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Last update: Pátková Vlasta (20.04.2018)
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Last update: Pátková Vlasta (20.04.2018)
Students completing the course will be able to model basic biological processes at the single-cell level, interactions of cells group, organs and the whole organism. For the modeling of biological processes in space and time they will be able to process 1D, 2D and 3D signals. To test the response of the organism to external stimuli they will be able to prepare experiments on the Vernier equipment (for sensing temperature, pressure, ECG, ventilation parameters and muscle activity) and Walter (to capture visual and cognitive evoked potentials and EEG). |
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Last update: Pátková Vlasta (20.04.2018)
R: Reddy D.C.: Biomedical Signal Processing � Principles and Techniques, McGraw Hill, 2005,ISBN: 0070583889 A: Weitkunat R.: Digital Biosignal Processing, Elsevier, 1991, ISBN-10: 0444891447, ISBN-13: 978-0444891440 Z: Drongelen W., Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals, Elsevier, 2007, ISBN-10: 0123708672 ISBN-13: 978-0123708670 A: Izhikevich E. M., Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience), The MIT Press, 2007, ISBN 0262090430, 9780262090438 |
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Last update: Mareš Jan doc. Ing. Ph.D. (26.04.2018)
www.honeywellprocess.com/
www.mathworks.com/
www.ni.com/ |
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Last update: Knociková Juliana Alexandra RNDr. Ing. Ph.D. (18.09.2023)
1. Biological signals as a source of medical data, homeostasis, genesis and characteristics of featured biosignals (ECG, EEG, EMG, ENG, EOG…) 2. Different biosignals according to the phyisical essence and rhytmicity. Properties of biosignals and adequate methods of analysis. 3. Generation and transfer of biosignals. Passive and active transport at a cellular level, action potential. 4. Recording of biosignals. Sampling, quantizing and digital filtration. Variety of filters and methods of the noise elimination. 5. Processing of signals in time and frequency domain, spectral analysis, periodogram and FFT. 6. Non-stationarity and modification of the time-frequency resolution. Wavelet analysis of biosignals. 7. Electrocardiography, heart rate variability, electrical axis of the heart. Nonlinear dynamics in analysis of parameters of normal and pathological ECG. 8. Chaos a dynamical analysis of biosignals. The entropic brain theory. 9. Quantitative electroencephalography, automatic detection of patterns. Analysis of EEG changes under different neuropsychiatric conditions. 10. Discriminant and cluster analysis, fuzzy sets. 11. Topographic brain mapping – amplitude, frequency… 12. Biostatistics and testing of the hypotheses in biomedical studies. 13. Artificial neural networks, introduction to the methods of artificial intelligence. 14. Biosignals and detection of quantitative biomarkers in physiological and pathophysiological conditions. |
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Last update: Pátková Vlasta (20.04.2018)
none |
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Last update: Knociková Juliana Alexandra RNDr. Ing. Ph.D. (17.09.2023)
Vypracování a obhajoba pěti ročníkových projektů: 0 - 30 bodů
Ústní zkouška: 0-70 bodů
Celkové bodové hodnocení: 100-90 A, 89-80 B, 79-70 C, 69-60 D, 59-50 E, 49-0 F. |
Teaching methods | ||||
Activity | Credits | Hours | ||
Obhajoba individuálního projektu | 1 | 28 | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 1 | 28 | ||
Účast na seminářích | 1 | 28 | ||
4 / 4 | 112 / 112 |