Czech

Not applicable.

The student is able to:
- formulate requirements for filters for noise suppression in ECG, EEG, EMG signals
- 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

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.

Not applicable.

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.

- 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 exam (the examination is necessary to obtain at least 35 points)

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

Not applicable.

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.

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

Not applicable.

Not applicable.

Kozumplík, J.: Multitaktní systémy. Elektronická skripta FEKT VUT v Brně, 2005 (CS)

Not applicable.