Résumé

The application of Artificial Intelligence (AI) in
automatic sleep scoring presents significant opportunities for
enhancing sleep analysis and diagnosing sleep disorders.
However, a major challenge lies in the lack of transparency in
AI-driven decision-making, which can hinder trust and
comprehension among sleep researchers and clinicians.
Explainable Artificial Intelligence (XAI) has emerged as a key
approach to addresss these concerns by providing insights into
AI model predictions and improving interpretability. This
review examines the role and effectiveness of Explainability and
interpretability in automatic sleep scoring, analyzing key
challenges, the impact of various methodologies, and commonly
used algorithms. Based on a comprehensive analysis of 100
recent studies, we bridge the gap between computer-readable
data encodings and human-understandable information,
enhancing model explainability and transparency. Ultimately,
this review underscores the vital role of Explainability in
refining sleep evaluation and decision-making, emphasizing the
necessity of further research to address existing challenges and
maximize its potential.