MX9312 BIOSIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME MEDICAL ELECTRONICS 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIRST SEMESTER ME MEDICAL ELECTRONICS DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI, TIRUNELVELI,COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
MX9312 BIOSIGNAL PROCESSING L T P C
3 0 0 3
UNIT I SIGNAL, SYSTEM AND SPECTRUM 9
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 9
Time series analysis – linear prediction models, process order estimation, lattice
representation, non stationary process, fixed segmentation, adaptive segmentation,
application in EEG, PCG signals, Time varying analysis of Heart-rate variability,
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 9
Filtering – LMS adaptive filter, adaptive noise canceling in ECG, improved adaptive
filtering in FECG, Wavelet detection in ECG – structural features, matched filtering,
adaptive wavelet detection, detection of overlapping wavelets.
5
UNIT IV BIOSIGNAL CLASSIFICATION AND RECOGNITION 9
Signal classification and recognition – Statistical signal classification, linear
discriminate function, direct feature selection and ordering, Back propagation neural
network based classification. Application in Normal versus Ectopic ECG beats.
UNIT V TIME FREQUENCY AND MULTIVARIATE ANALYSIS 9
Time frequency representation, spectrogram, Wigner distribution, Time-scale
representation, scalogram, wavelet analysis – Data reduction techniques, ECG data
compression, ECG characterization, Feature extraction- Wavelet packets,
Multivariate component analysis-PCA,ICA
L = 45 TOTAL: 45 PERIODS
REFERNCES
1. Arnon Cohen, Bio-Medical Signal Processing Vol I and Vol II, CRC Press Inc.,
Boca Rato, Florida 1999.
2. Rangaraj M. Rangayyan, ‘Biomedical Signal Analysis-A case study approach’,
Wiley- Interscience/IEEE Press, 2002
3. Willis J. Tompkins, Biomedical Digital Signal Processing, Prentice Hall of India,
New Delhi, 2003.
4. Emmanuel C. Ifeachor, Barrie W.Jervis, ‘Digital Signal processing- A Practical
Approach’ Pearson education Ltd., 2002
5. Raghuveer M. Rao and Ajith S.Bopardikar, Wavelets transform – Introduction to
theory and its applications, Pearson Education, India 2000.
6. K.P.Soman,K.I Ramachandran,”Insight into wavelet from theory to practice”, PHI,
New Delhi,2004
7. John L.Semmlow,” Biosignal and Biomedical Image Processing Matlab Based
applications” Marcel Dekker Inc.,New York,2004
8. Kavyan Najarian and Robert Splerstor,” Biomedical signals and Image
processing”, CRC – Taylor and Francis,New York,2006
9. D.C.Reddy,”Biomedical Signal Processing – Principles and
Techniques”,TMH,New Delhi,2005
10. Gari D.Clifford,Francisco Azuaje and Patrick E.McSharry,” Advanced Methods
and Tech for ECG Data Analysis”, ARTECH House,Boston,2006.
MX9312 BIOSIGNAL PROCESSING L T P C
3 0 0 3
UNIT I SIGNAL, SYSTEM AND SPECTRUM 9
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 9
Time series analysis – linear prediction models, process order estimation, lattice
representation, non stationary process, fixed segmentation, adaptive segmentation,
application in EEG, PCG signals, Time varying analysis of Heart-rate variability,
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 9
Filtering – LMS adaptive filter, adaptive noise canceling in ECG, improved adaptive
filtering in FECG, Wavelet detection in ECG – structural features, matched filtering,
adaptive wavelet detection, detection of overlapping wavelets.
5
UNIT IV BIOSIGNAL CLASSIFICATION AND RECOGNITION 9
Signal classification and recognition – Statistical signal classification, linear
discriminate function, direct feature selection and ordering, Back propagation neural
network based classification. Application in Normal versus Ectopic ECG beats.
UNIT V TIME FREQUENCY AND MULTIVARIATE ANALYSIS 9
Time frequency representation, spectrogram, Wigner distribution, Time-scale
representation, scalogram, wavelet analysis – Data reduction techniques, ECG data
compression, ECG characterization, Feature extraction- Wavelet packets,
Multivariate component analysis-PCA,ICA
L = 45 TOTAL: 45 PERIODS
REFERNCES
1. Arnon Cohen, Bio-Medical Signal Processing Vol I and Vol II, CRC Press Inc.,
Boca Rato, Florida 1999.
2. Rangaraj M. Rangayyan, ‘Biomedical Signal Analysis-A case study approach’,
Wiley- Interscience/IEEE Press, 2002
3. Willis J. Tompkins, Biomedical Digital Signal Processing, Prentice Hall of India,
New Delhi, 2003.
4. Emmanuel C. Ifeachor, Barrie W.Jervis, ‘Digital Signal processing- A Practical
Approach’ Pearson education Ltd., 2002
5. Raghuveer M. Rao and Ajith S.Bopardikar, Wavelets transform – Introduction to
theory and its applications, Pearson Education, India 2000.
6. K.P.Soman,K.I Ramachandran,”Insight into wavelet from theory to practice”, PHI,
New Delhi,2004
7. John L.Semmlow,” Biosignal and Biomedical Image Processing Matlab Based
applications” Marcel Dekker Inc.,New York,2004
8. Kavyan Najarian and Robert Splerstor,” Biomedical signals and Image
processing”, CRC – Taylor and Francis,New York,2006
9. D.C.Reddy,”Biomedical Signal Processing – Principles and
Techniques”,TMH,New Delhi,2005
10. Gari D.Clifford,Francisco Azuaje and Patrick E.McSharry,” Advanced Methods
and Tech for ECG Data Analysis”, ARTECH House,Boston,2006.
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