21EC734 Biomedical Signal Processing
Course Learning Objectives
● Possess the basic mathematical, scientific and computational skills necessary to analyse ECG and EEG signals.
● Apply classical and modern filtering and compression techniques for ECG and EEG signals.
● Develop a thorough understanding on basics of ECG and EEG feature extraction.
SYLLABUS COPY
MODULE - 1
Introduction to Biomedical Signals
The nature of Biomedical Signals, Examples of Biomedical Signals, Objectives of Biomedical Signal analysis, Difficulties in Biomedical Signal analysis.
Electrocardiography
Techniques used in electrocardiography, ECG Electrodes, the cardiac equivalent generator, genesis of the ECG, the standard and augmented limb leads, 12 lead ECG, the vectorcardiogram, ECG signal characteristics
Signal Conversion
Simple signal conversion systems, Conversion requirements for biomedical signals, Signal converter characteristics, D to A converters, A to D converters, Sample and Hold circuit, Analog Multiplexer, Amplifiers
MODULE - 2
Signal Averaging
Basics of signal averaging, Signal averaging as a digital filter, a typical averager, Software for signal averaging, Limitations of signal averaging.
Adaptive Filters
Principal noise canceller model, 60-Hz adaptive cancelling using a sine wave model, Applications: Maternal ECG in fetal ECG, Cardiogenic artifact, detection of ventricular fibrillation and tachycardia.
MODULE - 3
Data Reduction Techniques
Introduction, Turning point algorithm, AZTEC algorithm, Fano algorithm, Huffman coding: Static coding, Modified coding, Adaptive coding, Residual differencing, Runleng th coding.
Time and Frequency domain techniques
The Fourier transform for a discrete nonperiodic and periodic signals, the Fast Fourier transform, Correlation in time domain and in frequ ency domain, Convolution in time domain and in frequency domain, Power spectrum estimation: Parseval’s theorem
MODULE - 4
ECG QRS detection
Power spectrum of the ECG, Bandpass filtering techniques, Differentiation techniques, Template matching techniques: Template cross correlation, template subtraction, automata based template matching, a QRS detection algorithm.
ECG Analysis Systems
Interpretation of the 12 lead ECG, ST segment analyzer, Portable arrhythmia monitor: Holter recording, software and hardware design, arrhythmia analysis
MODULE - 5
Neurological signal processing
The brain and its potentials, origin of brain waves, the EEG signal and its characteristics, EEG analysis, Linear prediction theory, The Autoregressive method, Recursive estimation of AR parameters, Spectral error measure.
Event detection and waveform analysis
EEG rhythms, waves and transients, Detection of EEG rhythms, Template matching for EEG spike and wave detection, the matched filter
Course outcome
1. Describe the origin, properties and suitable models of important biological signals such as ECG and EEG.
2. Know the basic signal processing techniques in analysing biological signals.
3. Acquire mathematical and computational skills relevant to the field of biomedical signal processing.
4. Describe the basics of ECG signal compression algorithms.
5. Know the complexity of various biological phenomena.
Suggested Learning Resources
1. Biomedical Signal Analysis-Rangaraj M Rangayyan, John Wiley & Sons 2002
2. Biomedical Digital Signal Processing- Willis J Tompkins, PHI2001.
3. Biomedical Signal Processing Principles and Techniques-D C Reddy, McGraw-Hill publications, 2005.