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Novel methods for sleep analysis and classification
Navrátilová, Markéta ; Ronzhina, Marina (referee) ; Kolářová, Jana (advisor)
Tato diplomová práce se zabývá metodami pro analýzu a klasifikaci spánku. Popisuje jakjednotlivé spánkové fáze a vzorce biosignálů v průběhu spánku, tak metody pro klasifi-kaci. Příznaky jsou extrahovány na dodaných biosignálech ECG, EDA a RIP. Na základětěchto příznaků jsou klasifikovány jednotlivé spánkové fáze s využitím klasifikátoru ná-hodný les. Parametry klasifikátoru jsou optimalizovány a následně jsou vyhodnocenydosažené výsledky. Pomocí metod pro redukci dimenzionality je soubor příznaků analy-zován a výsledky jsou porovnány s výsledky ze standardní klasifikace. Řešení pro vizuali-zaci jak samotných nezpracovaných signálů, tak extrahovaných příznaků je navrhnuto aimplementováno. Dosažené výsledky jsou porovnány s publikovanými metodami.
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Sleep stages classification
Cikánek, Martin ; Mézl, Martin (referee) ; Potočňák, Tomáš (advisor)
The aim of this bachelor thesis was to elaborate a literature research on the topic of automatic classification of sleep stages from polysomnographic measurements and to subsequently select a way of feature extraction and quantitatively evaluate it. In the first part, the thesis deals mostly with the theory regarding the classification of sleep stages and analyzes the various possibilities of the process. This part is followed by a description of individual parts of the program, which is used for the extraction and subsequent quantitative evaluation of the features. The work is concluded by statistical evaluation of the results.
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Detection of interictal activity in long-term stereo-EEG recordings
Šikyňová, Soňa ; Králík, Martin (referee) ; Smital, Lukáš (advisor)
In patients with pharmacoresistant focal epilepsy, complete prevention of the clinical manifestation of epilepsy is achievable through surgical removal of the epileptic zone. Accurate localization of the epileptic zone relies on the detection of interictal epileptic discharges, which serve as an essential tool. However, the effectiveness of automated interictal epileptic discharge detectors may be influenced by the patient's state of vigilance. This study demonstrates a statistically significant difference in performance between different detectors during different sleep phases. The precise temporal and spatial distribution of interictal activity holds paramount importance for identifying the epileptic zone. Inaccurate determination of the epileptic zone and unsuccessful resection can occur if the sensitivity and accuracy of detectors vary across sleep stages. Consequently, it is crucial to consider the patient's consciousness status when pinpointing the epileptic zone. One approach to address these variations in detector performance at different states of vigilance is to optimize the parameters of detection algorithms. This study includes an investigation into the optimized parameters for two interictal epileptic discharge detectors.
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Methods for sleep spindles detection from EEG records
Matoušek, Šimon ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This bachelor work focuses on the detection of sleep spindles in EEG signals. The introductory chapter deals with the EEG signal, describes its components and describes the signal recording process. Explains the term sleep spindle and clarifies polysomnography. In the following chapter, some findings concerning studies that examined and practically used individual methods of sleep spindle detection are summarized in the form of research. The practical part of the work is focused on some sleep spindle detectors. At the end of the work is a comparison of the success of these detectors in comparison with other, previously performed studies. The highest success was achieved with the detector based on signal envelope calculation, where the sensitivity was 56.00 \% and the specificity 55.19 %, and also with the detector using wavelet transforms, where the sensitivity was 81.22 % and the specificity 46.15 %
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Novel methods for sleep analysis and classification
Navrátilová, Markéta ; Ronzhina, Marina (referee) ; Kolářová, Jana (advisor)
Tato diplomová práce se zabývá metodami pro analýzu a klasifikaci spánku. Popisuje jakjednotlivé spánkové fáze a vzorce biosignálů v průběhu spánku, tak metody pro klasifi-kaci. Příznaky jsou extrahovány na dodaných biosignálech ECG, EDA a RIP. Na základětěchto příznaků jsou klasifikovány jednotlivé spánkové fáze s využitím klasifikátoru ná-hodný les. Parametry klasifikátoru jsou optimalizovány a následně jsou vyhodnocenydosažené výsledky. Pomocí metod pro redukci dimenzionality je soubor příznaků analy-zován a výsledky jsou porovnány s výsledky ze standardní klasifikace. Řešení pro vizuali-zaci jak samotných nezpracovaných signálů, tak extrahovaných příznaků je navrhnuto aimplementováno. Dosažené výsledky jsou porovnány s publikovanými metodami.
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Polysomnographic data analysis
Jagošová, Petra ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
The bachelor´s thesis is focused on analysis of polysomnographic signals based on various parameters in time and frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EOG and EMG signals recorded during different sleep stages. The parameters useful for automatic detection of sleep stages are selected according to both visual analysis of boxplots and statistical analysis via comparison tests. EOG parameters selected in the time domain were mobility, skewness and kurtosis. Among EEG parameters, aktivity, 75. percentile, kurtosis and mobility were selected. Among EMG parameters, 75. percentile and complexity were selected. Finally, the parameters selected in the frequency domain were relative power spectra in alpha, delta and beta bands.
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Sleep scoring using artificial neural networks
Vašíčková, Zuzana ; Mézl, Martin (referee) ; Králík, Martin (advisor)
Hlavným cieľom semestrálnej práce je vytvorenie umelej neurónovej siete, ktorá bude schopná roztriediť spánok do spánkových epoch. Na začiatku je uvedené zhrnutie informácií o spánku a spánkových epochách. V ďalších kapitolách sa nachádza dôkladnejší prehľad metod na spracovávanie signálov a na klasifikáciu. Po zhrnutí teoretických znalostí potrebných na uskutočnenie praktickej časti práce boli na základe tohto rozboru vypočítané zo signálov potrebné znaky. Tieto znaky boli podrobené štatistickej analýze a na jej základe boli vybrané niektoré znaky, ktoré boli vhodné ako vstup do neurónovej siete, ktorá je po naučení schopná triediť spánkové epochy do príslušných fáz.
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Sleep stage classification
Lacinová, Michaela ; Smital, Lukáš (referee) ; Králík, Martin (advisor)
This bachelor thesis deals with analysis of polysomnography and its methods of measurement in electroencephalography, electromyography and electrooculography in the first part. It comprises an analysis of sleep stages recommended by the AASM. Polysomnographic data are further analysed in the domains of time and frequency, which are evaluated separately. In the second part the data are classified into particular classes using methods of decision trees and k-nearest neighbours in the MATLAB programming environment. These data are evaluated and compared with available literature.
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Sleep stages classification
Cikánek, Martin ; Mézl, Martin (referee) ; Potočňák, Tomáš (advisor)
The aim of this bachelor thesis was to elaborate a literature research on the topic of automatic classification of sleep stages from polysomnographic measurements and to subsequently select a way of feature extraction and quantitatively evaluate it. In the first part, the thesis deals mostly with the theory regarding the classification of sleep stages and analyzes the various possibilities of the process. This part is followed by a description of individual parts of the program, which is used for the extraction and subsequent quantitative evaluation of the features. The work is concluded by statistical evaluation of the results.
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