Sleep Data Repositories in Practice: Signal Processing, Quality Control, and Artificial Intelligence Integration

Realizing the scientific and clinical potential of large-scale sleep data repositories requires rigorous technical infrastructure for signal processing, quality control, and artificial intelligence (AI) integration. This article addresses the practical implementation steps that follow repository architecture and data ingestion. We outline strategies for linking sleep study data with longitudinal electronic health records, including deterministic and probabilistic matching, temporal alignment, and approaches to handling missing linkages and incomplete clinical data. Finally, we discuss emerging directions, including wearable data integration, federated and privacy-preserving learning, precision medicine applications, and regulatory and health equity considerations essential to responsible repository-scale AI deployment.