|
|
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%.
|
|
|
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.
|
|
|
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.
|
|
|
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).
|
|
| |
|
|
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.
|
|
|
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.
|
|
|
Sleep stages classification
Cikánek, Martin ; Mézl, Martin (referee) ; Potočňák, Tomáš (advisor)
The aim of this bachelor thesis was to elaborate a literature research on the topic of automatic classification of sleep stages from polysomnographic measurements and to subsequently select a way of feature extraction and quantitatively evaluate it. In the first part, the thesis deals mostly with the theory regarding the classification of sleep stages and analyzes the various possibilities of the process. This part is followed by a description of individual parts of the program, which is used for the extraction and subsequent quantitative evaluation of the features. The work is concluded by statistical evaluation of the results.
|
|
|
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.
|
|
|
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...
|
guest ::
