3260 papers • 126 benchmarks • 313 datasets
Sleep arousal is a kind of EEG events happened during octurnal sleep. Too many arousals will contribute to many health problem, like daytime sleepiness, memory loss, diabetes, etc. Some research take it as a kind of sleep deprivation.
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A novel deep learning architecture called Dreem One Shot Event Detector (DOSED) is proposed, inspired by object detectors developed for computer vision such as YOLO and SSD, which predicts locations, durations and types of events in EEG time series.
DeepSleep is presented, which ranked first in the 2018 PhysioNet Challenge for automatically segmenting sleep arousal regions based on polysomnographic recordings, and features accurate, high-resolution, and fast delineation of sleep arousals.
The results support the general recommendation to use a clinical dataset for training if the model is to be applied to clinical data, particularly with regard to the effects of different sleep disorders.
Adding a benchmark result helps the community track progress.