Advanced Analysis of Biological Signals
FEKT-MPA-ACSAcad. year: 2020/2021
The course is oriented to multirate signal processing, time-frequency analysis focused particularly on the different types of wavelet transform, parametric methods for power spectrum estimation, principal component analysis (PCA) and data compression.
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 lossless data compression (Huffman encoder, arithmetic coder)
- explain the principles and possibilities of the use of PCA
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
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
- 30 points can be obtained for activity in the laboratory exercises, consisting in solving tasks (for the procedure for the examination must be obtained at least 15 points)
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)
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. Losless compresion of biosignals (Huffman and arithmetic coding).
7. Use of DTWT in compression of biosignals.
8. Redundant DTWT for filtering and analysis of biosignals.
9. Spectral analysis of biosignals and parametric methods for power spectrum estimation.
10. Linear prediction and Burg method for power spectrum estimation.
11. Principal component analysis (PCA) for filtering and compression of data .
12. Independent component analysis (ICA) for signal separation.
13. Linear deconvolution.
14. Median filtration, pyramidal median transformation.
15. Homomorphic filtration and cepstral analysis.
Gaining knowledge about multirate signal processing, wavelet transforms for processing and analysis of biosignals, principal component analysis (PCA), applications of PCA for analysis of biosignals and parametric methods for power spectrum estimation. Basic understanding of information theory, getting to know with the methods of lossless and lossy data compression.
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.