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The visual scoring of arousals during sleep routinely conducted by sleepexperts is a challenging task warranting an automatic approach. This paperpresents an algorithm for automatic detection of arousals during sleep. Usingthe Physionet/CinC Challenge dataset, an 80-20% subject-level split wasperformed to create in-house training and test sets, respectively. The data foreach subject in the training set was split to 30-second epochs with no overlap.A total of 428 features from EEG, EMG, EOG, airflow, and SaO2 in each epochwere extracted and used for creating subject-specific models based on anensemble of bagged classification trees, resulting in 943 models. For markingarousal and non-arousal regions in the test set, the data in the test set wassplit to 30-second epochs with 50% overlaps. The average of arousalprobabilities from different patient-specific models was assigned to each30-second epoch and then a sample-wise probability vector with the same lengthas test data was created for model evaluation. Using the PhysioNet/CinCChallenge 2018 scoring criteria, AUPRCs of 0.25 and 0.21 were achieved for thein-house test and blind test sets, respectively.

Automatic Detection of Arousals during Sleep using Multiple Physiological Signals