Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
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arXiv
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| Format: | Preprint |
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2026
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| author | Xie, Donglin Zhao, Qingshuo Wang, Jingyu Geng, Shijia Jin, Jiarui Li, Jun Guo, Rongrong Nie, Guangkun Tang, Gongzheng Zhou, Yuxi Penzel, Thomas Hong, Shenda |
| author_facet | Xie, Donglin Zhao, Qingshuo Wang, Jingyu Geng, Shijia Jin, Jiarui Li, Jun Guo, Rongrong Nie, Guangkun Tang, Gongzheng Zhou, Yuxi Penzel, Thomas Hong, Shenda |
| contents | Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night, home-based, and large-scale screening. Single-lead electrocardiography (ECG), already ubiquitous in Holter and patch-based devices, enables comfortable long-term acquisition and encodes sleep-relevant physiology through autonomic modulation and cardiorespiratory coupling. Here, we present a proof-of-concept Holter-to-Sleep framework that, using single-lead ECG as the sole input, jointly supports overnight sleep phenotyping and Holter-grade cardiac phenotyping within the same recording, and further provides an explicit analytic pathway for scalable cardio-sleep association studies. The framework is developed and validated on a pooled multi-center PSG sample of 10,439 studies spanning four public cohorts, with independent external evaluation to assess cross-cohort generalizability, and additional real-world feasibility assessment using overnight patch-ECG recordings via objective-subjective consistency analysis. This integrated design enables robust extraction of clinically meaningful overnight sleep phenotypes under heterogeneous populations and acquisition conditions, and facilitates systematic linkage between ECG-derived sleep metrics and arrhythmia-related Holter phenotypes. Collectively, the Holter-to-Sleep paradigm offers a practical foundation for low-burden, home-deployable, and scalable cardio-sleep monitoring and research beyond traditional PSG-centric workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18714 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping Xie, Donglin Zhao, Qingshuo Wang, Jingyu Geng, Shijia Jin, Jiarui Li, Jun Guo, Rongrong Nie, Guangkun Tang, Gongzheng Zhou, Yuxi Penzel, Thomas Hong, Shenda Signal Processing Machine Learning Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night, home-based, and large-scale screening. Single-lead electrocardiography (ECG), already ubiquitous in Holter and patch-based devices, enables comfortable long-term acquisition and encodes sleep-relevant physiology through autonomic modulation and cardiorespiratory coupling. Here, we present a proof-of-concept Holter-to-Sleep framework that, using single-lead ECG as the sole input, jointly supports overnight sleep phenotyping and Holter-grade cardiac phenotyping within the same recording, and further provides an explicit analytic pathway for scalable cardio-sleep association studies. The framework is developed and validated on a pooled multi-center PSG sample of 10,439 studies spanning four public cohorts, with independent external evaluation to assess cross-cohort generalizability, and additional real-world feasibility assessment using overnight patch-ECG recordings via objective-subjective consistency analysis. This integrated design enables robust extraction of clinically meaningful overnight sleep phenotypes under heterogeneous populations and acquisition conditions, and facilitates systematic linkage between ECG-derived sleep metrics and arrhythmia-related Holter phenotypes. Collectively, the Holter-to-Sleep paradigm offers a practical foundation for low-burden, home-deployable, and scalable cardio-sleep monitoring and research beyond traditional PSG-centric workflows. |
| title | Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2603.18714 |