Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
🤖 AI Summary
This study addresses the limitations of conventional polysomnography (PSG)—its high cost and infeasibility for long-term home-based screening—and the inability of existing single-lead electrocardiogram (ECG) approaches to simultaneously assess sleep architecture and cardiac phenotypes. To this end, we propose Holter-to-Sleep, a novel framework that, for the first time, leverages deep learning to jointly extract overnight sleep staging and Holter-grade arrhythmia phenotypes solely from single-lead ECG signals, eliminating the need for PSG. This approach establishes a low-burden, home-deployable paradigm for integrated sleep–cardiac monitoring. Validated on 10,439 multicenter recordings, our framework demonstrates strong generalizability, accurately deriving clinically relevant sleep metrics and systematically uncovering associations between ECG-derived sleep features and arrhythmias.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
sleep phenotyping
single-lead ECG
polysomnography
cardio-sleep association
scalable monitoring
Innovation
Methods, ideas, or system contributions that make the work stand out.
single-lead ECG
sleep phenotyping
Holter monitoring
cardio-sleep association
AI-enabled repurposing
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