DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal

GitHub - rfonod/deepsleep2: A compact convolutional deep neural network with an encoder/decoder structure to detect at a 5-millisecond resolution level non-apnea sleep arousals from multi-channel polysomnographic recordings.

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A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

Automatic snoring detection using a hybrid 1D–2D convolutional neural network

Frontiers Intelligent automatic sleep staging model based on CNN and LSTM

Automated sleep state classification of wide-field calcium imaging

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DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Sleep staging based on single-channel EEG and EOG with Tiny U-Net

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Frontiers Sleep medicine: Practice, challenges and new frontiers

Research articles Communications Biology