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.
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.
 Sormno L, Laguna P, Bioelectrical Signal Processing in nurological and cardiological applications, Elsevier,2005
 Bruce, E.N. Biomedical Signal Processing and Signal Modelling.New York, J.Willey & sons 2001.
 Baura G.D. System Theory and Practical Applications of Biomedical Signals.Piscataway, IEEE Press 2002.
 Krajca V., Mohylova J. Biologicke signely. e-learning www.skolicka.fbmi.cvut.cz, password signaly
 MIKE X. COHEN. Analyzing neural time series data: theory and practice. 2014. ISBN 0262019876.