Automated Detection of Sleep Arousals from Polysomnography Data Using a Dense Convolutional Neural Network

Abstract

In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep arousals using available Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database. Our model structure is composed of multiple dense convolutional units (DCU) followed by a bidirectional long-short term memory (LSTM) layer followed by a softmax output layer. The sleep events including sleep stages, arousal regions and multiple types of apnea-hypopnea/normal are manually annotated in 2018 Physionet challenge database which enable us to train our proposed network using a multi-task learning mechanism. Three binary cross-entropy loss functions corresponding to sleep/wake, arousal presence/absence and apnea/hypopnea presence/absence detections are summed up to generate our overall network loss function that is optimized using the Adam method. Our model performance was evaluated using two metrics: the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). To measure our model generalization, 4-fold cross-validation was also performed. For training, full night recording data was applied to our model. Finally, our proposed algorithm achieves the first place in the official stage of the Physionet challenge with AUPRC of 0.54 on the blind testing dataset.