Sleep Arousal Detection
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State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset
DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning
Papers
State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset
Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine…
Sleep Arousal DetectionDeepSleep 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+1Deepsleep: 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 Quality1D Convolutional Neural Network Models for Sleep Arousal Detection
Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal det…
EEGElectroencephalogram (EEG)Sleep Arousal 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+5Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the…
Sleep Arousal DetectionSTS