1 research outputs found
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
The brain electrical activity presents several short events during sleep that
can be observed as distinctive micro-structures in the electroencephalogram
(EEG), such as sleep spindles and K-complexes. These events have been
associated with biological processes and neurological disorders, making them a
research topic in sleep medicine. However, manual detection limits their study
because it is time-consuming and affected by significant inter-expert
variability, motivating automatic approaches. We propose a deep learning
approach based on convolutional and recurrent neural networks for sleep EEG
event detection called Recurrent Event Detector (RED). RED uses one of two
input representations: a) the time-domain EEG signal, or b) a complex
spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT).
Unlike previous approaches, a fixed time window is avoided and temporal context
is integrated to better emulate the visual criteria of experts. When evaluated
on the MASS dataset, our detectors outperform the state of the art in both
sleep spindle and K-complex detection with a mean F1-score of at least 80.9%
and 82.6%, respectively. Although the CWT-domain model obtained a similar
performance than its time-domain counterpart, the former allows in principle a
more interpretable input representation due to the use of a spectrogram. The
proposed approach is event-agnostic and can be used directly to detect other
types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint
Conference on Neural Networks (IJCNN 2020