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Effect of acute sleep deprivation in different light conditions on the quality of recovery sleep
Zeithamlová, Barbora ; Kopřivová, Jana (advisor) ; Vlček, Kamil (referee)
Sleep is regulated by homeostatic and circadian processes. The circadian process is controlled by the internal biological clock, which is regularly synchronised with the external world by so-called zeitgebers. The most important zeitgeber for humans is light, therefore incorrect timing of light signals can lead to desynchronisation of the clock and sleep disruption; however, this depends on the intensity and spectral characteristics of the light. Dimmed red light is unlikely to significantly interfere with sleep regulation, but white light with a higher intensity could. White light is typically used when people stay awake during the night and experience acute total sleep deprivation. This could potentially interfere with the compensatory mechanisms occurring during subsequent recovery sleep. We therefore decided to test whether and how different lighting conditions during sleep deprivation can affect the structure of recovery sleep. We had 12 healthy uniform volunteers undergo two acute total sleep deprivations; one under normal white light, the other under dim red light. Using polysomnography, we measured the sleep parameters of both recovery sleeps and compared them. It turned out that sleep that occurred after the sleep deprivation in constant dim light conditions was less fragmented, had...
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Application of deep learning in sleep apnea detection
Láznička, Jakub ; Šaclová, Lucie (referee) ; Králík, Martin (advisor)
The master thesis focuses on the use of deep learning methods for the detection of sleep apnea, a sleep disorder characterized by repeated episodes of cessation or significant reduction in airway flow during sleep. The study investigates the effectiveness of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models in the automatic detection of different types of sleep apnea using polysomnographic recordings. The datasets used in this work are from the MESA database, which have been specially prepared and modified for deep learning. The best performing models achieved F1-scores of 0.87 and 0.83, showing that deep learning can provide accurate tools for sleep apnea diagnosis, representing a potential improvement in clinical practice. The paper also discusses the possibilities of integrating these models into clinical diagnostic processes and outlines directions for future research in this area.
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Classification of sleep phases using polysomnographic data
Králík, Martin ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
Aim of this thesis is the classification of polysomnographic data. The first part of the thesis is a review of mentioned topic and also the statistical analysis of classification features calculated from real EEG, EOG and EMG for evaluating of the features suitability for sleep stages scoring. The second part is focused on the automatic classification of the data using artificial neural networks. All the results are presented and discussed.
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Advanced scoring of sleep data
Jagošová, Petra ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
The master´s thesis is focused on advanced scoring of sleep data, which was performed using deep neural network. Heart rate data and the movement information were used for scoring measured using an Apple Watch smartwatch. After appropriate pre-processing, this data serves as input parameters to the designed networks. The goal of the LSTM network was to classify data into either two groups for sleep and wake or into three groups for wake, Non-REM and REM. The best results were achieved by network doing classification of sleep vs. wake using the accelerometer. The statistical evaluation of this best-designed network reached the values of sensitivity 71,06 %, specificity 57,05 %, accuracy 70,01 % and F1 score 81,42 %.
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Detection of sleep apnea from polysomnographic signals
Vecheta, Miroslav ; Potočňák, Tomáš (referee) ; Kozumplík, Jiří (advisor)
This thesis deals with the detection of sleep apnea using polysomnographic data and attempt to find a possible alternative and simpler method of this detection. The thesis consists of three parts: The first part is important for introduction to the lungs anatomy and the physiology of breathing and the sleep phisiology. The second part deals with the ways of testing sleep apnea. The third part then continues with implementation of alternative methods of testing in Matlab software. The final program calculates the breathing curve from ECG data. The curve is important for the final detection of sleep apnea.
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Obstructive sleep apnea detection using polysomnography
Smrčková, Markéta ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This thesis attempts to find an alternative method for automatic detection of sleep apnea using polysomnographic data. The first part is focused on introduction to lungs anatomy and physiology of breathing, sleeping and cardiac system. The second part describes the process of sleep examination and particular components of polysomnographic data. The third part is focused on realization of specific method for sleep apnoea detection, application on real data and results evalutation.
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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 apnea detection
Hastík, Matěj ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
This master‘s thesis deals with a detailed description of sleep apnea and methods of detection of sleep apnea. The first part of the work is focused on the physiology of sleep, sleep apnea itself, its distribution, symptoms, risk factors and treatment. The next part of the work deals with polysomnographic examination and methods for analysis of polysomnographic data. The last part is devoted to the procedure design for detecting sleep apnea by using only one kind of signal and by using more kinds of signals, implementation of these proposals, their testing on real data, evaluating the detection performance and comparing the results with data available in the literature.
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Sleep EEG analysis
Vávrová, Eva ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
The bachelor´s thesis is focused on analysis of sleep electroencephalograms based on extraction of chosen parameters in time and frequency domain. The parameters are acquired from segments of EEG signals coincident with sleep stages. The parameters used for automatic detection of sleep stages are selected according to statistical analysis. The program with a graphical user interface for selection, display and analysis EEG was created using Matlab.
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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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