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In this work, a dense recurrent convolutional neural network (DRCNN) wasconstructed to detect sleep disorders including arousal, apnea and hypopneausing Polysomnography (PSG) measurement channels provided in the 2018 Physionetchallenge database. Our model structure is composed of multiple denseconvolutional units (DCU) followed by a bidirectional long-short term memory(LSTM) layer followed by a softmax output layer. The sleep events includingsleep stages, arousal regions and multiple types of apnea and hypopnea aremanually annotated by experts which enables us to train our proposed networkusing a multi-task learning mechanism. Three binary cross-entropy lossfunctions corresponding to sleep/wake, target arousal and apnea-hypopnea/normaldetection tasks are summed up to generate our overall network loss functionthat is optimized using the Adam method. Our model performance was evaluatedusing two metrics: the area under the precision-recall curve (AUPRC) and thearea under the receiver operating characteristic curve (AUROC). To measure ourmodel generalization, 4-fold cross-validation was also performed. For training,our model was applied to full night recording data. Finally, the average AUPRCand AUROC values associated with the arousal detection task were 0.505 and0.922, respectively on our testing dataset. An ensemble of four models trainedon different data folds improved the AUPRC and AUROC to 0.543 and 0.931,respectively. Our proposed algorithm achieved the first place in the officialstage of the 2018 Physionet challenge for detecting sleep arousals with AUPRCof 0.54 on the blind testing dataset.

SleepNet: Automated Sleep Analysis via Dense Convolutional Neural Network Using Physiological Time Series