Automatic Sleep Arousal Detection Based on C-ELM

Abstract

Sleep arousals are sudden awakenings from sleep which can be identified as an abrupt shift in EEG frequency and can be manually scored from various physiological signals by sleep experts. Frequent sleep arousals can degrade sleep quality, result in sleep fragmentation and lead to daytime sleepiness. Visual inspection of arousal events from PSG recordings is time consuming and cumbersome, and manual scoring results can vary widely among different expert scorers. This paper reports the design and performance evaluation of an effective and efficient method to automatically detect sleep arousals using a single channel EEG. A detection model, based on a Curious Extreme Learning Machine (C-ELM), using a set of 22 features is proposed. The performance was evaluated using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and the Accuracy (ACC). The proposed C-ELM based model achieved an average AUC and ACC of 0.85 and 0.79 respectively. The best AUC from among the 50 datasets used was 0.88. In comparison, the average AUC and ACC of a Support Vector Machine (SVM) based model were 0.69 and 0.67 respectively, and the best AUC from among the same 50 datasets used was 0.88. This indicates that the proposed CELM based model works well for the sleep arousal detection problem.