Analysis and interpretation of biological data
FEKT-LABDAcad. year: 2015/2016
The course is focused on native and evoked biological signals (biosignals). It focuses on the characteristics of biosignals generated by the various systems of the human body (especially cardiovascular, nerve and muscle). The course is focused on methods for processing and analysis of biosignals in the time and frequency domain.
Learning outcomes of the course unit
- formulate requirements for filters for noise suppression in ECG, EEG, EMG signals
- design and implement adaptive filters for suppressing power hum in biosignals
- design and implement special filters Lynn type for narrowband interference suppression
- explain the principle of detection of QRS complexes in ECG signals and graphoelements in EEG signals
- describe the principle of detecting the beginning and end of major waves in the ECG signals
- explain the principles of stationarity tests of stochastic signals
- describe the principle of non-parametric and parametric methods for estimating power spectra
- describe the principle of cross-spectra and coherence spectra estimation and their use for analysis of EEG signals
- describe the principle of Poincare maps and their use for signal analysis (HRV, TWA)
- explain the principle of realization mapping for analysis of EEG signals
- explain the principle of continuous estimate the level of surface EMG signal
Recommended optional programme components
Svatoš, J.: Biologické signály I. Geneze, zpracování a analýza. Skripta FEL ČVUT, Vydavatelství ČVUT, Praha, 1992 (CS)
Sornmo,L., Laguna,P.: Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier, 2005. (EN)
Kozumplík, J.: Analýza biologických signálů. Elektronická skripta FEKT VUT v Brně, Brno, 2013 (CS)
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- obtaining at least 12 points (out of 24 as course-unit credit based on active presence in demonstration exercises),
- successful passing of final written exam (up to 76 points)
Language of instruction
2. Electrocardiogram (ECG), its properties and methods of scaning and display. The processing of rest and stress ECG signals.
3. Preprocessing of ECG signals, linear and non-linear filters for suppressing interference.
4. Detectors QRS complexes. Analysis of the heart rate variability (HRV) in the time and frequency domains..
5. Delineation of ECG signals, morphological and rhythm analysis. Analysis of the T wave alternans (TWA)
6. Introduction to the wavelet transform.
7. Filtering and analysis of biosignals using WT.
8. Phonocardiogram and its analysis. Elektrogastrogram (EGG) and its analysis.
9. Electromyogram (EMG signal), MUAP analysis and analysis of surface EMG signals.
10. Electroencephalogram (EEG signal). Analysis of EEG signals in the time domain.
11. Analysis of EEG signals in the frequency domain.
12. Evoked EEG signals, biosignals of visual and auditory systems.
Specification of controlled education, way of implementation and compensation for absences
- obligatory computer-lab tutorial
- voluntary lecture
Classification of course in study plans
- Programme EEKR-ML Master's
branch ML-BEI , 1. year of study, winter semester, 5 credits, compulsory
- Programme EEKR-ML1 Master's
branch ML1-BEI , 1. year of study, winter semester, 5 credits, compulsory
- Programme EEKR-CZV lifelong learning
branch ET-CZV , 1. year of study, winter semester, 5 credits, compulsory