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. Video: example of biosignals recording and processing in clinical practice in neurophysiologic laboratory. Introduction of the course. Basic signal characteristics (EEG, EKG, EOG, EP, EMG). EEG in epilepsy, in psychiatry, in neonatal recordings. Basic graphoelements, polysomnography, hypnogram.
2. Physiological parameters of measurement of neurological signals. Background activity, EEG bands, use in diagnosis. Artefacts, origin, sources, technical and biological artefacts Neonatal EEG, videomonitoring and sleep analysis. EEG montages: longitudinal, transversal, (average reference), Hjorth source derivation. System 10-20.
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. Statistic and probabilistic signal properties. Probability distribution. Stochastic processes and time series analysis. Convolution, impulse characteristics. Mean, standard deviation, correlation analysis. Cross-correlation function. Skewness, kurtosis, entropy. Nonstationary behaviour of EEG. Frequency bands. Adaptive parameter estimation.
5. 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.
6. 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.
7. Spectrum analysis. Power spectral density. Periodogram. Parametric and non-parametric methods of spectral analysis. Practical problems of spectrum estimation. Cross-spectrum, coherence and phase. Windowing. Relative, absolute, power and logarithmic spectrum. Modern method of spectrum analysis. Interhemispheric and local coherence.
 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