Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks

10/21/2018

∙

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 sample of the hidden test set with an AUROC of 88.0

READ FULL TEXT

Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks

Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

LiteLSTM Architecture for Deep Recurrent Neural Networks

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network

Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks

SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks

End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks

Related Research

Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

LiteLSTM Architecture for Deep Recurrent Neural Networks

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network

Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks

SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks

End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables