ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health
Authors: Donglin Xie, Qingshuo Zhao, Songchi Zhou, Haoyu Wang, Jingyu Wang, Yuxi Zhou, Thomas Penzel, Shenda Hong
Organizations: Institute of Medical Technology, Peking University Health Science Center, Beijing, China · National Institute of Health Data Science, Peking University, Beijing, China · Department of Computer Science, Tianjin University of Technology, Tianjin, China · Department of Statistics and Data Science, Tsinghua University, Beijing, China · University of Chinese Academy of Sciences, China · Department of Respiratory and Critical Care Medicine, Binzhou Medical University Hospital, Binzhou, China · Department of Respiratory, The Second Hospital of Tianjin Medical University, Tianjin, China · Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany · Institute for Artificial Intelligence, Peking University, Beijing, China
Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation. We developed ECG-SDI, a single-lead electrocardiography (ECG) framework for continuous sleep-depth profiling and digital biomarker extraction. Using 12,392 overnight polysomnographic recordings, we trained and validated a two-stage deep learning model supervised by conventional sleep-stage labels. Correlations with ordered stages excluding rapid eye movement (REM) sleep were 0.828 in the pooled internal test set and 0.848 in the external Cleveland Family Study; corresponding REM classification AUROCs were 0.954 and 0.977. Overnight trajectories captured within-stage variation. Biomarker-based phenotyping identified a disturbed subtype associated with poorer subjective sleep quality and higher all-cause mortality in the Sleep Heart Health Study (adjusted hazard ratio, 1.20; 95% CI, 1.05-1.38). Among 507 participants with paired recordings before and after continuous positive airway pressure (CPAP), normalized sleep-depth area increased by 6.1% and shallow-sleep burden below 0.3 decreased by 16.0%. Exploratory analyses linked reductions in approximate entropy and depth variability to reduced oxygen desaturation and increased sleep efficiency, respectively (Spearman rho = 0.24 and 0.12); both associations remained positive after covariate adjustment. Mean ECG-SDI within N2 increased by 0.017 (95% CI, 0.004-0.030). A wearable proof-of-concept illustrated the acquisition-to-report workflow. These findings support ECG-based sleep phenotyping and further evaluation of CPAP response monitoring at home.
Figures & tables
Figure 1: Overall study framework and analytical pipeline. a, Single-lead ECG was extracted from multi-cohort PSG recordings to construct internal datasets for model development (SHHS, MESA, and MrOS) and fully independent external validation datasets (CFS and BZ-CPAP). b, A two-stage neural network was used to learn a continuous sleep depth index (ECG-SDI) from raw ECG under supervision from conventional discrete sleep-stage labels, thereby transforming coarse-grained sleep staging into a continuous representation of sleep depth. c, Digital biomarkers were then derived from whole-night ECG-SDI trajectories and used for sleep-phenotype clustering, clinical risk stratification, and tracking CPAP treatment effects, enabling systematic evaluation of the physiological relevance, clinical utility, and treatment responsiveness of ECG-SDI.
Cohort
Corr. SS (excl. REM) ↑
REM class. AUROC ↑
Pooled internal test
0.828 (0.827–0.828)
0.954 (0.953–0.954)
Internal SHHS
0.730 (0.729–0.732)
0.908 (0.907–0.909)
Internal MESA
0.797 (0.796–0.798)
0.934 (0.933–0.935)
Internal MrOS
0.882 (0.882–0.883)
0.987 (0.987–0.988)
External CFS
0.848 (0.847–0.849)
0.977 (0.976–0.977)
External BZ-CPAP (pre-CPAP)
0.456 (0.453–0.459)
0.803 (0.802–0.805)
Table 1: Performance of ECG-SDI for sleep-depth ordering and REM classification in internal and external cohorts. Corr. SS (excl. REM), correlation between ECG-SDI and ordered sleep stages excluding REM; REM class. AUROC, area under the receiver operating characteristic curve for REM classification. Values are reported as point estimates with corresponding uncertainty intervals.
