450-4075/01 – Processing of Biosignals (ZBS)
| Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
| Subject guarantor | Ing. Jaroslav Vondrák, Ph.D. | Subject version guarantor | Ing. Jaroslav Vondrák, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Objective of the course in terms of learning outcomes and competences The aim of the subject is to introduce the students to the individual methods and methods of processing of biosignals. All students are tested with practical examples of processing of different biosignals in MATLAB environment.
Teaching methods
Lectures
Individual consultations
Experimental work in labs
Summary
A subject completely deals with an issue of the mathematical methods for processing and modelling of the biological signals and consequent extraction of the clinical information. The first part of the subject is focused on basic methods for processing and analysis of the biological signals in the time, frequency and time-frequency domain. Individual methods always will be put to a context of the real biological signals and practical applications which are closely connected with the clinical practice. A significant part of the subject is an analysis and methods for the noise elimination from the biological signals. In this context, we will use both the synthetic noise generators and real noise signals for demonstration and the noise effect analysis on the diagnostic information quality. As a part of this analysis we will analyze instruments quantifying a noise level and objectively measure the filtration methods effectivity. In the last part of the subject, we will discuss conventional mathematical algorithms which are closely connected with specific tasks from an area of the biomedical signals processing. We will figure out visualization and possibilities of the EEG processing. Algorithms for features extraction from the ECG signal as it is the QRS complex extraction, the R peak detection and heart rate variability (HRV). In the last stage, this subject will be focused on an issue of the PPG, EMG, EGG, breathing and acoustic signals.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Continuous examination of the study: Two tests of continuous examination, combined examination, recognition of the examination only after successful completion of all its parts. Assesment methods and criteria linked to learning outcomes: Credit test: Two tests of continuous control max. 20 points. Total max. 40 points, min. 21 points. Assesment methods and criteria linked to learning outcomes: Attendance at seminars requires at least 80% of the taught lessons.
E-learning
Other requirements
Assesment methods and criteria linked to learning outcomes: Attendance at seminars requires at least 80% of the taught lessons.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
Basic characteristics and classification of the biologic signals: computer representation, discretization, types, biological origin, diagnostic, time, frequency, and time-frequency analysis.
Convolution analysis of the biological signals: analysis of the continuous and discrete convolution.
Classification of the biological signals: neural networks, genetic algorithms, unsupervised learning, and clustering analysis.
Spectral analysis of the biologic signals: Fourier series, Fourier transformation, algorithms for the FFT, spectral density, spectral energy and power density, frequency spectra and window functions.
Noise analysis in the biological signals: types, origin, representation, methods for noise evaluation, biological signal distortion by the noise.
Filtration of the biological signals: synthesis of the analog and digital filters, FIR and IIR filters, notch filter, recursive filters, and filter frequency analysis.
The EEG signal analysis: the ECG signal representation, compressed spectral array (CSA), topographic mapping of the electrophysiological activity, interpolation of the spatial information, amplitude and frequency mapping, local coherency and phase measuring.
The ECG signal analysis: the noise analysis of the ECG signal, ECG signal representation, algorithms for the QRS complex extraction, Pan Tompkins algorithm, the R peak detection, ECG signal classification and calculation of the heart rate variability (HRV).
The PPG signal analysis: the PPG signal noise analysis, representation of the PPG signal, detection of the heart systolic phase and comparison of the heart rate from the PPG and ECG signal.
Analysis of the EMG signal: genesis, representation, features, measuring of the EMG signal, and basic methods of the EMG processing.
Breathing signals: analysis of the breathing curves and gases. Analysis of lung capacity and volume.
Electrical signals of eye: representation, detection, features, and processing of the EOG and ERG signals.
EGG analysis: analysis of the stomach electrical activity, analysis in the time and frequency domain, frequency components of the EGG signal, and spectrogram.
Analysis of the acoustic biologic signals.
Computer Excercises:
Basics of signal processing in the MATLAB.
Convolution analysis of the biological signals.
Classification of the biological signals.
Spectral analysis of the biological signals.
Noise analysis in the biological signals.
Biological signals filtration.
Analysis of the EEG signal.
Analysis of the ECG signal.
Analysis of the PPG signal.
Analysis of the EMG signal.
Analysis of the breathing signals.
Analysis of the eye electrical signals.
Analysis of the EGG signal.
Analysis of the acoustic biological signals.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction