Course detail

Advanced Analysis of Biological Signals

FEKT-MPC-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.


Not applicable.

Recommended optional programme components

Not applicable.

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

- 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


Work placements

Not applicable.

Course curriculum

1. Sampling rate conversion
2. Design of multirate filters
3. Time-frequency analysis, wavelet transforms (CTWT, DTWT)
4. Use of DTWT in compression and for filtering and analysis of biosignals
5. Adaptive filters
6. Spectral analysis of biosignals and parametric methods for power spectrum estimation
7. Stockwell transform (S-transform), theory and use
8. Empirical mode decomposition (EMD), principle and use
9. Complex signals, Hilbert transform, Hilbert-Huang transform
10. Signal envelope and instantaneous signal frequency, their estimates
11. Multiplicative modulation, SSB modulation
12. Linear deconvolution
13. Nonlinear filtering: median filtering and homomorphic filtering
14. Mobile phone applications


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.

Classification of course in study plans

  • Programme MPC-BTB Master's, 1. year of study, winter semester, 5 credits, compulsory
  • Programme MPC-BIO Master's, 2. year of study, winter semester, 5 credits, compulsory

Type of course unit


Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer