Year: 2026
Type: article
Abstract: Sleep disorders are associated with cardiovascular diseases and cognitive decline worldwide, highlighting the need for effective therapeutic strategies. Polysomnography (PSG) is the gold standard diagnostic tool for sleep disorders. However, long examination times, high costs, specialized biosignal equipment and controlled settings requirements hinder large-scale data collection. As artificial intelligence applications in sleep research expand, structural heterogeneity in PSG data across institutions complicates multi-center integration, increases preprocessing burden, and limits reproducible large-scale algorithm validation. We investigated biosignals, sleep parameters, and demographic features from eight publicly available PSG datasets. A set of schemas for PSG harmonization is proposed to formalize unified structural specifications aligned with established clinical guidelines. These schemas systematically integrate heterogeneous biosignals, derived sleep parameters, and demographic features to achieve consistent attribute selection and uniform preprocessing across datasets. When applied to construct an integrated dataset, the schemas demonstrated stable demographic and clinical distributions through repeated sampling. By addressing schema-level interoperability, the proposed framework facilitates consistent algorithm evaluation and comparison and provides a structured reference for reproducible, large-scale sleep research.
Source: PLoS ONE
Authors: Yujin Han, Gayeon Ryu, Yeongdon Kim, Hangsik Shin
Institutions: Asan Medical Center, University of Ulsan
Language: English
Topic: Sleep and related disorders
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funder: Ministry of Food and Drug Safety
OpenAlex ID: W7225322539