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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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Study of the influence of acoustic stimuli on man
Schwanzer, Miroslav ; Kolář, Radim (referee) ; Ronzhina, Marina (advisor)
The thesis deals with EEG signals, their description, methods of quantitative analysis and the processes in time-frequency domains, or power spectrums. The relationsheep between brain electrical activity and acustic stimuli (Mozart´s “Sonata K448”) was studied using EEG analysis in relation to sound impulses from replayed extracts of. The proposed experiment protocol included recording of EEG of volunteers. In order to visualize and analyze the data, the software with the graphic user interface was created, which enables topological mapping of brain activity and its vizualization in the time-frequency domain.
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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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Study of the influence of acoustic stimuli on man
Schwanzer, Miroslav ; Kolář, Radim (referee) ; Ronzhina, Marina (advisor)
The thesis deals with EEG signals, their description, methods of quantitative analysis and the processes in time-frequency domains, or power spectrums. The relationsheep between brain electrical activity and acustic stimuli (Mozart´s “Sonata K448”) was studied using EEG analysis in relation to sound impulses from replayed extracts of. The proposed experiment protocol included recording of EEG of volunteers. In order to visualize and analyze the data, the software with the graphic user interface was created, which enables topological mapping of brain activity and its vizualization in the time-frequency domain.
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