Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping

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.

Figures (10)

• Figure 1: Overview of the Holter-to-Sleep study design and analytic workflow.a, Data collection. Single-lead ECG was extracted from multi-center PSG cohorts. b, Model development. ECG-based deep learning model for sleep staging and sleep disturbance detection. c, Wearable data validation. Participants underwent overnight recordings using a patch-based ECG device, accompanied by PSQI-based subjective sleep quality assessment; model-derived sleep-consistency metrics were used to evaluate real-world agreement. d, Holter-derived cardiac metrics and sleep associations. Model outputs were used to align long-duration nocturnal ECG with sleep, enabling joint extraction of sleep metrics and Holter-grade cardiac phenotypes, followed by association analyses to support scalable cardio--sleep phenotyping.
• Figure 2: Cross-cohort generalization of single-lead ECG sleep staging on internal and external validation. The top row shows internal validation and the bottom row shows external validation; from left to right are confusion matrices for 5-class, 4-class, 3-class, and 2-class staging. Each cell reports the epoch count, with the percentage in parentheses normalized by the predicted class. For 4-class staging, Light merges N1+N2 and Deep corresponds to N3; for 3-class staging, NREM merges N1--N3; for 2-class staging, Sleep merges N1--N3 and REM.
• Figure 3: Cross-cohort generalization of sleep event detection. ROC curves are shown for arousal and respiratory event detection; the left panel corresponds to internal validation and the right panel to external validation. The area under the ROC curve (AUC) is reported in each panel.
• Figure 4: Real-world wearable validation of ECG-derived sleep consistency metrics against PSQI. Scatter plots show associations between normalized PSQI scores and model-derived overnight metrics. Left: wake probability, defined as the overnight mean of epoch-wise predicted wake probabilities. Right: sleep efficiency estimated from model-inferred sleep--wake information. Solid lines indicate linear regression with 95% confidence bands. Pearson's correlation coefficient ($r$) and two-sided $p$ values are shown in each panel.
• Figure 5: ECG-derived sleep phenotypes differ across Holter arrhythmia-related strata. Violin plots show distributions of time in bed (TIB), sleep latency (SL), sleep efficiency (SE), and oxygen desaturation index at 3% (ODI 3%). Participants are stratified by (i) frequent premature ventricular contractions (PVC burden $\geq 21$ events h$^{-1}$) versus non-frequent PVC, (ii) frequent premature atrial contractions (PAC burden $\geq 30$ events h$^{-1}$) versus non-frequent PAC, (iii) atrial fibrillation (AF) presence during sleep versus absence, and (iv) ventricular tachycardia (VT) presence during sleep versus absence. Embedded boxplots indicate the median and interquartile range. Values below each panel denote mean$\pm$s.d. $P$ values from between-group comparisons are annotated in each panel. TIB and SL are reported in minutes, SE in %, and ODI 3% in events h$^{-1}$.