Title: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series,
Abstract: Sleep stage classification constitutes an important preliminary exam in the
diagnosis of sleep disorders. It is traditionally performed by a sleep expert
who assigns to each 30s of signal a sleep stage, based on the visual inspection
of signals such as electroencephalograms (EEG), electrooculograms (EOG),
electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first
deep learning approach for sleep stage classification that learns end-to-end
without computing spectrograms or extracting hand-crafted features, that
exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG,
EMG and EOG), and that can exploit the temporal context of each 30s window of
data. For each modality the first layer learns linear spatial filters that
exploit the array of sensors to increase the signal-to-noise ratio, and the
last layer feeds the learnt representation to a softmax classifier. Our model
is compared to alternative automatic approaches based on convolutional networks
or decisions trees. Results obtained on 61 publicly available PSG records with
up to 20 EEG channels demonstrate that our network architecture yields
state-of-the-art performance. Our study reveals a number of insights on the
spatio-temporal distribution of the signal of interest: a good trade-off for
optimal classification performance measured with balanced accuracy is to use 6
EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one
minute of data before and after each data segment offers the strongest
improvement when a limited number of channels is available. As sleep experts,
our system exploits the multivariate and multimodal nature of PSG signals in
order to deliver state-of-the-art classification performance with a small
computational cost.