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Found 4 papers, 3 papers with code
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders.
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.
Ranked #1 on
Sleep Arousal Detection
on MESA
A deep learning architecture to detect events in EEG signals during sleep
Annotations of such events require a trained sleep expert, a time consuming and tedious process with a large inter-scorer variability.
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can exploit the temporal context of each 30s window of data.
