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Advanced sleep quality estimation
Benáček, Petr ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
This thesis deals with the assessment of sleep quality using modern deep learning methods. The thesis describes metrics for automatic classification of sleep stages. A selected database of sleep data is discussed. Due to the low number of data in the wakefulness phase, different methods of data augmentation are described and implemented. Models based on 1D convolutional networks are the basis for the classification. As a result, models for binary classification and classification of 3 and 4 sleep phases are prepared. Finally, sleep quality metrics are calculated using these models and the results are compared with the literature.
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Sleep quality assessment
Benáček, Petr ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
The topic of this bachelor thesis is an automatic sleep quality assessment using artificial neural network. To assess the quality of sleep were used the movement and heart rate data measured by Apple Watch smartwatch. From these data, statistical variables were calculated. Then were they used as an input to the neural networks. The goal was to automatically identify sleep and wakefulness. In this case, the sensitivity was 89 % and the specificity was 70 %. These values are comparable with other studies. Furthermore, the data were also divided into categories W (wakefulness), NON REM and REM. Parameters evaluating sleep quality, such as TST, % REM or sleep latency, were derived from the output of the neural networks created.
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Advanced sleep quality estimation
Benáček, Petr ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
This thesis deals with the assessment of sleep quality using modern deep learning methods. The thesis describes metrics for automatic classification of sleep stages. A selected database of sleep data is discussed. Due to the low number of data in the wakefulness phase, different methods of data augmentation are described and implemented. Models based on 1D convolutional networks are the basis for the classification. As a result, models for binary classification and classification of 3 and 4 sleep phases are prepared. Finally, sleep quality metrics are calculated using these models and the results are compared with the literature.
|
|
|
Sleep quality assessment
Benáček, Petr ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
The topic of this bachelor thesis is an automatic sleep quality assessment using artificial neural network. To assess the quality of sleep were used the movement and heart rate data measured by Apple Watch smartwatch. From these data, statistical variables were calculated. Then were they used as an input to the neural networks. The goal was to automatically identify sleep and wakefulness. In this case, the sensitivity was 89 % and the specificity was 70 %. These values are comparable with other studies. Furthermore, the data were also divided into categories W (wakefulness), NON REM and REM. Parameters evaluating sleep quality, such as TST, % REM or sleep latency, were derived from the output of the neural networks created.
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