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13M054MAS - Advanced Methods of Biosignal Analysis

Course specification
Course title Advanced Methods of Biosignal Analysis
Acronym 13M054MAS
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition None.
The goal Applying modern methods of physiological signal analysis for application in diagnostics and automatic diagnosis.
The outcome The ability to apply modern computational methods for the analysis physiological signals in research and in clinical conditions, as well as signal processing with the aim of diagnostic and automatic diagnosis.
Contents
Contents of lectures Dynamic characteristics of physiological signals with an emphasis on electrophysiological (EF) signals. Application of Fourier's transformation in the analysis of EF signals. Correlation, cross-correlation, and auto-correlation. Digital filters and window functions. Adaptive Filters. Model and state estimation. Time series analysis. Application of selected methods for EF signal analysis.
Contents of exercises Computer-based analysis of biological signals.
Literature
  1. Dunn S. M., Constantinides A., Moghe P. V. "Numerical methods in biomedical engineering", Elsevier, 2006. (Original title)
  2. Cohen A., "Biomedical signal processing", Vol. I and II, CRC Press, 2000. (Original title)
  3. Rangayyan R. M. "Biomedical signal analysis", John Wiley & Sons, 2015. (Original title)
  4. Biosignal analysis with practical examples in the R programming language
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching Lectures, supervised exercises, exercises, and application of methods on real-life signals.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 50
Practical lessons 0 Oral examination 0
Projects
Colloquia 20
Seminars 30