Automatic Detection of Target Regions of Respiratory Effort-Related Arousals Using Recurrent Neural Networks

Abstract

We present a method for classifying target sleep arousal regions of polysomnographies. Time- and frequency-domain features of clinical and statistical origins were derived from the polysomnography signals and the features fed into a Bidirectional Recurrent Neural Network, using Long Short-Term Memory units (BRNN-LSTM). The predictions of five recurrent neural networks, trained using different features and training sets, were averaged for each sample, to yield a more robust classifier. The proposed method was developed and validated on the PhysioNet Challenge dataset which consisted of a training set of 994 subjects and a hidden test set of 989 subjects. Five-fold cross-validation on the training set resulted in an area under precision-recall curve (AUPRC) score of 0.452, an area under receiver operating characteristic curve (AUROC) score of 0.901 and intraclass correlation ICC(2,1) of 0.59. The classifier was further validated on the PhysioNet Challenge test set, resulting in an AUPRC score of 0.45.