UFBM002 Medical diagnostic systems and medical data processing

Faculty of Philosophy and Science in Opava
Winter 2017
Extent and Intensity
2/2/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Iveta Bryjová (lecturer)
Ing. Iveta Bryjová (seminar tutor)
Guaranteed by
doc. RNDr. Stanislav Hledík, Ph.D.
Centrum interdisciplinárních studií – Faculty of Philosophy and Science in Opava
Prerequisites (in Czech)
Vhodné je absolvování předmětu UF/PA112 "Základy elektřiny a magnetismu".
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course makes students acquainted with selected medical diagnostic systems at the clinical and technological level, and with the way of their data outputs processing. The aim is to demonstrate the role of technology and data processing in current diagnostic methods. The course includes field trips to a selected hospital wards.
Syllabus
  • 1. Introduction to biological systems and signals. Characteristics, information transfer and control, biological signals and their classification, the basic division of biosignals.
    2. Systems for diagnostics with 1D output. Measurement of blood pressure - biophysics and biomechanics of blood circulation, measurement of blood pressure. Bioelectric phenomena in the body - nature, selected diagnostics (EKG, EEG, EMG, cutometry), analysis and signal processing of ECG and EEG output.
    3. Systems for diagnostics with image (2D) output. Ultrasonography, X-ray, CT, MRI, PET, SPECT, LDPI - principles and methods of diagnosis, typical image outputs, data processing.
    4. Medical data processing I. Acquisition and pre-processing of biological data. Digitization, sampling and quantization, aliasing. Filtration. Trends.
    5. Medical data processing II. Spectral analysis. Parametric and non-parametric methods. Fast Fourier Transform (FFT), spectrum estimation, cross-spectrum, coherence, phase. Imaging using compressed spectral arrays (CSA). EEG - local and interhemispheric coherence. Topographic mapping of electrophysiologic activity. Brain mapping. Amplitude and frequency mapping.
    6. Medical data processing III. Methods of automatic classification. Learning without a teacher, metrics, data normalization. Cluster analysis, K-means algorithms. Fuzzy sets. Optimizing the number of classes. Neural networks. Hebb learning. Multi-channel signals. Self-organizing principal components. Learning classifiers. Supervised vs. insupervized learning. On-line classification. k-NN classifier (classical, fuzzy).
    7. Medical data processing IV. Adaptive segmentation. Motivation. Nonstationarity of biosignals. Basic methods. Multi-channel on-line adaptive segmentation. Feature extraction.
    8. Medical data processing V. Automatic detection of epileptic graphoelements. Automatic detector spikes, arithmetic detector, combined detector. Main components method and classical filtering for detection. ECG signal, digital processing, properties. ECG, criteria for digitization, frequency analysis and filtering, adaptive filtering. Data reduction, Holter identification techniques.
    9. Medical data processing VI. Introduction to statistical methodology, statistics in biomedical research. Fundamentals of probability theory and basic probability distribution. Descriptive statistics. Hypothesis testing. ROC analysis.
    10. Implementation in the Mathematica system. Signal image outputs, EEG output sonification, mapping of brain activity, CSA method.
    Current information and additional study materials can be found here: http://j.mp/iaka4/
    (once the page appears, click gradually on the folders Kursy, SUO-FPF, 1-ZS, UFBM001-MeDiSysZpMeDat)
Literature
    recommended literature
  • Svatoš J. Biologické signály I. Geneze, zpracování a analýza. Skriptum ČVUT FEL, Praha, 1995. info
    not specified
  • Ullmann V. Aplikace ionizujícího záření. URL info
  • Najarian K. Biomedical Signal and Image Processing. CRC Press, 2005. ISBN 978-0849320996. info
Teaching methods
Lecture with a video analysis
One-to-One tutorial
Internship
Skills demonstration
Assessment methods
Test
The analysis of student 's performance
Credit
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
Credit: elaboration of semester project on a given topic of at least 4 and a maximum of 20 pages of text. Topics will be assigned during the semester list of selected topics suggested by teachers is available on the website (see section contents). Students can design their own theme and have them approve the teacher. We welcome topics the processing of which actively contributes to the student's skills and acquired knowledge. Examination: oral only, consisting of one of the clinical questions and one question on the monitoring. In case of an extremely successful semestral project it can be (after successful discussion with the student) recognized as equivalent to passing the exam and graded A.
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016.
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