The concept of the research group Biocybernetics of physiological processes can be studied as a part of an optional course Biosignals and Models. The course content is designed in accordance with the latest trends in the analysis and modeling of biomedical data in research and clinical practice. The main topics of the course are:
- Genesis and characteristics of selected biosignals (EKG, EEG, EMG, ENG, EOG...)
- Sampling, quantization, digital filtering
- Electrocardiography and heart rate variability
- Spectral analysis, periodogram and FFT
- Non-stationarity and modification of time-frequency resolution, wavelet analysis
- Quantitative electroencephalography, automatic pattern detection
- Nonlinear dynamics and chaos theory
- Discriminant and cluster analysis, fuzzy sets
- Topographic mapping of brain electrical activity
- Artificial neural networks, an introduction to artificial intelligence methods
The issues of biomedical data analysis, mathematical modeling in biomedicine and biocybernetics can also be studied as part of final bachelor's or diploma theses. Contact email in case of interest in the final thesis:
Juliana.Alexandra.Knocikova@vscht.cz
Bachelor's theses:
A graphical user interface for topographic mapping of brain electrical activity
The main goal of this bachelor's thesis is to create a custom graphical user interface in the Matlab environment with the possibility of mapping the electrical activity of the brain. Different approaches to topographic mapping will be compared and discussed. This "user-friendly" graphical user interface will be demonstrated on real EEG data with the potential to be used in clinical practice.
Principles of quantitative electroencephalography
Diploma theses:
Modern mathematical methods of EEG analysis in monitoring of anesthesia
Complex brain activity, representing a non-linear and non-regular system, is often explained by non-linear dynamics methods. The diploma thesis discusses the application of these modern mathematical procedures for the characterization of isoflurane anesthesia, in accordance with the entropic brain theory. The work will also include a proposal for topographic mapping.
Nonlinear dynamics of heart rate changes and its quantitative analysis in the Matlab environment
Diploma thesis will be focused on the evaluation of cardiovascular dynamics during various /patho/physiological changes. The student will create a diagnostic tool for monitoring selected linear and mainly non-linear heart rate variability parameters and implement them in the Matlab environment.
Supporting the diagnosis of respiratory diseases from the cough sound analysis by machine learning methods
The aim of this diploma thesis is to analyze the possibilities of machine learning methods for the classification of respiratory diseases from the sound of cough. The student will design a mathematical method for analyzing cough sounds in patients with respiratory diseases and implement an optimal discriminant analysis method for classification.
Artificial neural networks in automatic detection of sleep stages from EEG
An artificial neural network, as a computational model used in artificial intelligence, represents an important tool in the analysis of complex brain activities. In this thesis, various parameters of the network structure and the learning process will be used in the automatic detection of sleep states from EEG data. The application in diagnosis of sleep changeswill also be discussed.
Mathematical modeling of cell volume alterations under different osmotic conditions
In this diploma thesis, the student models the mechanisms of changes in cell volume and tests the response of cells in different ion concentrations. A mathematical model based on a system of differential equations will be created in accordance with the known biophysical mechanisms of mammalian cells. Water permeability values in different osmotic environments will also be calculated and discussed in the context of maintaining homeostasis in physiological and pathophysiological conditions.
Quantitative EEG biomarkers behind major depressive disorder
The aim of this diploma thesis is to present the methods of analysis of neurophysiological EEG data as a prognostic and diagnostic tool for depressive disorders. Emphasis will be placed on the mathematical description of linear and non-linear EEG parameters and methods of their automatic detection.
Mathematical model of sound perception by human ear
This diploma thesis deals with the algorithmization of sound perception. Emphasis is placed on the biophysical properties of the basilar membrane and cochlear hydrodynamics. However, the main goal is to construct a mathematical model of the transformation of a sound stimulus into a series of nerve impulses. The model will be created in accordance with the masking theory of tones of different volume and frequency.