  In this work, a dense recurrent convolutional neural network (DRCNN) was
constructed to detect sleep disorders including arousal, apnea and hypopnea
using 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 and hypopnea are
manually annotated by experts which enables us to train our proposed network
using a multi-task learning mechanism. Three binary cross-entropy loss
functions corresponding to sleep/wake, target arousal and apnea-hypopnea/normal
detection tasks 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,
our model was applied to full night recording data. Finally, the average AUPRC
and AUROC values associated with the arousal detection task were 0.505 and
0.922, respectively on our testing dataset. An ensemble of four models trained
on different data folds improved the AUPRC and AUROC to 0.543 and 0.931,
respectively. Our proposed algorithm achieved the first place in the official
stage of the 2018 Physionet challenge for detecting sleep arousals with AUPRC
of 0.54 on the blind testing dataset.
