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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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Sleep stage classification based on HRV signals
Schlorová, Hana ; Kubičková, Alena (referee) ; Kozumplík, Jiří (advisor)
The most common method for scoring of sleep stages is the evaluation by EEG. This work utilizes ECG signal to the comparable evaluation of sleep. It summarizes the methods of presentation and assessment of heart rate variability (HRV) and describes the whole algorithm of calculation and presentation of this signal using Lorenz plot. This work also focuses on evaluation of Lorentz plots and parametrs quantifying variability of samples in maps. It seeks to draw the conclusion of sleep stages from their waveform.
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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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Automatic sleep scoring using polysomnographic data
Vávrová, Eva ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
The thesis is focused on analysis of polysomnographic signals based on extraction of chosen parameters in time, frequency and time-frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. The classification is realized by artificial neural networks, k-NN classifier and linear discriminant analysis. The program with a graphical user interface was created using Matlab.
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Automatic sleep scoring using polysomnographic data
Kříženecká, Tereza ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
The thesis is focused on automatic classification of polysomnographic signals based on various parameters in time and frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. Classification is performed using the SVM method and evaluation of the success of the classification is done using sensitivity, specificity and percentage success. Classification method was implemented using Matlab.
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Sleep stages classification
Nováková, Kateřina ; Ronzhina, Marina (referee) ; Potočňák, Tomáš (advisor)
This work deals with the basic description of polysomnography, sleep morphology and sleep stages. Furtherly, some methods to process electroencephalographic signals are mentioned. Those processing methods are mainly focused on sleep stage classification. The practical part deals with the realization of three classification algorithms using artificial neural networks and verifying the functionality of these methods. All algorithms are designed in Matlab. Feature vectors for individual methods are obtained using energy values, Welch's spectral analysis and Hilbert-Huang Transform. For classification three types of artificial neural networks were used - layer recurrent network, feedforward network and pattern recognition network. On the basis of feature vectors, the sleep is divided into three stages - wakefulness (W), sleep without rapid eye movements (NREM) and sleep with rapid eye movements (REM).
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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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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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