Course detail

Biological Signal Analysis

FEKT-BPC-ABSAcad. year: 2023/2024

The course is focused on native and evoked biological signals (biosignals). It focuses on the characteristics of biosignals generated by the various systems of the human body (especially cardiovascular, nerve and muscle). The course is focused on methods for processing and analysis of biosignals in the time and frequency domain.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

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). Assuming knowledge of the discrete Fourier transform (DFT) and the ability to interpret the result DFT. The laboratory work is expected knowledge of Matlab programming environment.

Rules for evaluation and completion of the course

- 30 points can be obtained forproject (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)

Laboratory is compulsory, missed labs must be properly excused and can be replaced after agreement with the teacher.


The aim of the course is to familiarize students with the principles of the genesis of various types of biological signals, with their basic properties and methods of digital processing and automated analysis.
The student is able to:
- formulate requirements for filters for noise suppression in ECG signals, EEG, EMG
- design and implement adaptive filters for suppressing power hum in biosignals
- design and implement special filters Lynn type for narrowband interference suppression
- explain the principle of detection of QRS complexes in ECG signals and graphoelements in EEG signals
- describe the principle of detecting the beginning and end of major waves in the ECG signals
- explain the principles of stationarity tests of stochastic signals
- describe the principle of non-parametric and parametric methods for estimating power spectra
- describe the principle of cross-spectra and coherence spectra estimation and their use for analysis of EEG signals
- describe the principle of Poincare maps and their use for signal analysis (HRV, TWA)
- explain the principle of realization mapping for analysis of EEG signals
- explain the principle of continuous estimate the level of surface EMG signal

Study aids

Study materials are available on e-learning. 

Prerequisites and corequisites

Not applicable.

Basic literature

Kozumplík, J.: Analýza biologických signálů. Elektronická skripta FEKT VUT v Brně, Brno, 2013 (CS)

Recommended reading

Not applicable.


Classification of course in study plans

  • Programme BPC-BTB Bachelor's, 2. year of study, summer semester, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Basic classification of biosignals. Genesis of electrical biosignals, action potential and its spread.
2. Electrocardiogram (ECG), its properties and methods of scaning and display. Types of ECG signal processing requirements.
3. Preprocessing of ECG signals, linear and non-linear filters for suppressing interference.
4. Detectors QRS complexes. Delineation of ECG signals, rhythm and morphological analysis.
5. Analysis of heart rate variability (HRV) in the time and frequency domains.
6. Analysis of the T wave alternans (TWA). Analysis of the stress ECG. Phonocardiogram (PKG) and its analysis.
7. Elektrogastrogram (EGG) and its analysis.
8. Electroencephalogram (EEG signal). Analysis of EEG signals in the time domain.
9. Analysis of EEG signals in the frequency domain.
10. Evoked EEG signals, biosignals of visual and auditory systems.
11. Electromyogram (EMG signal), MUAP analysis and analysis of surface EMG signals.
12. Polygraphic biosignals.

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer


1. Design of linear filters for noise suppression in ECG signals
2. Design of Lynn filters to suppress drift and hum in ECG signals
3. Vectorcardigram (VCG), the heart axis slope in the frontal plane
4. Design of adaptive filters for hum suppression in ECG signals
5. Detection of the QRS complexes
6. The signals of heart rate variability (HRV)
7. Spectral analysis of HRV signals
8. Graphoelements Detection in EEG signals
9. Spectral analysis of EEG signals, cross spectrum, coherence spectrum
10. Hjorth parameters for analysis of EEG signals
11. Estimation of amplitude of surface EMG signal
12. Spectral analysis of EMG signals