UFPF533 Methods of biosignal processing

Faculty of Philosophy and Science in Opava
Summer 2016
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Stanislav Hledík, Ph.D. (lecturer)
doc. RNDr. Stanislav Hledík, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Stanislav Hledík, Ph.D.
Centrum interdisciplinárních studií – Faculty of Philosophy and Science in Opava
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
The aim of the course is to familiarize students with the basic biological signals, their specifics, processing, analysis and ways to display the processed results. For the analysis of real EEG and ECG data, Mathematica environment will be used.
Syllabus
  • Resources, specifics, general principles and diagnostic use of digital signal processing. Overview of methods and algorithms of signal processing. EEG, EMG, ECG, EOG. Real time and offline processing, statistical characteristics, stochastic processes, time series analysis, nonstationarity.
    Acquisition and pre-processing of biological data. Digitization, sampling and quantization, aliasing. Filtration. Trends.
    Spectral analysis. Basic methods. Periodogram, autoregressive model. Parametric and nonparametric methods. Estimation of the spectrum, cross spectrum, coherence, phase. Fast Fourier Transform (FFT), its importance and applications. Displaying using compressed spectral arrays (CSA). EEG - local and interhemispheric coherence. Topographic mapping of electrophysiological activity. Brain mapping. Amplitude and frequency mapping. Use in clinical diagnostics. Dynamic mapping.
    Adaptive segmentation. Motivation. Nonstationary biosignals. Basic methods. Multi-channel on-line adaptive segmentation. Feature extraction.
    Methods for automatic classification. Unsupervised learning, metrics, data normalization. Clustering, K-means algorithm. Fuzzy sets. Optimizing the number of classes. Neural networks. Hebb learning. Multi-channel signals. Self-organizing method of principal components. Learning classifiers. Supervised vs. unsupervizované learning. On-line classification. k-NN classifier (classical, fuzzy). Comparison with neural networks.
    Automatic detection of epileptic graphoelements. Automatic detector of spikes, arithmetic detector, combined detector. Principal components and conventional filtering for detection. ECG signal, the digital processing, features. Criteria for ECG digitization, frequency analysis and filtering, adaptive filtering. Data reduction, Holter identification techniques.
Literature
    recommended literature
  • Lyons R.G. Understanding Digital Signal Processing. Prentice Hall; 3 edition, 2010. ISBN 978-0137027415. info
  • Oppenheim A.V., Schafer R.W. Discrete-Time Signal Processing. Prentice Hall; 3 edition, 2009. ISBN 978-0131988422. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
The attendance at lectures is recommended. It can be substituted by
the self-study of recommended literature and individual consultations. The attendance at tutorials
is compulsory (min. 80%).
The course is also listed under the following terms Summer 2015.
  • Enrolment Statistics (recent)
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