English

Not applicable.

The home faculty only

The student should have knowledge of digital signal processing. They should be familiar with different methods of describing linear systems (transfer function, impulse response, difference equation, frequency response). Basic knowledge of biosignal characteristics (especially ECG, EEG, EMG) is expected. In computer-based exercises, familiarity with the Matlab programming environment is assumed.

Knowledge test (selected topics from lectures and laboratory exercises) - 10 points.
Individual project (software solution + report + presentation) - 20 points.
Final exam (written form) - 70 points.

To be admitted to the final exam, a minimum of 15 points must be obtained from the test and project, and a minimum of 35 points must be obtained from the written exam. Laboratory exercises are mandatory.

The aim of the course is to provide students with a deeper understanding of advanced biosignal processing methods, focusing on wavelet transformation, empirical mode decomposition, and related analytical techniques. Students will become familiar with the theoretical principles and practical implementation of these methods. Emphasis is placed on applications in biomedical engineering and telemedicine, including the use of smartphones.

A graduate of the course will be able to:
- perform sampling rate conversion and explain its impact on signal processing
- design and implement filters with sampling rate conversion
- understand the principles of wavelet transformation and apply it for filtering, compression, and analysis of biological signals
- analyze data using empirical mode decomposition
- estimate instantaneous frequency and amplitude of mono-component signals
- implement linear deconvolution and nonlinear filtering methods
- use smartphones for telemedicine applications

Not applicable.

Not applicable.

DEBNATH, Lokenath. Wavelet Transforms and Their Applications. 2002. ISBN 3764342048. (EN)
HUANG, Norden E., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, 454.1971: 903-995. (EN)
JAN, Jiří. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 0852967608. (EN)
REYES ORTIZ, Jorge Luis. Smartphone-Based Human Activity Recognition. Springer, 2016. ISBN 9783319367705. (EN)
SALOMON, David. Data Compression: The Complete Reference. Springer Science & Business Media, 2007. ISBN 9781846286032. (EN)

Not applicable.