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.
@article{arxiv.1807.05981,
title = {A deep learning architecture to detect events in EEG signals during sleep},
author = {Stanislas Chambon and Valentin Thorey and Pierrick J. Arnal and Emmanuel Mignot and Alexandre Gramfort},
journal= {arXiv preprint arXiv:1807.05981},
year = {2018}
}