aSAGA: Automatic Sleep Analysis with Gray Areas

TL;DR

This paper introduces a human-in-the-loop automatic sleep staging model, aSAGA, that uses uncertainty mapping to identify ambiguous regions, improving accuracy and facilitating integration into clinical workflows.

Contribution

The study presents a novel explainable AI approach for sleep staging that incorporates gray areas for manual review, enhancing clinical applicability and interpretability.

Findings

aSAGA achieves comparable agreement with manual analysis across datasets.

Gray areas effectively identify regions needing manual re-evaluation.

The approach supports integration into clinical workflows with improved transparency.

Abstract

State-of-the-art automatic sleep staging methods have already demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow and the interaction between explainable automatic methods and the work of sleep technologists remains underexplored and inadequately conceptualized. Thus, we propose a human-in-the-loop concept for sleep analysis, presenting an automatic sleep staging model (aSAGA), that performs effectively with both clinical polysomnographic recordings and home sleep studies. To validate the model, extensive testing was conducted, employing a preclinical validation approach with three retrospective datasets; open-access, clinical, and research-driven. Furthermore, we validate the utilization of uncertainty mapping to identify ambiguous regions, conceptualized as gray areas, in automatic sleep analysis that warrants manual re-evaluation. The results demonstrate that the automatic sleep analysis achieved a comparable level of agreement with manual analysis across different sleep recording types. Moreover, validation of the gray area concept revealed its potential to enhance sleep staging accuracy and identify areas in the recordings where sleep technologists struggle to reach a consensus. In conclusion, this study introduces and validates a concept from explainable artificial intelligence into sleep medicine and provides the basis for integrating human-in-the-loop automatic sleep staging into clinical workflows, aiming to reduce black-box criticism and the burden associated with manual sleep staging.