  Electroencephalography (EEG) during sleep is used by clinicians to evaluate
various neurological disorders. In sleep medicine, it is relevant to detect
macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as
spindles and K-complexes. Annotations of such events require a trained sleep
expert, a time consuming and tedious process with a large inter-scorer
variability. Automatic algorithms have been developed to detect various types
of events but these are event-specific. We propose a deep learning method that
jointly predicts locations, durations and types of events in EEG time series.
It relies on a convolutional neural network that builds a feature
representation from raw EEG signals. Numerical experiments demonstrate
efficiency of this new approach on various event detection tasks compared to
current state-of-the-art, event specific, algorithms.
