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F7AMBBLS - Biological Signals

Code Completion Credits Range Language
F7AMBBLS ZK 3 2P English
Vladimír Krajča (guarantor)
Department of Biomedical Technology

The subject deals with origins and description of the most important electric and non-electric biological signals. The principles of generation, recording and basic properties are studied in all the signals. The studied signals involve native and evoked biosignals, including biological signals of the heart, brain, muscles, nervous system, auditory signals, visual system, signals from the gastro-intestinal system etc.


Requirements for credit:

Compulsory attendance at the exercises, max. 1 excused absence, handing in the prepared measurement protocols, a presentation on the selected topic (5-10 min, PowerPoint) and successful completion of the test at the end of the semester containing questions from practical measurements. Of the total course grade, practicals make up a maximum of 30%.

Course and evaluation of the exam:

The exam has a written and an oral part. The written part consists of a test in which questions will be asked and answered by the students. The test must be written with a minimum score of 50%. Some questions may carry more weight in the assessment than others, for example those involving a numerical problem or requiring a complex approach to solving. Successful completion of the tests is followed by an oral examination. If the results of the written part are sufficient for classification, the oral part of the examination may be waived. If the result of any part is graded F, the result of the examination is automatically F.

Syllabus of lectures:

1. Introduction to digital biosignal processing. Motivation. Basic characteristics of EEG, EKG, EOG, EMG. Basic graphoelements in EEG, polysomnography, hypnogram. Polysomnography. Artefacts.

2. Statistic and probabilistic signal properties. Probability distribution. Stochastic processes and time series analysis. Convolution, impulse characteristics. Mean, standard deviation, correlation analysis. Cross-correlation function. The nonstationary behaviour of EEG. Frequency bands.

3. Biological signals recording and preprocessing. Digital EEG devices. Basic sequence of signal transfer into computer. A/D converter, differential amplifiers. Analog and digital filters. Problems of sampling and quantization, Nyquist theorem and sampling frequency. Errors during signal conversion. Signal conditioning, aliasing in the time and frequency domains. Digital and frequency aliasing. Denoising a detrending. EEG machine calibration.

4. ECG, method of measurement and basic signal characteristics. EOG, method of measurement and basic signal characteristics.

5. EMG, method of measurement and basic signal characteristics. Multimodal monitoring.

6. Evoked potentials, VEP, AEP, SEP, BAEP, MEP.

7. Fourier transformation. Discrete FT. Fast FT (FFT). Principles of computing. Decimation in time and frequency. FFT butterfly. Special algorithms of computing. Inverse transform. Signal analysis and synthesis. Spectrum estimation. Filtering using FFT. Digital filters for biosignal analysis. FIR and IIR filters, properties. Linear and nonlinear phase characteristics. Types of filters, band pass, low pass, high pass, notch filters. Simple methods of design. Example of design using FFT (window method). Examples of application to real and simulated signal.

8. Spectrum analysis. Power spectral density. Periodogram. Parametric and non-parametric methods of spectral analysis. Practical problems of spectrum estimation. CSA

9. Multichannel adaptive segmentation. Motivation. Non-stationarity of biosignals. Basic methods. Multi-channel on-line adaptive segmentation. Extraction of symptoms. The parameter settings. Advantages and limitations of methods. Other segmentation algorithms.

10. Methods of automatic classification. Basic algorithms of cluster analysis. K-means algorithm. Optimal number of classes. Limits and constraints of cluster analysis. Fuzzy cluster analysis.

11. Density-based classification methods. Instance-based learning methods. K-NN classification. Fuzzy k-NN. Practical examples of classification methods for biological signals.

12. Simple methods for automatic epileptic spikes detection.

13. Topographic mapping of electrophysiological activity. Visualization. Principle of brain mapping. Amplitude and frequency brain mapping. Interpolation. Direct and inverse task. Use in clinical diagnostics.

14. Metrics. Data normalization. Statistical data processing.

Syllabus of tutorials:
Study Objective:
Study materials:


1. SÖRNMO, Leif a Pablo LAGUNA. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, ©2005. xiii, 668 s. ISBN 0-12-437552-9

2. MALMIVUO, Jaakko a Robert PLONSEY. Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. New York: Oxford University Press, 1995. ISBN 978-0-19-505823-9. Available online at the webpage:

3. SANEI, Saeid a Jonathon CHAMBERS. EEG signal processing. Hoboken, NJ: John Wiley, 2007. ISBN 978-0-470-02581-9.


1. MIKE X. COHEN. Analyzing neural time series data: theory and practice. 2014. ISBN 0262019876.

2. ESCABÍ, Monty A. BIOSIGNAL PROCESSING. Introduction to Biomedical Engineering [online]. Second Edition. Amsterdam: Elsevier Academic Press, 2005, 2005, s. 549-625. DOI: 10.1016/B978-0-12-238662-6.50012-4. ISBN 9780122386626. Available online at the webpage:

3. KANIUSAS, Eugenijus. Biomedical Signals and Sensors II [online]. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. Biological and Medical Physics, Biomedical Engineering. DOI: 10.1007/978-3-662-45106-9. ISBN 978-3-662-45105-2.

4. KANIUSAS, Eugenijus. Biomedical Signals and Sensors I [online]. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. Biological and Medical Physics, Biomedical Engineering. DOI: 10.1007/978-3-642-24843-6. ISBN 978-3-642-24842-9.

5. Advanced biosignal processing. Editor Amine NAIT-ALI. Berlin: Springer, 2009. ISBN 9783540895053.

The course is a part of the following study plans:

Package icon English Lectures I19.02 MB
Package icon English Lectures II18.11 MB