Automated Sleep Arousal Detection Based on EEG Envelograms

Abstract

Background: Sleep arousal is basically described as a shift in EEG activity in frequencies > 16 Hz for a duration of > 3 sec (by the American Sleep Disorders Association - ASDA). The number of these arousals during sleep is a reflection of sleep quality. In accordance with the PhysioNet/CinC Challenge 2018, we present a method for automatic detection of arousals in polysomnographic recordings. Method: Each file in the training dataset (N=994) has defined 'Target Arousal Regions' (TAR, median length 33 seconds); however, arousals were usually located in the right half of these TARs. We built a method detecting EEG frequency shift to locate arousals inside ARs: envelograms (14-20, 16-25 and 20-40 Hz) were inspected in a 3-sec floating window for an increase against a 10-sec background. We then extracted 133,573 blocks with such a shift from TARs (N=38,628) as well as outside TARs (N=94,945). We extracted 23 features from these blocks (how many EEG channels/frequency bands EEG frequency shift; heart rate before/during arousal; airflow and EMG changes) and trained a bagged tree ensemble model (70/30 % hold-out). Results: The method showed AUPRC 0.27 on a training set and AUPRC 0.20 on a testing set (N=989).