Hybrid scattering-LSTM networks for automated detection of sleep arousals.

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Abstract

Early detection of sleep arousal in polysomnographic (PSG) signals is
 crucial for monitoring or diagnosing sleep disorders and reducing the risk of further
 complications, including heart disease and blood pressure fluctuations. In this paper,
 we present a new automatic detector of non-apnea arousal regions in multichannel
 PSG recordings. This detector cascades four different modules: a second-order
 scattering transform (ST) with Morlet wavelets; depthwise-separable convolutional
 layers; bidirectional long short-term memory (BiLSTM) layers; and dense layers. While
 the first two are shared across all channels, the latter two operate in a multichannel
 formulation. Following a deep learning paradigm, the whole architecture is trained in
 an end-to-end fashion in order to optimize two objectives: the detection of arousal onset
 and offset, and the classification of the type of arousal. The novelty of the approach
 is three-fold: it is the first use of a hybrid ST-BiLSTM network with biomedical
 signals; it captures frequency information lower (0.1 Hz) than the detection sampling
 rate (0.5 Hz); and it requires no explicit mechanism to overcome class imbalance in
 the data. In the follow-up phase of the 2018 Physionet/CinC Challenge the proposed
 architecture achieved a state-of-the-art area under the precision-recall curve (AUPRC)
 of 0.50 on the hidden test data, tied for the second-highest official result overall.
© 2019 Institute of Physics and Engineering in Medicine.