A Convolutional Network for the Classification of Sleep Stages†
AbstractThe classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole night sleep study can take several hours. Most of the automatic methods trying to solve this problem use human engineered features biased for a specific dataset. In this work we use deep learning to avoid human bias. We propose an ensemble of 5 convolutional networks achieving a kappa index of 0.83 when classifying 500 sleep studies.
Share & Cite This Article
Fernández-Varela, I.; Hernández-Pereira, E.; Moret-Bonillo, V. A Convolutional Network for the Classification of Sleep Stages. Proceedings 2018, 2, 1174.
Fernández-Varela I, Hernández-Pereira E, Moret-Bonillo V. A Convolutional Network for the Classification of Sleep Stages. Proceedings. 2018; 2(18):1174.Chicago/Turabian Style
Fernández-Varela, Isaac; Hernández-Pereira, Elena; Moret-Bonillo, Vicente. 2018. "A Convolutional Network for the Classification of Sleep Stages." Proceedings 2, no. 18: 1174.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.