Video Lectures on "Biomedical Signal Analysis" by Prof. Rangaraj M. Rangayyan

Lecture Videos:

 Biomedical Signal Analysis 

by Prof. Rangaraj M. Rangayyan:

 

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1	1. Introduction to biomedical signals 1.1 The nature of biomedical signals
2	1.1 The nature of biomedical signals (cont.) 1.2 Examples of biomedical signals      1.2.1 The action potential      1.2.2 The electroneurogram
3	1.2.3 The electromyogram
4	1.2.3 The electromyogram (cont.)      1.2.4 The electrocardiogram
5	1.2.8 The phonocardiogram
6	1.2.8 The phonocardiogram (cont.)      1.2.9 The carotid pulse      1.2.10 Signals from catheter-tip sensors      1.2.11 The speech signals      1.2.12 The vibromyogram      1.2.13 The vibroarthrogram      1.2.14 Oto-acoustic emission signals
7	1.3 Objectives of biomedical signal analysis 1.4 Difficulties in biomedical signal acquisition and analysis 1.5 Computer-aided diagnosis: CAD. 2. Analysis of concurrent, coupled, and correlated processes 2.1 Illustration of the problem with case-studies      2.1.1 The electrocardiogram and the phonocardiogram      2.1.2 The phonocardiogram and the carotid pulse      2.1.3 The ECG and the atrial electrogram      2.1.4 Cardio-respiratory interaction      2.1.5 The electromyogram and the vibromyogram      2.2.6 The knee-joint and muscle vibration signals 2.2 Application: Segmentation of the PCG
8	3. Filtering for removal of artifacts 3.1 Problem statement      3.1.1 Random noise, structured noise, and physiological interference
9	3.1.2 Stationary versus nonstationary processes 3.2 Illustration of the problem with the case-studies      3.2.1 Noise in event-related potentials      3.2.2 High-frequency noise in the ECG      3.2.3 Motion artifact in the ECG      3.2.4 Power-line interferences in ECG signals      3.2.5 Maternal interference in fetal ECG      3.2.6 Muscle-contraction interference in VAG signals      3.2.7 Potential solutions to the problem 3.3 Time-domain filters      3.3.1 Synchronized averaging
10	3.3.2 Moving-average filters      3.3.3 Derivative-based operators to remove low-frequency artifacts
11	3.3.3 Derivative-based operators to remove low-frequency artifacts (cont.) 3.4 Frequency-domain filters      3.4.1 Removal of high-frequency noise: Butterworth lowpass filters
12	3.4.1 Removal of high-frequency noise: Butterworth lowpass filters (cont.)      3.4.2 Removal of low-frequency noise: Butterworth highpass filters
13	3.4.3 Removal of periodic artifacts: Notch and comb filters 3.5 Optimal filtering: the Wiener filter
14	3.6 Adaptive filters for removal of interference      3.6.1 The adaptive noise canceler;      3.6.2 The least-mean-squares adaptive filter. 3.7 Selecting an appropriate filter. 3.8 Application: Removal of artifacts in the ECG. 3.9 Application: Maternal – Fetal ECG.
15	4. Event detection 4.1 Problem statement 4.2 Illustration of the problem with case-studies      4.2.1 The P, QRS, and T waves in the ECG      4.2.2 The first and second heart sounds      4.2.3 The dicrotic notch in the carotid pulse      4.2.4 EEG rhythms, waves, and transients 4.3 Detection of events and waves:      4.3.1 Derivative-based methods for QRS detection      4.3.2 The Pan-Tompkins algorithm for QRS detection
16	4.3.2 The Pan-Tompkins algorithm for QRS detection (cont.)
17	4.3.3 Detection of the dicrotic notch 4.4 Correlation analysis of EEG channels      4.4.1 Detection of EEG rhythms
18	4.4.2 Template matching for EEG spike-and-wave detection 4.5 Cross-spectral techniques      4.5.1 Coherence analysis of EEG channels 4.6 The matched filter      4.6.1 Detection of EEG spike-and-wave complexes
19	4.7 Detection of the P waves
20	4.8 Homomorphing filtering and the complex cepstrum 4.9 Application: ECG rhythm analysis 4.10 Application: Identification of heart sounds 4.11 Application: Detection of the aortic components of S2
21	5. Analysis of waveshape and waveform complexity 5.1 Problem statement 5.2 Illustration of the problem with case-studies      5.2.1 The QRS complex in the case of bundle-branch block      5.2.2 The effect of myocardial ischemia and infarction on QRS waveshape      5.2.3 Ectopic beats      5.2.4 EMG interference pattern complexity      5.2.5 PCG intensity patterns 5.3 Analysis of event-related potentials 5.4 Morphological analysis of ECG waves      5.4.1 Correlation coefficient      5.4.3 ECG waveform analysis 5.5 Envelope extraction and analysis      5.5.1 Amplitude demodulation      5.5.2 Synchronized averaging of PCG envelopes      5.5.3 The envelogram
22	5.6 Analysis of activity      5.6.1 The root mean-squared value      5.6.2 Zero-crossing rate      5.6.3 Turns count      5.6.4 Form factor 5.7 Application: Parameterization of normal and ectopic ECG beats
23	5.8 Application: Analysis of exercise ECG 5.9 Application: Analysis of respiration 5.10 Application: electrical and mechanical correlates of muscular contraction
24	6. Frequency-domain characterization of signals and systems 6.1 Problem statement 6.2 Illustration of the problem with case-studies      6.2.1 The effect of myocardial elasticity on heart sound spectra      6.2.2 Frequency analysis of murmurs to diagnose valvular defects
25	6.3 The Fourier spectrum
26	6.4 Estimation of the power spectral density function      6.4.1 The periodogram      6.4.2 The need for averaging      6.4.3 The use of windows: spectral resolution and leakage
27	6.4.3 The use of windows: spectral resolution and leakage (cont.)      6.4.4 Estimation of the autocorrelation function.
28	6.4.5 Synchronized averaging of PCG spectra. 6.5 Measures derived from PSDs      6.5.1 Moments of PSD functions      6.5.2 Spectral power ratios 6.6 Application: Evaluation of prosthetic valves
	Videos from the following chapters are not available: 7. Modeling biomedical systems 8. Analysis of nonstationary signals 9. Pattern classification and diagnostic decision