@inproceedings{Serrano Alarc{\´o}nGaidukMart{\´i}nez Madridetal.2024,
  author    = {Serrano Alarc{\´o}n, {\´A}ngel and Gaiduk, Maksym and Mart{\´i}nez Madrid, Natividad and Seepold, Ralf and Ortega, Juan},
  title     = {Classification of the sleep-wake state through the development of a deep learning model},
  booktitle = {Procedia computer science},
  volume    = {246},
  issn      = {1877-0509},
  doi       = {10.1016/j.procs.2024.09.328},
  institution = {Informatik},
  pages     = {4636 -- 4645},
  year      = {2024},
  abstract  = {The classification of sleep and wake states is of paramount importance in the context of sleep disorders. In order to detect and monitor disorders such as obstructive sleep apnea (OSA), it is essential to obtain the total sleep time (TST) so as to assess the severity of the patient's sleep apnea. With the advent of new technologies for detecting events associated with sleep disorders, it is not always straightforward to calculate the sleep/wakefulness state. Consequently, this work presents the development of a deep learning model (a variant of U-Net) for the detection of sleep/wakefulness states. For this purpose, an engineering approach using Keras Tuner and the use of three signals with minimal processing was employed. The three signals, oxygen saturation (SpO2), heart rate (HR) and abdominal respiratory effort (AbdRes), were selected to ensure both patient comfort during signal collection and the possibility of using portable monitors. The models were trained and tested on data from polysomnography studies, namely the Sleep Heart Health Study (SHHS) and the Multiethnic Study of Atherosclerosis (MESA). The best performing model achieved results with 88\% binary precision, 88\% recall, 89\% precision, 89\% f1-score and Cohen's Kappa of 0.74 for the SHHS test set. The model obtained 82\% binary accuracy, 82\% recall, 84\% precision, 82\% f1-score and 0.62 Cohen's kappa for the MESA data set.},
  language  = {de}
}