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 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 |
| References |
Biomedical Signal Processing
Instructor: Prof. Sudipta Mukhopadhyay, Department of Electrical and Electronics Communication Engineering, IIT Kharagpur. This course introduces the fundamental concepts of biomedical signal processing.
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