Automatic Detection of Arousals during Sleep using Multiple Physiological Signals
This addresses the need for automated scoring of sleep arousals, which is a challenging manual task for sleep experts, but the approach is incremental as it builds on existing methods and datasets.
The paper tackled the problem of automatically detecting arousals during sleep by developing an algorithm using multiple physiological signals, achieving AUPRCs of 0.25 and 0.21 on in-house and blind test sets, respectively.
The visual scoring of arousals during sleep routinely conducted by sleep experts is a challenging task warranting an automatic approach. This paper presents an algorithm for automatic detection of arousals during sleep. Using the Physionet/CinC Challenge dataset, an 80-20% subject-level split was performed to create in-house training and test sets, respectively. The data for each 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 epoch were extracted and used for creating subject-specific models based on an ensemble of bagged classification trees, resulting in 943 models. For marking arousal and non-arousal regions in the test set, the data in the test set was split to 30-second epochs with 50% overlaps. The average of arousal probabilities from different patient-specific models was assigned to each 30-second epoch and then a sample-wise probability vector with the same length as test data was created for model evaluation. Using the PhysioNet/CinC Challenge 2018 scoring criteria, AUPRCs of 0.25 and 0.21 were achieved for the in-house test and blind test sets, respectively.