computing
• Assistive Technology
• Event Change Detection
• Augmented Reality Medical Systems
Reference Textbooks
[1]. Rangaraj M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, Publisher: Wiley India;
Publishing Date: 2009
[2]. Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling, Wiley-Interscience; 1 edition
(November 20, 2000)
[3]. John L. Semmlow, Biosignal and biomedical image processing: MATLAB-based applications, CRC; 1
edition, 2004.
[4]. Metin Akay, Time Frequency and Wavelets in Biomedical Signal Processing, Wiley-IEEE Press; 1
edition (October 24, 1997)
[5]. Stephane Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, third edition, Academic
Press
[6]. Monson H Hayes, Statistical Digital Signal Processing And Modeling, Wiley
[7]. J. Proakis and D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th
edition, Prentice-Hall.
[8]. Li Tan, Digital Signal Processing: Fundamentals and Applications, Elsevier, 2008.
[9]. Mrinal Mandal, Amir Asif, Continuous and Discrete Time Signals and Systems, Cambridge University
Press, 2008.
[10]. Instructor's Notes](https://image.slidesharecdn.com/biomedicalsignalprocessingsyllabus-140328033146-phpapp01/85/Biomedical-signal-processing-syllabus-7-320.jpg)
Biomedical signal processing syllabus
- 1. EES6004-Biomedical Signal Processing Teachers: Dr. M. Sabarimalai Manikandan and Dr. Debi Prosad Dogra Course Prerequisites: Digital Signal Processing and MATLAB Programming Grading Plan: • Mid-Semester Examination (30%): o Question Pattern: Individual Term Project (10%) + Course Topics (20%) • End-Semester Examination (50%): o Question Pattern: Individual Term Project (20%) + Course Topics (30%) • Project Evaluation (15%): Final MATLAB code, final spec, final README and technical report explaining how your project works and what you did and did not get finished. • Quizzes (5%): Expect to show what you know on at least one quiz each month.
- 2. Course Syllabus Lecture Number Course Topics Instructor Lecture 01 • Objectives of Biomedical Signal Processing • Research Challenges in Physiological Signal Monitoring and Diagnosis • Recent Trends in Healthcare Monitoring and Diagnostic Systems MSM/DPD Lecture 01 • Human Anatomy and Physiology • Physiological Systems • Different types of biomedical signals DPD Lecture 02 Membrane Cell Physiology • Membrane Structure and Skeletal muscle activity • Generation and transmission of bioelectricity in excitable cells; ionic transport in cellular membranes; propagation of electricity within and between cells; cardiac and neural physiology; measurement of extracellular fields; electrical stimulation of excitable cells. DPD
- 3. • Ionic permeability and membrane potential – Nernst & Goldman equations • Muscle Action potential – voltage dependent Na+ channel, activation and inactivation, and propagation • Depolarization, repolarization and Plateau phase in Action potential • Nervous Systems Lecture 03 • Electrocardiography DPD Lecture 04 • Phonocardiography • Electrocorticography • Abdominal Electrocardiography DPD Lecture 05 • Electroencephalography • Magnetoencephalography • Electroretinography • Electrooculography DPD Lecture 06 • Respiratory Sounds DPD Lecture 07 • Electrogastrography • Electromyography • Mechanomyography DPD
- 4. • Vibroarthrography MID-SEMESTER EXAMINATION Lecture 08 Biosignal Recording System • Spectral Characteristics of Biosignals • Biosensors and Analog Frond-End for Signal Acquisition • Sampling, Quantization and Encoding • Multi-rate Systems • Compressed Sensing • Lossless Data Compression MSM Lecture 09 Biosignal Signal Analysis • Time-Domain Analysis of Biosignals • Statistical Analysis of biosignals: HOS, SVD, PCA and ICA • Information-theoretic Analysis MSM Lecture 10 Time and Frequency Domain Analysis • Fourier Spectrum of Biosignals • Short-time Fourier Transform and Spectrogram • Discrete Cosine Transform (DCT) and its Applications MSM
- 5. • Wavelet Transform and Time-Frequency Analysis • Hilbert Transform and its Application • Empirical Mode Decomposition and Empirical Wavelet Transform • Correlation Analysis and Power Spectral Estimation Lecture 11 Digital Filters for Biosignal Applications • Illustration of different types of artifacts and Noise • Time Domain Filters • Frequency Domain Filters • Notch and Comb Filters • Optimal Filtering • Adaptive Filters • Signal Decomposition based Filtering MSM Lecture 12 Event Detection and Feature Extraction Techniques • Signal Segmentation • Envelope Extraction and Analysis • Temporal and Spectral Features • Statistical Features • Information-Theoretic Features MSM
- 6. • Cross-spectral Features • Waveform Complexity Lecture 13 Modeling Biomedical Systems • Autoregressive or All-pole Modeling • Pole-zero Modeling • Spectral Modeling Pattern Classification and Diagnostic Decision • Supervised and Unsupervised Classification • Probabilistic Models and Statistical Decision Performance Measures for Detection and Classification System MSM Lecture 14 Overview of Biosignal Processing Systems • Heart Rate Variability Analysis • Computer Aided Auscultation • Sleep Apnea Monitoring • ECG Wave Delineation • ECG Beat Classification • Brainwave Classification • Epileptic Seizure Detection MSM
- 7. • Affective Computing • Assistive Technology • Event Change Detection • Augmented Reality Medical Systems Reference Textbooks [1]. Rangaraj M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, Publisher: Wiley India; Publishing Date: 2009 [2]. Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling, Wiley-Interscience; 1 edition (November 20, 2000) [3]. John L. Semmlow, Biosignal and biomedical image processing: MATLAB-based applications, CRC; 1 edition, 2004. [4]. Metin Akay, Time Frequency and Wavelets in Biomedical Signal Processing, Wiley-IEEE Press; 1 edition (October 24, 1997) [5]. Stephane Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, third edition, Academic Press [6]. Monson H Hayes, Statistical Digital Signal Processing And Modeling, Wiley [7]. J. Proakis and D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th edition, Prentice-Hall. [8]. Li Tan, Digital Signal Processing: Fundamentals and Applications, Elsevier, 2008. [9]. Mrinal Mandal, Amir Asif, Continuous and Discrete Time Signals and Systems, Cambridge University Press, 2008. [10]. Instructor's Notes
- 8. Programming Language: MATLAB Journals in the Field of Biomedical Engineering International Conferences in the Field of Biomedical Engineering • IEEE Transactions on Biomedical Engineering • IEEE Journal on Biomedical and Health Informatics • IEEE Transactions on Neural Systems and Rehabilitation Engineering • IEEE Signal Processing and IEEE Signal Processing Letters • Electronics Letters • Healthcare Technology Letters • Signal Processing (Elsevier) • Digital Signal Processing (Elsevier) • Biosignal Processing and Control(Elsevier) • Medical Engineering Physics (Elsevier) • Computers in Biology and Medicine (Elsevier) • Annals of Biomedical Engineering (Springer) • Cardiovascular Engineering and technology (Springer) • Medical & Biological Engineering & Computing (Springer) • Medical System (Springer) • Annual International Conference of the IEEE Engineering in Medicine and Biology Society • IEEE-EMBS International Conference on Biomedical and Health Informatics • Computing in Cardiology Biomedical Engineering Society • IEEE Engineering in Medicine and Biology Society (IEEE-EMBS) • Biomedical Engineering Society (BMES)