Papers Sleep Micro-event detection
“Sleep Micro-event detection” 태그가 달린 논문 5편 · 필터 해제
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. The pro…
DecoderDeep LearningSleep Arousal DetectionSleep Micro-event detection+1Advanced sleep spindle identification with neural networks
Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during …
DiagnosticEEGElectroencephalogram (EEG)Sleep Micro-event detection+4RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection
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. These events have b…
EEGElectroencephalogram (EEG)Event DetectionK-complex detection+3Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning
Background: Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality ann…
Sleep Arousal DetectionSleep Micro-event detectionSleep QualityDOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned…
EEGElectroencephalogram (EEG)K-complex detectionSleep apnea detection+5