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Found 7 papers, 5 papers with code
RobustSleepNet: Transfer learning for automated sleep staging at scale
Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics.
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
We developed a framework to compare automated approaches to a consensus of multiple human scorers.
Ranked #1 on
Sleep Stage Detection
on MASS SS3
Automatic Sleep Stage Classification
Multimodal Sleep Stage Detection
+1
AI vs Humans for the diagnosis of sleep apnea
We assessed the performance of the automatic approach and compared it to the inter-scorer performance for both the diagnosis of OSA severity and, at the microscale, for the detection of single breathing events.
The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging
We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH’s automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring.
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.
