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Found 4 papers, 3 papers with code
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.
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.
