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Sleep apnea detection from ECG records
Trnková, Simona ; Smital, Lukáš (referee) ; Králík, Martin (advisor)
The aim of this work is to propose an algorithm for the detection of sleep apnea from the respiratory signal that was extracted from the ECG signal. The work includes the theory of sleep medicine, methods for extracting the respiratory curve from the ECG signal and methods for detecting apnoeic sections. In the practical part, the algorithm for estimating the breathing curve and models classifying apnoeic sections are described and evaluated.
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REM sleep behavior disorder:Characteristics of polysomnographic and behavioral manifestations.
Nepožitek, Jiří ; Šonka, Karel (advisor) ; Bušková, Jitka (referee) ; Marusič, Petr (referee)
REM sleep behavior disorder: Characteristics of polysomnographic and behavioral manifestations Abstract REM sleep behavior disorder (RBD) is a disease characterized by abnormal motor activity corresponding to the dream content. REM sleep without atonia (RWA) and behavioral manifestations are the main features registered by video-polysomnography (PSG). Because idiopathic RBD (iRBD) is considered as prodromal stage of synucleinopathies, the direction of current research is the search for markers of early conversion. The goal of this study was to observe the group of patients with iRBD with regard to the development of manifest neurodegenerative disease, to find and test a new polysomnographic marker of phenoconversion, to perform analysis of the movements registered by video and to quantify excessive fragmentary myoclonus (EFM), which is a frequent finding in neurodegenerative processes. A total of 55 patients with iRBD were observed for 2.3±0.7 years. The annual conversion rate was 5.5%. Mixed RWA, representing simultaneous occurrence of phasic and tonic RWA, was suggested as a new marker of phenoconversion. Converted patients showed a higher mixed RWA (p=0.009) and the ROC analysis confirmed that mixed RWA is the best predictive marker of conversion among other RWA types (AUC 0.778). An average of...
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Identification Of Sleep/Wake Stages In Actigraphy Data Utilising Gradient Boosting Algorithm
Mikulec, Marek
Sleep disorders are early markers of various serious diseases that can be treated moreeffectively when diagnosed in their prodromal stage. Actigraphy is a noninvasive sleep monitoringmethod for the detection of sleep patterns and determination of sleep parameters that could support thediagnosis of these disorders. This study aims to compare a newly proposed actigraphy-based methodof sleep/wake detection with a conventional one in terms of consistency with a polysomnography(PSG) reference. 55 recordings (acquired in 28 subjects) of actigraphy and PSG were modelled by aheuristics-based method and by a new approach utilising a gradient boosting algorithm. In addition,another database (22 subjects, 150 recordings) was used to compare scores of the new method withdata reported in sleep diaries. The proposed method achieves 89% accuracy and Mathews correlationcoefficient equal to 0.75 when compared to the polysomnography reference. Such results outperformthe ones provided by the heuristic technique. The newly proposed method has good consistency withthe PSG reference, thus being a good alternative to the golden standard in sleep disorders assessment,especially in decentralised clinical trials.
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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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REM sleep behavior disorder:Characteristics of polysomnographic and behavioral manifestations.
Nepožitek, Jiří ; Šonka, Karel (advisor) ; Bušková, Jitka (referee) ; Marusič, Petr (referee)
REM sleep behavior disorder: Characteristics of polysomnographic and behavioral manifestations Abstract REM sleep behavior disorder (RBD) is a disease characterized by abnormal motor activity corresponding to the dream content. REM sleep without atonia (RWA) and behavioral manifestations are the main features registered by video-polysomnography (PSG). Because idiopathic RBD (iRBD) is considered as prodromal stage of synucleinopathies, the direction of current research is the search for markers of early conversion. The goal of this study was to observe the group of patients with iRBD with regard to the development of manifest neurodegenerative disease, to find and test a new polysomnographic marker of phenoconversion, to perform analysis of the movements registered by video and to quantify excessive fragmentary myoclonus (EFM), which is a frequent finding in neurodegenerative processes. A total of 55 patients with iRBD were observed for 2.3±0.7 years. The annual conversion rate was 5.5%. Mixed RWA, representing simultaneous occurrence of phasic and tonic RWA, was suggested as a new marker of phenoconversion. Converted patients showed a higher mixed RWA (p=0.009) and the ROC analysis confirmed that mixed RWA is the best predictive marker of conversion among other RWA types (AUC 0.778). An average of...
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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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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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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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