  Background: Electroencephalography (EEG) monitors brain activity during sleep
and is used to identify sleep disorders. In sleep medicine, clinicians
interpret raw EEG signals in so-called sleep stages, which are assigned by
experts to every 30s window of signal. For diagnosis, they also rely on shorter
prototypical micro-architecture events which exhibit variable durations and
shapes, such as spindles, K-complexes or arousals. Annotating such events is
traditionally performed by a trained sleep expert, making the process time
consuming, tedious and subject to inter-scorer variability. To automate this
procedure, various methods have been developed, yet these are event-specific
and rely on the extraction of hand-crafted features.
  New method: We propose a novel deep learning architecure called Dreem One
Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and
types of events in EEG time series. The proposed approach, applied here on
sleep related micro-architecture events, is inspired by object detectors
developed for computer vision such as YOLO and SSD. It relies on a
convolutional neural network that builds a feature representation from raw EEG
signals, as well as two modules performing localization and classification
respectively.
  Results and comparison with other methods: The proposed approach is tested on
4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared
to the current state-of-the-art detection algorithms.
  Conclusions: Results demonstrate the versatility of this new approach and
improved performance compared to the current state-of-the-art detection
methods.
