Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks
This work addresses sleep disorder diagnosis by improving arousal detection, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of detecting sleep arousals from polysomnography data using a signal-processing and machine learning approach, achieving an AUROC of 88.0% and an AUPRC of 42.1% on a test set.
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%.