  Sleep disorders are implicated in a growing number of health problems. In
this paper, we present a signal-processing/machine learning approach to
detecting arousals in the multi-channel polysomnographic recordings of the
Physionet/CinC Challenge2018 dataset.
  Methods: Our network architecture consists of two components. Inputs were
presented to a Scattering Transform (ST) representation layer which fed a
recurrent neural network for sequence learning using three layers of Long
Short-Term Memory (LSTM). The STs were calculated for each signal with
downsampling parameters chosen to give approximately 1 s time resolution,
resulting in an eighteen-fold data reduction. The LSTM layers then operated at
this downsampled rate.
  Results: The proposed approach detected arousal regions on the 10% random
sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%.
