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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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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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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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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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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 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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Analysis of sleep EEG signal
Ježek, Martin ; Kozumplík, Jiří (referee) ; Rozman, Jiří (advisor)
Cílem této práce byl vývoj programu pro automatickou detekci arousalu v signálu spánkového EEG s použitím metod časově-frekvenční analýzy. Předmětem studie bylo 13 celonočních polysomnografických nahrávek (čtyři svody EEG, EMG, EKG a EOG), tj. celkově více než 100 hodin záznamu. Jednalo se o část dat z dřívějších výzkumných prací expertní lékařky v problematice spánku Dr. Emilie Sforzy, Ženeva, Švýcarsko, která rovněž poskytla základní hodnocení těchto dat. V záznamech bylo celkem označeno 1551 arousal událostí. Pro usnadnění výběru konkrétní metody časově-frekvenční analýzy byla následně vytvořena sada nástrojů pro vizualizaci jednotlivých signálů a jejich různých časově-frekvenčních vyjádření. S ohledem na závěry vizuální analýzy, charakter signálu EEG a efektivitu výpočetních metod byla pro analýzu vybrána waveletová transformace s mateřskou vlnkou Daubechies řádu 6. Jednotlivé svody EEG byly dekomponovány do šesti frekvenčních pásem. Z takto odvozených signálů a signálu EMG byly následně stanoveny ukazatele možné přítomnosti události arousalu. Tyto ukazatele byly dále váhovány lineárním klasifikátorem, jehož hodnoty vah byly optimalizovány pomocí genetického algoritmu. Na základě hodnoty lineárního klasifikátoru bylo rozhodnuto o přítomnosti události arousalu v daném svodě EEG – arousal byl detekován, jestliže hodnota klasifikátoru překročila danou mez na dobu více než 3 a méně než 30 vteřin. V celém záznamu pak byl arousal označen, byl-li detekován alespoň v jednom ze svodů EEG. Následně byly odvozeny míry senzitivity a selektivity detekce, jež byly rovněž základem pro stanovení fitness funkce genetického algoritmu. Pro učení genetického algoritmu byly vybrány první čtyři záznamy. Na základě takto optimalizovaných vah vznikl program pro automatickou detekci, který na celém souboru 13 záznamů dosáhl ve srovnání s expertním hodnocením míry senzitivity 76,09%, selektivity 53,26% a specificity 97,66%.
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PSG-Based Classification of Sleep Phases
Králík, M.
This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.
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Multidisciplinary collaboration and the role nurse in the care of patients with sleep apnea syndrome
MATĚJKOVÁ, Naděžda
Sleep belongs to basic human needs and it is not only its length but also its quality that matters. Sleep disorders cause a lot of illnesses and therefore it is important to examine them and to treat them What are the most common symptoms of the ill state? somnolence, i. e. sleepiness during the day, disturbed or interrupted night sleep, or its lack. All these symptoms are necessary to be focused on. Sleep disorders are classified according to the International Classification of Sleep disorders ICSD 3 of 2014. This work deals with the problem theoretically. The first aim of this work was clarifying basic concepts of the problem - the syndrome called sleep apnoea. The next one was focusing on the multidisciplinary approach to the patients with this syndrome, so typical and very important. The main focus is upon the sleep apnoea syndrome, the patients suffering from it and multidisciplinary collaboration during their treatment. The main focus is upon the sleep apnoea syndrome, the patients suffering from it and multidisciplinary collaboration during their treatment. After having studied both Czech and foreign sources thouroughly, the general task of this work became summarising all the results of the research in one study . One part of this work is devoted to the detailed medical examination of sleep and its disorders. It describes polygraphic and polysomnographic tests, the procedure of the testing, the interpretation of the results, also the methods of the treatment with positive airway pressure during the sleep currently considered to be the most effective method of treatment of the adult and child patients. Next part of the work which focuses on multidisciplinary approach to the patients suffering from sleep apnoea describes surgical possibilities of the treatment within otolaryngology and methods of treatment within pulmanology. The last part of the work concentrates on the work of the nurse. It deals with the competences and further education of the nurses working for sleep laboratories. Prominent medical journals have become the key sources of information and the latest knowledge of sleep medicine.
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