← Back to all news
arXiv Machine Learning

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

ECG‑SDI is a single‑lead electrocardiography framework that provides continuous sleep‑depth profiling and digital biomarker extraction. Trained on 12,392 overnight polysomnographic recordings, the two‑stage deep learning model achieved strong correlations with conventional sleep stages (0.828–0.848) and high REM classification AUROCs (0.954–0.977). The approach identified a disturbed sleep subtype linked to poorer subjective quality and higher mortality, and demonstrated measurable improvements in sleep‑depth metrics after CPAP therapy, suggesting its utility for home‑based sleep monitoring.

Related stories

arXiv AI
Sep 10

Learning transferable human physiology from two million hours of sleep with SleepFM-2

SleepFM-2 is a foundation model trained on 282,511 polysomnography recordings, covering over two million hours of multimodal sleep physiology. It outperforms its predecessor in disease prediction, sleep scoring, and event detection, and its representation improves performance across diverse tasks—including wearable sensing, subjective sleep reports, and transfer to other EEG modalities. When combined with age, sex, and BMI, the model meets stringent discrimination criteria for 215 EHR phenotypes, adding reproducible information beyond demographics for 155 of them.

By Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler, Sophia Claire Kivelson, Umaer Hanif, Hyatt Moore IV, Harrison G. Zhang, Hafsa Ahmed, Marcus Dige, Niels R. Lorenzen, Elisabeth Roxane M. Heremans, Adrien Specht, Ulysse Gimenez, Robin Guillard, Andreas Brink-Kjaer, James Zou, Emmanuel Mignot
multimodal
More like this →
arXiv AI
Oct 15

Combining scEEG and PPG for reliable sleep staging

arXiv:2602.15042v2 Announce Type: replace-cross Abstract: Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photople...

By Jiawei Wang, Liang Xu, Shuntian Zheng, Yu Guan, Kaichen Wang, Ziqing Zhang, Chen Chen, Laurence T. Yang, Sai Gu, M. Jamal Deen
More like this →
arXiv Machine Learning
Sep 22

Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction

The study compares six state‑of‑the‑art sleep‑staging foundation models on the MESA polysomnography dataset under three signal conditions: EEG only, ECG only, and EEG+ECG. Results show that EEG alone yields the highest accuracy (BIOT macro‑F1 = 0.7237), while switching to ECG alone incurs a consistent accuracy loss of about 0.35 macro‑F1 and reduces data rate to one‑third. Adding ECG to EEG offers little benefit for most models, indicating that EEG carries most of the sleep‑staging signal and that wearable‑compatible ECG alone is a viable but less accurate alternative.

By Hassan Mehdi, Riku Klen, Ayse Kosal Bulbul, Suzanne Timmons, Abdulhamit Subasi, Wei Chen, Zou Zhu, Muhammad Irfan
llmscomputer-visionbenchmarks
More like this →
arXiv Machine Learning
Aug 4

Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks

arXiv:2608. 00943v1 Announce Type: cross Abstract: Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard.

By Shuntian Zheng, Jiawei Wang, Cong Fu, Huan Yu, Chen Chen, Yu Guan, Sai Gu
benchmarks
More like this →
arXiv Machine Learning
Jul 28

StageGuard: Physiologically Constrained Sleep Staging

arXiv:2607. 23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficiency, and bout-duration statistics.

By Juntang Wang, Yihan Wang, Hao Wu, Jiayu Gao, Shixin Xu, Dongmian Zou
safety
More like this →
arXiv AI
Jul 13

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics

arXiv:2607. 07720v2 Announce Type: replace-cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration.

By Zhoujie Hou, Song Wang, Kexin Lou, Mo Wang, Chen Wei, Quanying Liu
multimodalbenchmarks
More like this →