Building a Sleep Data Repository: Architecture, Data Sources, and Governance
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Updated: Nov 5, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Arash Maghsoudi1, Saleem D Mohamed2, Saiju Pyarajan3
1Artificial Intelligence in Health Lab, Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA; Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Abstract:
Large-scale sleep data repositories are critical for precision sleep medicine, artificial intelligence-driven research, and population-level studies of sleep disorders. This article presents a practical framework for building scalable repositories. Key components include data intake and processing, centralized storage, external data integration, and secure study environments. We discuss emerging data sources such as polysomnography, home sleep apnea testing, and consumer wearables, along with requirements for data standardization, including European Data Format parsing, channel harmonization, and metadata validation. The framework also highlights governance principles for compliant data use across multiple regulatory environments, providing guidance for developing secure and scalable sleep data infrastructure.
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