Automatic Sleep Arousals Detection From Polysomnography Using Multi-Convolution Neural Network and Random Forest

Abstract

Sleep arousals is a type of sleep disorder, which refers to the phenomenon of waking up and falling asleep again. Monitoring the number and duration of sleep arousals is a crucial aspect of sleep quality assessment. The detection of sleep arousals caused by apnea is relatively easy, and existing methods have been able to give high quality results. However, sleep arousals caused by non-apnea remains an ongoing challenge, and this is also the subject of PhysioNet Computing in Cardiology Challenge 2018. We proposed a non-apnea sleep arousals automatic detection algorithm based on polysomnography (PSG) data. We took 8 most representative signals selected from the 13 channels of PSG signals as input, conducted preliminary classification through multiple convolutional neural networks, and then sent the initial results to the random forest module for ensemble voting, and obtained the final judgment. We carried out some experiments using the CinC 2018 database. We grouped the original dataset reasonably, and based on each group of data, we trained a corresponding CNN, ensuring the balance of positive and negative samples during the training. Our 4-fold cross validation results for the AUROC and AUPRC were 0.953 and 0.552, which were better than the results of the team which ranked first in the CinC 2018.