Course detail
Advanced Analysis of Biological Signals
FEKT-MPA-ACSAcad. year: 2024/2025
The course is oriented to multirate signal processing, time-frequency analysis focused particularly on the different types of wavelet transform, parametric methods for power spectrum estimation, principal component analysis (PCA) and data compression.
Language of instruction
English
Number of ECTS credits
5
Mode of study
Not applicable.
Guarantor
Offered to foreign students
The home faculty only
Entry knowledge
Students should have knowledge of digital signal processing, be familiar with the ways of describing the linear filters (transfer function, impulse response, difference equations, frequency response). We assume basic knowledge of students about the properties of biosignals (especially ECG, EEG, EMG). The laboratory work is expected knowledge of Matlab programming environment.
Rules for evaluation and completion of the course
- 30 points can be obtained for activity in the laboratory exercises, consisting in solving tasks (for the procedure for the examination must be obtained at least 15 points)
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)
Laboratory is compulsory, missed labs must be properly excused and can be replaced after agreement with the teacher.
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)
Laboratory is compulsory, missed labs must be properly excused and can be replaced after agreement with the teacher.
Aims
Gaining knowledge about multirate signal processing, wavelet transforms for processing and analysis of biosignals, principal component analysis (PCA), applications of PCA for analysis of biosignals and parametric methods for power spectrum estimation. Basic understanding of information theory, getting to know with the methods of lossless and lossy data compression.
The student is able to:
- implement the sampling rate conversion
- explain the principles and advantages of multirate filtering
- implement of the various types of wavelet transforms
- explain the principles of filtering and data compression based on wavelet transform
- explain the principles of lossless data compression (Huffman encoder, arithmetic coder)
- explain the principles and possibilities of the use of PCA
The student is able to:
- implement the sampling rate conversion
- explain the principles and advantages of multirate filtering
- implement of the various types of wavelet transforms
- explain the principles of filtering and data compression based on wavelet transform
- explain the principles of lossless data compression (Huffman encoder, arithmetic coder)
- explain the principles and possibilities of the use of PCA
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
DEBNATH, L. Wavelet transforms and their applications. Boston: Birkhäuser, c2002. ISBN 0-8176-4204-8. (EN)
JAN, Jiří. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 0852967608. (EN)
JAN, Jiří. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 0852967608. (EN)
Recommended reading
Not applicable.
Elearning
eLearning: currently opened course
Classification of course in study plans
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
Syllabus
01-Course introduction
02-Sampling frequency conversion
03-Filters with sampling frequency conversion
04-Wavelet Transform - introduction
05-Wavelet Transform - wavelet filter design
06-Wavelet Transform - orthogonal
07-Wavelet Transforms + signal filtering
08-Wavelet Transforms + signal compression
09-Wavelet Transforms + ECG delineation
10-Use of a SmartPhone in telemedicine
11-Empirical Mode Decomposition
12-Instantaneous frequency/amplitude
13-Linear deconvolution, Nonlinear filtration methods
02-Sampling frequency conversion
03-Filters with sampling frequency conversion
04-Wavelet Transform - introduction
05-Wavelet Transform - wavelet filter design
06-Wavelet Transform - orthogonal
07-Wavelet Transforms + signal filtering
08-Wavelet Transforms + signal compression
09-Wavelet Transforms + ECG delineation
10-Use of a SmartPhone in telemedicine
11-Empirical Mode Decomposition
12-Instantaneous frequency/amplitude
13-Linear deconvolution, Nonlinear filtration methods
Exercise in computer lab
26 hod., compulsory
Teacher / Lecturer
Syllabus
01-Repetition, signal processing in Matlab
02-Sampling frequency conversion
03-Project assignment + project kick off
04-WT in Matlab - wavelet toolbox, manual wavelet decomposition
05-WT in Matlab - manual wavelet reconstruction
06-WT in Matlab - signal filtering
07-WT in Matlab - signal compression
08-WT in Matlab - ECG delineation
09-Test + SmartPhone in telemedicine
10-Work on individual projects (individual consultation)
11-Work on individual projects (individual consultation)
12-Work on individual projects (individual consultation)
13-Project presentation + lab evaluation
02-Sampling frequency conversion
03-Project assignment + project kick off
04-WT in Matlab - wavelet toolbox, manual wavelet decomposition
05-WT in Matlab - manual wavelet reconstruction
06-WT in Matlab - signal filtering
07-WT in Matlab - signal compression
08-WT in Matlab - ECG delineation
09-Test + SmartPhone in telemedicine
10-Work on individual projects (individual consultation)
11-Work on individual projects (individual consultation)
12-Work on individual projects (individual consultation)
13-Project presentation + lab evaluation
Elearning
eLearning: currently opened course