To give an improved understanding of signal processing principles, tools and algorithms for biosignals and systems
To provide a key set of tools and skills to students to enable them to apply signal analysis to solve practical biomedical problems
After completing this course, a student should be able to
Make a proper judgment on the classification of the biosignals and systems
Apply tools such as LTI systems and convolution for analysis real biosystems
Analyze the frequency content of biosignals
Be able to perform preprocessing of biosignals using appropriate digital filters
Model observed biosignals strength, time series and frequency content
Build classification models of activities or physiological conditions based on biosignals
Introduction to signal processing
Review of signals, LTI systems, Convolution, Correlation
Review of probability theory
Foundations, Bayes law, expectations.
Discrete Fourier transforms and time-frequency analysis
Discrete time Fourier transform, Discrete Fourier transform, STFT
Digital Filters for Biosignals
FIR & IIR filters, filter design, and realizations, applications to biosignals
Biosignal modeling
Bio-signal strength modeling, time series models, AR and ARMA models
Biosignal Classification methods
Fundamentals, linear models, feature extraction, classic ML and DL methods
Oppenheim, A. V., and R. W. Schafer, with J. R. Buck. Discrete-Time Signal Processing. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999. ISBN: 9780137549207.
Papoulis, A., and S. U. Pillai. Probability, Random Variables, and Stochastic Processes. New York, NY: McGraw Hill, 2001. ISBN: 9780072817256.
R. M. Rangayyan (2015). Biomedical Signal Analysis. Wiley, NY.
Willis J. Tompkins “ Biomedical Digital Signal Processing”, EEE, PHI, 2004
Steven M. Kay, "Modern spectral estimation theory and application ", Prentice Hall, Englewood Cliffs, NJ, 198
Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010). Introduction to pattern recognition: a matlab approach. Academic Press.