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Detection of K-complexes in sleep EEG signals
Hlaváčová, Kristýna ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This master’s thesis deals with issues of the detection of K-complexes in EEG sleep signals. Record from an electroencephalograph is important for non-invasive diagnosis and research of brain activity. The scanned signal is used to examine sleep phases, disturbances, states of consciousness and the effects of various substances. This work follows the automatic detection of K-complexes, because the manual labeling of graphoelements is complicated. Two approaches were used –Stockwell transform and bandpass filtration followed by TKEO operator application. All algorithms were created in the MATLAB R2014a.
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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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Microstates analysis in EEG data of sleep-deprived subjects
Křápková, Monika ; Koudelka, Vlastimil (referee) ; Lamoš, Martin (advisor)
This bachelor’s thesis deals with the processing and analysis of EEG data in sleep deprived subjects. In the theoretical part, the electroencephalography method is presented first. Further, there are possibilities of preprocessing and analysis of EEG data, introduction to statistics, and the last one is a research on the influence of sleep deprivation on human electrophysiology. The practical part consists of the preprocessing of EEG data, EEG microstates analysis and statistical evaluation of the results from the study of sleep deprivation. Finally, the results of this part are discussed in a separate chapter.
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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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The EEG segmentation
Nečadová, Anežka ; Kozumplík, Jiří (referee) ; Kubicová, Vladimíra (advisor)
Subject of this bachelor project is the introduction of the EEG signal. Are discussed his characteristics, application and methods of processing. The main part deals with the segmentation of the EEG signal. Two methods are implemented in program Matlab - adaptive segmentation based on differential average amplitude and differential average frequency and adaptive segmentation based on differential estimated based on FFT. Functionality of algorithms is verified on real EEG signals.
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Automated EEG data segmentation
Krupka, Ondřej ; Ronzhina, Marina (referee) ; Bubník, Karel (advisor)
This bachelor's thesis deals with EEG signal, its properties, usage and its processing methods. The main task is introduction with different methods of automatic EEG data segmentation. Furthermore the subject of this project is realization of some methods in MATLAB software, verification of functionality and mutual comparison of segmentation results.
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The EEG segmentation
Nečadová, Anežka ; Kozumplík, Jiří (referee) ; Kubicová, Vladimíra (advisor)
Subject of this bachelor project is the introduction of the EEG signal. Are discussed his characteristics, application and methods of processing. The main part deals with the segmentation of the EEG signal. Two methods are implemented in program Matlab - adaptive segmentation based on differential average amplitude and differential average frequency and adaptive segmentation based on differential estimated based on FFT. Functionality of algorithms is verified on real EEG signals.
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Study of EEG signal in experiment with flicker-fusion test
Malá, Aneta ; Chmelař, Milan (referee) ; Ronzhina, Marina (advisor)
Quick and easy detection of vigilance level is very important in practise. Questionnaires, psychological tests and analysis of biological signals are usually used for determination of awakening level. This work deals with studying of possibilities of using the flicker-fusion test (FF test) and electroencephalogram (EEG) analysis for determination of vigilance level. First part of thesis defines terms, second part represents brief introduction of EEG and third part is about FF test. Last part is about realization of spectral analysis of EEG using MATLAB. Method for EEG and FF test parameters assesment is also proposed in this part.
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Measurement and analysis of electroencephalogram
Klus, Michal ; Chmelař, Milan (referee) ; Kubicová, Vladimíra (advisor)
The first theoretic part studies the origin and history of electroencephalography. It describes the basic part of the brain, neuron, from anatomical to functional sides. It discusses formation of electrical potential. It continues on overview and description of alternative diagnostic methods used for brain testing and testing of brain electrical activity. Describes the types of using electrodes and their location. Finish of theoretic part shows the descritpion of EEG record and characteristics of types of waves. The practical part shows the attempt to measurement. Procedure of correct measurement and capture the dates. Describes the transferring data into right format drawing EEG spectral analysis and brain mapping. All of this is included in the generated program analyza_EEG The end of practical part describes the outputs from progmam using own measurement data.
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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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