Sleep Arousal Detection
3 papers with code • 2 benchmarks • 0 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.
Benchmarks
These leaderboards are used to track progress in Sleep Arousal Detection
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Most implemented papers
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD.
Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning
GuanLab/DeepSleep
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Lancet 2019
Background: Sleep arousals are transient periods of wakefulness punctuated into sleep.
DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning
DeepSleep 2. 0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge.
