BM4151 Bio Signal Processing Syllabus:
BM4151 Bio Signal Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To introduce the characteristics of different biosignals
To discuss linear and non-linear filtering techniques to extract desired information
To demonstrate the significance of wavelet detection applied in biosignal processing.
To extract the features from the biosignal
To introduce techniques for automated classification and decision making to aid diagnosis
UNIT I SIGNAL, SYSTEM AND SPECTRUM
Characteristics of some dynamic biomedical signals, Noises- random, structured and physiological noises. Filters- IIR and FIR filters. Spectrum – power spectral density function, cross-spectral density and coherence function, cepstrum and homomorphic filtering. Estimation of mean of finite time signals.
UNIT II TIME SERIES ANALYSIS AND SPECTRAL ESTIMATION
Time series analysis – linear prediction models, process order estimation, non-stationary process, fixed segmentation, adaptive segmentation, application in EEG, PCG and HRV signals, model based ECG simulator. Spectral estimation – Blackman Tukey method, periodogram and model based estimation. Application in Heart rate variability, PCG signals.
UNIT III ADAPTIVE FILTERING AND WAVELET DETECTION
Filtering – LMS adaptive filter, adaptive noise cancelling in ECG, improved adaptive filtering in FECG, EEG and other applications in Bio signals, Wavelet detection in ECG – structural features, matched filtering, adaptive wavelet detection, detection of overlapping wavelets.
UNIT IV ANALYSIS OF BIOSIGNAL
Removal of artifact – ECG, Event detection –ECG, P Wave, QRS complex, T wave, Correlation analysis of ECG signals, Average of Signals-PCG, ECG and EMG.
UNIT V BIOSIGNAL CLASSIFICATION AND RECOGNITION
Statistical signal classification, linear discriminate function, direct feature selection and ordering, Back propagation neural network based classification.
Case study: 1. Various methods used to extract features from EEG signal
Case Study 2: Diagnosis and monitoring of sleep apnea
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to:
CO1: Analyse the different types of signals & systems
CO2: Analyse signals in time series domain & estimate the spectrum
CO3: Understand the significance of wavelet detection applied in biosignal processing
CO4: Extract the features from biosignal
CO5: Describe the performance of the classification of biosignals
TOTAL:45 PERIODS
REFERENCES:
1. P.Ramesh Babu, “Digital Signal Processing, Sixth Edition, Scitech publications, Chennai, 2014.
2. Raghuveer M. Rao and AjithS.Bopardikar, Wavelets transform – Introduction to theory and its applications, Pearson Education, India 2000
3. Rangaraj M. Rangayyan, 2nd edition “Biomedical Signal Analysis-A case study approach”, Wiley- Interscience /IEEE Press, 2015
4. Emmanuel C. Ifeachor, Barrie W.Jervis, second edition, “Digital Signal processing- A Practical Approach” Pearson education Ltd., 2002
5. Willis J.Tompkins, Biomedical Digital Signal Processing, Prentice Hall of India, New Delhi, 2006