用单导联心电图同时分析睡眠与心脏,实现居家低成本监测。
Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
- 仅用单导联心电图,同步完成睡眠与心脏表型分析。
- 在10,439例多中心数据上验证,跨人群泛化性强。
- 适合大规模心睡联合研究,推动家庭式健康监测。
睡眠障碍与心血管风险密切相关,但临床金标准多导睡眠图(PSG)资源消耗大,不适用于多夜、居家及大规模筛查。单导联心电图(ECG)已广泛用于霍尔特和贴片设备,可舒适地进行长期采集,并通过自主神经调节和心肺耦合编码睡眠相关生理信息。本文提出一个概念验证的「霍尔特转睡眠」框架,仅以单导联ECG为输入,在同一记录中同步实现整夜睡眠表型与霍尔特级心脏表型分析,并提供可扩展的心脏-睡眠关联研究分析路径。该框架基于10,439例来自四个公开队列的多中心PSG样本进行开发与验证,通过独立外部评估检验跨队列泛化能力,并利用真实世界贴片ECG记录进行客观-主观一致性分析以评估可行性。此集成设计能在异质人群与采集条件下稳健提取具有临床意义的整夜睡眠表型,促进ECG衍生睡眠指标与心律失常相关的霍尔特表型之间的系统性关联。总体而言,「霍尔特转睡眠」范式为低负担、居家部署、可扩展的心脏-睡眠监测与研究提供了实用基础,突破传统以PSG为中心的工作流程。
原文摘要 · Abstract (English)
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.
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