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Sleep scoring using EEG
Holdova, Kamila ; Smital, Lukáš (referee) ; Ronzhina, Marina (advisor)
This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.
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Automatic sleep scoring
Schwanzer, Miroslav ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.
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Automatic detection of graphoelements in sleep EEG
Balcarová, Anežka ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This project is aimed at sleeping EEG signal, especially at searching of sleeping graphoelements and next at processing signal, witch this segmentation go before. Charakterization of sleeping graphoelements and problems with their classification are outlined here. Principle of two detection methods of k-komplex are explained and processed by Matlab with graphically representation of results. Results of automatic classification are compared with scoring of two experts.
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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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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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Sleep dynamics analysis using electrophysiological features
Lampert, Frederik ; Janoušek, Oto (referee) ; Mívalt, Filip (advisor)
Táto práca sa zaoberá metódami detekcie spánku pomocou elektrofiziologických príznakov, bez dostupnosti anotácií v podobe hypnogramov. Vyhodnocovanie spánku pomocou polysomnografických (PSG) dát je časovo apersonálne náročný proces, ktorý sa odohráva zväčša vnemocničnom prostredí. Moderné implantabilné zariadenia schopné kontinuálneho snímania azdieľania dát otvárajú možnosť dlhodobého akontinuálneho monitorovania spánkovej aktivity upacientov snurologickými chorobami v domácom prostredí. Súčasné metódy na spánkovú detekciu využívajú intrakraniálne elektroencefalografické (iEEG) spánkové klasifikátory, ktoré sú tvorené a validované na štandardných polysomnografických dátach. Tie však nie sú vždy kdispozícii čo vedie kpotrebe vyvinutia metódy spánkového hodnotenia, ktorá by bola schopná analyzovať spánok pomocou elektrofyziologických príznakov aj bez dostupnosti anotácií v podobe hypnogramov ato obecne z malého počtu zvodov. Za týmto účelom bola navrhnutá metóda spánkového hodnotenia, ktorá vyhodnocuje spánok na základe výkonu elektroencefalografické (EEG) signálu v spektrálnej oblasti pomocou metrík zvaných Power in Band (PIB) metriky. V tejto práci bol analyzovaný výkon vdelta pásme (0,5-4 Hz), keďže EEG signál má vňom najvyššiu amplitúdu azároveň je jeho aktivita najviac výrazná počas N2 aN3 spánkových cyklov, ktoré sú najviac zastúpené vspánku počas noci, takže poskytujú najlepšiu informáciu orozložení spánku počas noci. Scieľom validácie PIB metrík boli taktiež predstavené štandardné metriky založené na hypnogramoch. Tieto metriky boli následne implementované do programovacieho prostredia Python a aplikované na dva voľne dostupné datasety, Dreem Open Dataset-Healthy (DOD-H) a Dreem Open Dataset-Obstructive (DOD-O) obsahujúce polysomnografické merania 25 zdravých jedincov (DOD-H dataset) a56 jedincov so syndrómom spánkového apnoe (OSA)(DOD-O dataset). Výsledky analýz boli vyhodnotené pomocou vizuálnej analýzy vo forme boxplotov, korelačných matíc a štatistických testov. Z výsledkov analýz vyplýva, že navrhnuté PIB metriky majú schopnosť rozlišovať medzi fyziologickým a patofyziologickým spánkom, avšak ich schopnosť rozlišovať niektoré aspekty spánku sa líši od štandardných metrík založených na hypnogramoch. Ztoho vyplýva, že PIB metriky nenahrádzajú štandardné metriky, ale skôr ponúkajú inú perspektívu na analýzu spánku.
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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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Automatic sleep scoring
Schwanzer, Miroslav ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.
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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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Automatic detection of graphoelements in sleep EEG
Balcarová, Anežka ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This project is aimed at sleeping EEG signal, especially at searching of sleeping graphoelements and next at processing signal, witch this segmentation go before. Charakterization of sleeping graphoelements and problems with their classification are outlined here. Principle of two detection methods of k-komplex are explained and processed by Matlab with graphically representation of results. Results of automatic classification are compared with scoring of two experts.
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