Biomedical Signal Processing
Biomedical Signal Processing. Instructor: Prof. Sudipta Mukhopadhyay, Department of Electrical and Electronics Communication Engineering, IIT Kharagpur. This course is prepared for the engineering students in their final year of undergraduate studies or in their graduate studies. Electrical Engineering students with a good background in Signals and Systems are prepared to take this course. Students in other engineering disciplines, or in computer science, mathematics, geophysics or physics should also be able to follow this course. While a course in Digital Signal Processing would be useful, it is not necessary for a capable student. The course has followed problem solving approach as engineers are known as problem solvers. The entire course is presented in the form of series of problems and solutions.
(from nptel.ac.in
Lecture 06 - Electromyography, Electroneurogram, Event Related Potential, etc.
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Lecture 01 - Motivation, Human Body as a System
Lecture 02 - Preliminaries: Building Blocks, Human Cell, Action Potential
Biomedical Signal Origin and Dynamics
Lecture 03 - Cardiovascular System and Electrocardiogram
Lecture 04 - ECG Lead Configuration, ECG Unipolar Leads, Electroencephalogram
Lecture 05 - EEG Lead Position, Recording Configuration, and Applications
Lecture 06 - Electromyography, Electroneurogram, Event Related Potential, etc.
Removal of Artifacts
Lecture 07 - Introduction and Statistical Preliminaries
Lecture 08 - Case Studies, Time Domain Filtering: Sync Averaging
Lecture 09 - Time Domain Filtering: Moving Average, Integration Filter, Derivative based Filter
Lecture 10 - Time Domain Filtering: Improved Derivation based Filter, Frequency Domain Filtering
Lecture 11 - Optimal Filtering
Lecture 12 - Optimal Filtering (cont.)
Lecture 13 - Optimal Filtering (cont.)
Lecture 14 - Adaptive Filtering: Need and Basics of Adaptive Filtering
Lecture 15 - Least Mean Square Adaptive Filtering
Lecture 16 - Recursive Least Square Adaptive Filtering
Lecture 17 - Summary of the Artifact Removal Techniques
Event Detection
Lecture 18 - Example Events
Lecture 19 - QRS Wave Detection: 1st and 2nd Derivative Based Methods
Lecture 20 - QRS Wave Detection: Pan Tompkin Algorithm and Dicrotic Notch Detection
Lecture 21 - Case Study: EEG Signal Description
Lecture 22 - EEG Rhythm Detection: Cross Correlation Coefficient, Cross Spectral Density
Lecture 23 - EEG Rhythm Detection: Match Filter
Lecture 24 - Summary of the Event Detection
Homomorphic System
Lecture 25 - Multiplicative Homomorphic System
Lecture 26 - Homomorphic Deconvolutions
Waveform Analysis
Lecture 27 - Case Studies: Changes in ECG Waveform
Lecture 28 - Morphological Analysis of ECG Wave: Correlation Coefficient and Minimum Phase Correspondent
Lecture 29 - Minimum Phase Correspondent and Signal Length (cont.)
Lecture 30 - ECG Waveform Analysis
Lecture 31 - Envelop Extraction and Analysis
Lecture 32 - Analysis of Activity
Lecture 33 - Summary of Waveform Analysis
Frequency Domain Characterization
Lecture 34 - Motivation, Periodogram
Lecture 35 - Periodogram (cont.)
Lecture 36 - More on Properties of Periodogram
Lecture 37 - Averaged Periodogram, Blackman-Tukey Spectral Estimator
Lecture 38 - Blackman-Tukey Spectral Estimator (cont.)
Lecture 39 - Daniels Spectral Estimator, Summary of Periodogram
Modelling of Biomedical Systems
Lecture 40 - Modelling of Biomedical Systems: Motivation
Lecture 41 - Point Process
Lecture 42 - Parametric Model and AR Model Parameters
Lecture 43 - AR Model Parameter Estimation using ACF and Covariance Method
Lecture 44 - Spectral Matching, Modd Order Selection, Relation of AR Model and Cepstral Coefficients
Lecture 45 - Parameter Estimation of ARMA Model
Lecture 46 - Summary of the Chapter on Modelling of Biomedical Systems