Course detail

Advanced Analysis of Biological Signals

FEKT-MPA-ACSAcad. year: 2022/2023

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.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Offered to foreign students

The home faculty only

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.

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)

Course curriculum

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.

Work placements

Not applicable.


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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Proakis,J.G., Manolakis,D.G.: Digital Signal Processing. Principles, Algorithms and Applications. Macmillan, 1992 (EN)

Recommended reading

Not applicable.


Classification of course in study plans

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

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer