@inproceedings{Serrano Alarc{\´o}nMart{\´i}nez MadridSeepoldetal.2022,
  author    = {Serrano Alarc{\´o}n, {\´A}ngel and Mart{\´i}nez Madrid, Natividad and Seepold, Ralf and Ortega, Juan Antonio},
  title     = {Apnea-hypopnea index using deep learning models with whole and window-based time series},
  booktitle = {Hardware and software supporting physiological measurement (HSPM-2022)},
  isbn      = {978-3-00-074291-0},
  doi       = {10.34645/opus-3991},
  institution = {Informatik},
  pages     = {13 -- 15},
  year      = {2022},
  abstract  = {Today many scientific works are using deep learning algorithms and time series, which can detect physiological events of interest. In sleep medicine, this is particularly relevant in detecting sleep apnea, specifically in detecting obstructive sleep apnea events. Deep learning algorithms with different architectures are used to achieve decent results in accuracy, sensitivity, etc. Although there are models that can reliably determine apnea and hypopnea events, another essential aspect to consider is the explainability of these models, i.e., why a model makes a particular decision. Another critical factor is how these deep learning models determine how severe obstructive sleep apnea is in patients based on the apnea-hypopnea index (AHI). Deep learning models trained by two approaches for AHI determination are exposed in this work. Approaches vary depending on the data format the models are fed: full-time series and window-based time series.},
  language  = {en}
}