BCGNet: An AI Model Trained on 600K Hours of Sleep Data for BCG-based Contactless Monitoring

Sleep profoundly impacts health, yet current gold-standard Polysomnogram (PSG) is constrained by cost, discomfort, and limited scalability for longitudinal monitoring. Ballistocardiogram (BCG) offers a non-invasive and user-friendly alternative but often lacks the precision needed for reliable real-world applications. To address this gap, we propose BCGNet, a two-stage transfer learning model that is first pre-trained on 580,866 hours of PSG and then fine-tuned and validated on 15,081 hours of BCG (total 595,947 hours of recordings). Across multiple validation cohorts, BCGNet achieves strong performance in 4-class sleep staging (F1: 0.710 − 0.817), Apnea-Hypopnea Index (AHI3%) estimation (Pearson’s r > 0.95), and robust quantification of sleep continuity and architecture (ICC and Pearson’s r generally > 0.8). Notably, BCGNet maintains strong performance even on short daytime naps and demonstrates excellent generalizability across diverse external datasets. Deployed as a portable, contactless sleep tracking mat, BCGNet represents a major step towards scalable, user-friendly solutions for longitudinal home sleep monitoring, with important implications for population screening and personalized sleep medicine.