Identification of Arousals With Deep Neural Networks (DNNs) Using Different Physiological Signals

Due to the increasing life pressure in modern society, more and more people are suffering from sleep disorders. The most serious case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. However, great obstacles still exist in automatic identification of arousals. In this study, a novel method was developed to detect non-apnea sources of arousals during sleep using several physiological signals. In the dataset provided, the duration of arousal regions is much less than that of non-arousal regions. In order to address this issue, a set of segments were extracted for model training in which arousal regions take up a much larger proportion than that in the original training set. After the preprocessing, a sequence-to-sequence deep neural networks (DNNs) which consists of a series of convolutional layers with residual connections, a long short-term memory (LSTM) layer and two fully connected layers, was trained to classify samples in the segments. Result shows that the area under receiver precision recall curve (AUPRC) is 0.43 in test dataset. In this study, an effective algorithm to detect non-apnea arousals was developed, which has great potentials in the clinical diagnosis and treatment of automatic sleep disturbance in the future.