PřF:Bi5445 Biosignal analysis - Course Information
Bi5445 Biosignal Processing and AnalysisFaculty of Science
- Extent and Intensity
- 2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
- prof. Ing. Jiří Holčík, CSc. (lecturer)
doc. Ing. Daniel Schwarz, Ph.D. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX - Faculty of Science
- Tue 14:00–15:50 MP2,01014a
- Prerequisites (in Czech)
- Bi5440 Signals & Linear Systems
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Course objectives
- At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of methods and algorithms of biosignal processing and analysis; - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics
- 1. Signals - definitions, characteristics. Genesis and properties of biosignals. General block diagram of biosignal processing and analysis. Basic characteristics of biosignals in time and frequency domain – repetitive and non-repetitive signals, signals coupled with external events. 2. Signals of cardiovascular system. ECG – origin and basic signal parameters of the complete signal as well as its parts in time and frequency domains. Kinds of ECG noise and its characteristics. 3. Algorithms for removing basic kinds of noise – principles of algorithms for filtering baseline drift and AC interference. 4. Algorithms for filtering myopotentials. Data reduction. 5. ECG analysis. Principles of ECG wave detection. Various ECG records (short-time record, long-time record, on-line ECG signal monitoring), requirements for their proc-essing. 6. Fetal ECG signal, properties in time and frequency domain. Methods to separate FECG from the maternal ECG – spatial averaging, correlation approach. Phonocar-diogram – basic characteristics in time and frequency domain, relationship between ECG and phonoCG. 7. Heart rate variability – origin and description. Properties of its frequency spectrum in connection to a cardiovascular system control and other information of cardiovascular activity. 8. Signals of electrical brain activity. Spontaneous EEG activity. EEG noise. Characteris-tics in time and frequency domain. Power spectral density and algorithms to compute it. Diagnostically important characteristics of EEG signals. 9. EEG graphoelements, their characteristics in time domain. Detection and application in diagnostics. Maps of spontaneous electrical activity of brain cells. 10. Evoked potentials. Averaging for filtering EP noise. Acoustically evoked potentials - characteristics, detection of important waves and points. Visually evoked potentials. Properties and analysis of important parameters. 11. Electromyogram. Definition, genesis, recording. Properties. Application. 12. Oculographic signals. Properties in time and frequency domain. Processing. Applica-tion. Electronystagmogram.
- Holčík,J.: Biologické a lékařské signály. [Elektronické studijní texty], http://www.fbmi. cvut.cz/predmety/bbls/
- 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
- Cohen, A. Biomedical Signal Processing. Vol. I Time and Frequency Domains Analysis. Vol. II Compression and Automatic Recognition. Boca Raton, CRC Press 1986
- Teaching methods
- Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
- Assessment methods
- credit course requirements for the credit: written essay about methods for processing and analysis of biological signals not mentioned in lectures
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.