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F7ABBBLS - Biological Signals

Code Completion Credits Range Language
F7ABBBLS Z,ZK 4 2P+2L English
In order to register for the course F7ABBBLS, the student must have successfully completed or received credit for and not exhausted all examination dates for the course F7ABBUSS. The course F7ABBBLS can be graded only after the course F7ABBUSS has been successfully completed.
The course F7ABBLPZ1 can be graded only after the course F7ABBBLS has been successfully completed.
The course F7ABBLPZ2 can be graded only after the course F7ABBBLS has been successfully completed.
In order to register for the course F7ABBTA, the student must have successfully completed the course F7ABBBLS.
The course F7ABBEMP can be graded only after the course F7ABBBLS has been successfully completed.
Garant předmětu:
Václava Piorecká
Václava Piorecká, Marek Piorecký
Christiane Malá, Marek Piorecký, Zoltán Szabó
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. Advanced methods of digital biosignal processing,spectrum analysis, modern methods of artificial intelligence, features extraction, automatic classification, graphic presentation of results. Adaptive segmentation, artificial neural networks for signal procesing.


Basic knowledge of MATLAB, C++ or other object-oriented language is an advantage, but it is not strictly required.

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.

8. 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.

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

10. 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.

11. 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.

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

13. Simple methods for automatic epileptic spikes detection.

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

Syllabus of tutorials:

Outline and syllabus of excersises

1. Artefacts in biosignal recording. Measurement of the electrical properties of the recording electrodes.

2. Measurement on ECG.

3. Measurement on EMG.

4. Measurement on EEG.

5. Measurement of tendon jerks.

6. Recording of evoked EEG potentials.

7. Audiometric measurements.

Study Objective:

The aim of the subject is to get to know the basic biological signals of electric origin, methods of their recording and protecting their diagnostic values. The basic and advanced methods of biosignal processing will be discussed.

Study materials:

Povinná literatura:

[1] KRAJČA V. , MOHYLOVÁ J.: Číslicové zpracování neurofyziologických signálů. Skriptum ČVUT, 2011.

Doporučená literatura:

[1] ROZMAN J. a kol. Elektronické přístroje v lékařství. ACADEMIA Praha 2006

[2] BAURA, Gail D. System theory and practical applications of biomedical signals. Hoboken, NJ: Wiley-Interscience, c2002. ISBN 0-471-23653-5.

[3] SVATOŠ J., Biologické signály I. Geneze, zpracování a analýza. Skriptum ČVUT FEL,1995

[4] SÖRNMO L., LAGUNA P.: Bioelectrical signal processing in electrocardiac and neurological applications. Elsevier 2009

[5] SANEI S., CHAMBERS J.A., EEG Signal Processing,Wiley 2007

Studijní pomůcka:

e-learning: password: signaly

The course is a part of the following study plans: