Sleep Micro-event detection
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Sleep Micro-event detection
Latest papers
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.
Advanced sleep spindle identification with neural networks
dslaborg/sumo
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Scientific Reports 2022
Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset.
RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
nicolasigor/cmorlet-tensorflow
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The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes.
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.
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.
