AI vs Humans for the diagnosis of sleep apnea

Thorey, Valentin; Hernández, Albert Bou; Arnal, Pierrick J.; During, Emmanuel

doi:10.48550/arxiv.1906.09936

Cited by 1 publication

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“…More details and exclusion criteria can be found in [25]. Demographics are summarized in Table I [25] for DOD-H and [27] for DOD-O.…”

Section: Methodsmentioning

confidence: 99%

Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

Guillot¹,

Sauvet²,

During³

et al. 2019

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Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an interrater agreement of about 85 % only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches. We also developed and benchmarked a new deep learning method, SimpleSleepNet, inspired by current state-of-the-art. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89.9 % vs 86.8 % on average for human scorers on DOD-H, and an F1 of 88.3 % vs 84.8 % on DOD-O. Our study highlights that using stateof-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Consideration could be made to use automated approaches in the clinical setting.

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“…More details and exclusion criteria can be found in [25]. Demographics are summarized in Table I [25] for DOD-H and [27] for DOD-O.…”

Section: Methodsmentioning

confidence: 99%