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Sleep stage classification based on HRV signals
Schlorová, Hana ; Kubičková, Alena (referee) ; Kozumplík, Jiří (advisor)
The most common method for scoring of sleep stages is the evaluation by EEG. This work utilizes ECG signal to the comparable evaluation of sleep. It summarizes the methods of presentation and assessment of heart rate variability (HRV) and describes the whole algorithm of calculation and presentation of this signal using Lorenz plot. This work also focuses on evaluation of Lorentz plots and parametrs quantifying variability of samples in maps. It seeks to draw the conclusion of sleep stages from their waveform.
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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 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.
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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.
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|
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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 stage classification based on HRV signals
Schlorová, Hana ; Kubičková, Alena (referee) ; Kozumplík, Jiří (advisor)
The most common method for scoring of sleep stages is the evaluation by EEG. This work utilizes ECG signal to the comparable evaluation of sleep. It summarizes the methods of presentation and assessment of heart rate variability (HRV) and describes the whole algorithm of calculation and presentation of this signal using Lorenz plot. This work also focuses on evaluation of Lorentz plots and parametrs quantifying variability of samples in maps. It seeks to draw the conclusion of sleep stages from their waveform.
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