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.
Learning outcomes of the course unit
The student is able to:
- 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
Students should have knowledge of digital signal processing, be familiar with the ways of describing the linear filters (transfer function, impulse response, difference equations, frequency response). We assume basic knowledge of students about the properties of biosignals (especially ECG, EEG, EMG). The laboratory work is expected knowledge of Matlab programming environment.
Recommended optional programme components
Recommended or required reading
Kozumplík, J.: Multitaktní systémy. Elektronická skripta FEKT VUT v Brně, 2005 (CS)
Proakis,J.G., Manolakis,D.G.: Digital Signal Processing. Principles, Algorithms and Applications. Macmillan, 1992 (EN)
Akay, M.: Detection and Estimation Methods for Biomedical Signals. Academic Press, 1996 (EN)
Planned learning activities and teaching methods
Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.
Assesment methods and criteria linked to learning outcomes
- 40 points can be obtained for activity in the laboratory exercises, consisting in solving tasks
- 60 points can be obtained for the written exam
Language of instruction
1. Sampling rate conversion
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
Gaining knowledge about multirate signal processing, wavelet transforms for processing and analysis of biosignals, Gaining knowledge about Stockwell transform, empirical mode decompsition (EMD), Hilbert-Huang transform and estimation of instantaneous frequency of signal.
Specification of controlled education, way of implementation and compensation for absences
Laboratory is compulsory, missed labs must be properly excused and can be replaced after agreement with the teacher.