Course detail
Advanced Analysis of Biological Signals
FEKT-FACSAcad. year: 2018/2019
The course is oriented to multirate signal processing, time-frequency analysis focused particularly on the different types of wavelet transform, S-transform and empirical mode decomposition.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- implement the sampling rate conversion
- explain the principles and advantages of multirate filtering
- implement of the various types of wavelet transforms
- explain the principles of filtering and data compression based on wavelet transform
- explain the principles of Stockwell transform,
- explain the principles of empirical mode decompsition and Hilbert-Huang transform
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- 60 points can be obtained for the written exam
Course curriculum
2. Design of multirate filters
3. Time-frequency analysis, continuous-time wavelet transform (CTWT)
4. Discrete-time wavelet transform (DTWT), dyadic and packet DTWT
5. Use of CTWT in analysis of biosignals
6. Use of DTWT in compression of biosignals
7. Shift-invariant DTWT and filtering of biosignals,
8. Stockwell transform (S-transform), theory and use
9. Empirical mode decomposition (EMD), principle and use
10. Complex signals, Hilbert transform, Hilbert-Huang transform
11. Signal envelope and instantaneous signal frequency, their estimates
12. Mobile phone applications
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Proakis,J.G., Manolakis,D.G.: Digital Signal Processing. Principles, Algorithms and Applications. Macmillan, 1992 (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Design of multirate filters
3. Time-frequency analysis, continuous-time wavelet transform (CTWT)
4. Discrete-time wavelet transform (DTWT), dyadic and packet DTWT
5. Use of CTWT in analysis of biosignals
6. Use of DTWT in compression of biosignals
7. Shift-invariant DTWT and filtering of biosignals,
8. Stockwell transform (S-transform), theory and use
9. Empirical mode decomposition (EMD), principle and use
10. Complex signals, Hilbert transform, Hilbert-Huang transform
11. Signal envelope and instantaneous signal frequency, their estimates
12. Mobile phone applications
Exercise in computer lab
Teacher / Lecturer
Syllabus
Implementations of sampling rate conversion
Implementations of multirate filters
Wavelet transform in Matlab, Wavelet Toolbox
DTWT and signal compression
Shift-invariant DTWT and signal filtering
CWT for signal analysis
Mobile phone applications
Solution of individual projects