Fundamentals of Bio-signal Processing

Objectives

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

Outcomes

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

Contents

  1. Introduction to signal processing

    • Review of signals, LTI systems, Convolution, Correlation

  2. Review of probability theory

    • Foundations, Bayes law, expectations.

  3. Discrete Fourier transforms and time-frequency analysis

    • Discrete time Fourier transform, Discrete Fourier transform, STFT

  4. Digital Filters for Biosignals

    • FIR & IIR filters, filter design, and realizations, applications to biosignals

  5. Biosignal modeling

    • Bio-signal strength modeling, time series models, AR and ARMA models

  6. Biosignal Classification methods

    • Fundamentals, linear models, feature extraction, classic ML and DL methods


Materials

  • 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.