| Course Unit Code | 450-4032/02 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| KUB631 | Ing. Jan Kubíček, Ph.D. |
| Summary |
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Characteristics of biological signals, coding data structures, digital signal processing - time domain and frequency domain - spectral analysis (Fourier analysis random (biological) signal and parametric spectrum analysis of random (biological) signal. Adaptive segmentation, automatic classifying biological
signal - learning operations, cluster analysis. Neural networks.Applications of signal processing EKG, EEG. |
| Learning Outcomes of the Course Unit |
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The goal of this course is to give an information about several biomedical signals and their digital signal processing - spectral analysis (frequency and phase spectrum, autocorrelation and cross-correlation analysis), segmentation, classification, fuzzy analysis and methods of display. Acquired knowledge and skill in this subject and the whole alignment forms the basic presumption of knowledge of biomedical engineering.
Graduates will have an appropriate knowledge of techniques, equipments, systems and facilities for acquiring, processing and transferring information and how to use such information in practice. They will be able to apply this knowledge in order to interpret, describe and solve engineering problems. |
| Course Contents |
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Lectures:
Signals in medicine - origin, character and common principles processing biological signal, view of methods and algorithms processing biological signal, EEG, EMG, ECG, EOG. Origin, resources, diagnostics. Chances of exercise bioengineer.
Processing biological signal in real-time and off line. Statistical properties, probability distribution, stochastic processes, analysis of signals in time domain, analysis of signals in frequency domain
Data about patient, identification files. Collection and preprocessing biological data, A/D inverter, aliasing. Filtering. Trends.
Spectral analysis I. - fundamental method. Power spectral density, parametric and non-parametric method. Practical problems estimation of spectra. Cross spectrum, coherency and phase.
Spectral analysis II. - FFT. Application. Method compressed spectral array (CSA). Extraction of the hidden information from signals - local and interhemispheric coherence, phase, measurement of small time differences between EEG channels, time delay
Topographic mapping of brain activity - principle. Use in clinical diagnostics. Dynamic mapping
Adaptive segmentation - Adaptive segmentation with fixed and moving window, Segmentation using the two connected windows. Multichannel adaptive segmentation. Extraction symptoms.
Method automatic classification I - learning without teacher. Structure of data, classes, cluster analysis, fuzzy analysis. K-means algorithm. Limits and limitation fuzzy analyses.
Neural networks, Automatic classification II. - learning classifier, Kohonen layer, classification, classical set theory, fuzzy set theory. Compare with neural net.
Long-term EEG processing, automatic epileptic spike detection. Arithmetical detector, median detector, spike detector based on combination of classical filtering and median filtering
ECG signal, digital processing, characteristics. - frequency analysis, filtration, adaptive filtration. Data reduction, Holter's techniques patient identification.
Respirometry, description signal data. Demand on digital processing and graphic presence.
Video signal - image processing, Presentation in discrete form.
Computer labs:
Introduction into processing biosignal. Practical examples of EEG, EMG, ECG activities, epileptic graphoelements, artefacts.
Statistical characteristics of biosignals. Software. User's interface. Data format.
I Semester work - reading and displaying real signal, term: 1 week
Collection and preprocessing biological data. Data reading in classical and paperless apparatus. A/D inverter Nyquist theorem. Mistakes at transmission.
Spectral analysis I. Fundamental method. Spectral analysis and synthesis - FFT. Filtration, windowing.
II Semester work. Spectral analysis and synthesis of signal term: 2 weeks
Topographic mapping. Demonstration topographic of brain activity - spectral, phase, time delay and coherence mapping - iterative generation map. Animation.
Spectral analysis II - application CSA. Coherence analysis
III Semester work Topographic (brain) mapping - net with 20 point term: 2 weeks
Adaptive segmentation - setting parameter, preference and limitation, algorithm.
Method automatic classification I - learning without teacher. Fundamental algorithm of cluster analyses on simulated data. Examples classification EEG data. Using fuzzy set.
Analysis long-term signal. Summary information.
Extraction compressed information from long-term signal. Applications on real data, further method, programme WaveFinder.
Automatic classification II - learning classifier. Demonstration fundamental algorithm learning classifier on simulated and data. Using fuzzy set in to-NN classifier
IV semester work: 3-NN learning classifier for simulated data. Term: 2 for weeks
Automatic epileptic spike detection - Demonstration commercial programme (Gotman, Scherg, FOCUS).
Preprocessing ECG signal using of wavelet transformation: compression, filtration, artefacts. Calculation of frequency, amplitude and phase spectrum.
One detector algorithm of QRS complex - calculation and comparison, using method detection R-R internals depending on morbid state and variability heart rhythm. Practical demonstration fully automatic evaluative system signal ECG on regional hygienic station in Ostrava.
(Excurse in computerized EEG laboratory Neurological department FN Bulovka. Consultation record semester washing.)
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| Recommended or Required Reading |
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| Required Reading: |
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De Luca, G.: Fundamental Concepts in EMG Signal Acquisition; DelSys Inc, 2001
Kay, S.M., Marple, S.L.: Spectrum Analysis – A Modern Perspective, Proc. IEEE, vol. 69, 1981, pp. 1380-1419
Proakis, J.G., Manolakis, D.G.: Introduction to Digital Signal Processing. Macmillan Publishing Company , New York, 1988 (ISBN 0-02-396815-X)
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[1] Mohylová,J., Krajča, V.: Zpracování biosignálů. Skriptum VŠB TUO, 2007
[2] Krajča, V., Mohylová,J.: Číslicové zpracování neurofyziologických signálů. Skriptum ČVUT, 2011
[3] Svatoš J., Biologické signály I. Geneze, zpracování a analýza. Skriptum ČVUT FEL,1995
[4] Mohylová J.,Využití číslicového zpracování signálů EEG pro lokalizaci epileptického ložiska. Disertační doktorská práce, 1999 Literatura doporučená (separáty článků lze získat u přednášejícího)
[5] De Luca, G.: Fundamental Concepts in EMG Signal Acquisition; DelSys Inc, 2001
[6] Kay, S.M., Marple, S.L.: Spectrum Analysis – A Modern Perspective, Proc. IEEE, vol. 69, 1981, pp. 1380-1419
[7] Proakis, J.G., Manolakis, D.G.: Introduction to Digital Signal Processing. Macmillan Publishing Company , New York, 1988 (ISBN 0-02-396815-X)
[8] Šíma J., Neruda R. Teoretické otázky neuronových sítí
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| Recommended Reading: |
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Cohen A., Biomedical signal processing, CRC Press, Boca Raton, Florida
Remond, A.: (Editor-in-chief): Handbook of electroencephalograph and clinical neuro-physiology, vol. 5. Elsevier, 1972
Dumermuth G., Fundamentals of spectral analysis in electroencephalography, In: A. Rémond (Ed.), EEG Informatics : A Didactic Review of Methods and Applications of EEG data Processing. Elsevier, Amsterdam,1977, pp. 83-105
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Svatoš J., Biologické signály I. Geneze, zpracování a analýza. Skriptum ČVUT FEL,1995
Mohylová J.,Využití číslicového zpracování signálů EEG pro lokalizaci epileptického ložiska. Disertační doktorská práce, 1999 Literatura doporučená (separáty článků lze získat u přednášejícího) |
| Planned learning activities and teaching methods |
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| Lectures, Individual consultations, Tutorials, Experimental work in labs, Project work |
| Assesment methods and criteria |
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| Tasks are not Defined |