A deep learning architecture for temporal sleep stage classification\n using multivariate and multimodal time series

Sleep stage classification constitutes an important preliminary exam in the\ndiagnosis of sleep disorders. It is traditionally performed by a sleep expert\nwho assigns to each 30s of signal a sleep stage, based on the visual inspection\nof signals such as electroencephalograms (EEG), electrooculograms (EOG),\nelectrocardiograms (ECG) and electromyograms (EMG). We introduce here the first\ndeep learning approach for sleep stage classification that learns end-to-end\nwithout computing spectrograms or extracting hand-crafted features, that\nexploits all multivariate and multimodal Polysomnography (PSG) signals (EEG,\nEMG and EOG), and that can exploit the temporal context of each 30s window of\ndata. For each…

• AN
  Association Nationale de la Recherche et de la Technologie

  Award: 2015/1005