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ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health

L5 · ResearcherResearcharXiv· 10/16/2026

Presents novel ML architecture for sleep health monitoring with clinical validation and mortality associations.

AI Summary

Researchers developed ECG-SDI, a deep learning framework using single-lead ECG data to continuously monitor sleep depth and extract digital biomarkers from 12,392 overnight recordings.

Excerpt

Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction. Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels. Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in t

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