Figure 2: Whole-night ECG-SDI trajectories and within-stage ECG examples in the external CFS cohort. A,B, Conventional hypnograms (upper panels) and corresponding ECG-SDI trajectories (lower panels) for a lower-AHI recording ( A ; cfs-visit5-802076) and an OSA recording ( B ; cfs-visit5-801193). REM epochs are highlighted in red, and wake/NREM epochs are shown in blue. Insets report total sleep time (TST), sleep efficiency (SE), apnea–hypopnea index (AHI), normalized area under the depth curve (AP), and area under the depth curve (AUC). C–E, Representative 30-s ECG segments from REM ( C ; cfs-visit5-800129), N1 ( D ; cfs-visit5-802137), and N2 ( E ; cfs-visit5-801622). Within each row, three epochs from the same participant and conventional sleep stage are arranged in ascending order of ECG-SDI. Each segment is annotated with its epoch identifier, stage, SDI value, and heart rate (HR; beats min -1 ). The labels shallow, medium, and deep indicate relative positions on the ECG-SDI scale within the illustrated stage and do not represent additional conventional sleep stages.
Figure 3: Clinical profiles and all-cause mortality associated with ECG-SDI-derived sleep phenotypes. A, Comparisons between the normal sleep and disturbed sleep subtypes in SHHS, CFS, and MrOS. Upper panels show Cohen’s d for demographic characteristics, conventional sleep measures, and ECG-SDI-derived biomarkers; positive values indicate higher values in the disturbed subtype. Lower panels show odds ratios for binary characteristics and clinical outcomes. Points and horizontal bars denote estimates and 95% confidence intervals, respectively. Clinical outcome models were adjusted for age, body mass index, sex, sleep efficiency, and sleep latency, where applicable. Biomarker comparisons describe variables used for clustering. B, Kaplan–Meier curves for all-cause mortality in SHHS, with shaded 95% confidence intervals and numbers at risk below. Blue and red indicate the normal sleep and disturbed sleep subtypes, respectively. The inset reports the log-rank test and Cox proportional hazards estimates. Model 1 adjusts for age, sex, and race; Model 2 additionally adjusts for body mass index, smoking status, hypertension, and diabetes mellitus; Model 3 additionally adjusts for apnea–hypopnea index and total sleep time.
Figure 4: Paired distributions of key ECG-SDI-derived biomarkers before and after CPAP treatment in the BZ-CPAP cohort. Violin plots with embedded boxplots compare the distributions of AP, RB, RB 0.3 , CV, SK, APPe, DETRf, MDR, and PR between pre-CPAP and post-CPAP recordings. Red horizontal lines indicate medians, boxes denote interquartile ranges, and violin contours represent the full distribution of each biomarker. Across multiple metrics, post-CPAP values shifted toward higher accumulated sleep depth and reduced shallow-sleep burden, consistent with improved sleep continuity after treatment.
Figure 5: Exploratory associations of CPAP-associated ECG-SDI changes with PSG outcomes and within-stage sleep depth. A, Association between reductions in APPe and PSG-derived ODI 3 , both calculated as pre-CPAP minus post-CPAP values. B, Association between the reduction in CV (pre-CPAP minus post-CPAP) and the increase in PSG-derived sleep efficiency (post-CPAP minus pre-CPAP, in percentage points). Points represent paired participant-level changes; solid lines illustrate fitted trends. Spearman correlations, 95% confidence intervals (CIs), and P values are reported in each panel. C, Adjusted standardized regression coefficients and 95% CIs for the associations shown in A and B, with 395 paired participants included in each model. M0 adjusts for age, sex, BMI, baseline AHI, and the corresponding baseline PSG outcome; M1 additionally adjusts for the baseline value of the corresponding ECG-SDI biomarker. Open circles denote M0 and filled diamonds denote M1. D, Paired mean ECG-SDI values within PSG-defined N2 before and after CPAP, restricted to participants with at least 10 min of N2 at each visit. Lines connect paired observations. The mean paired change, its 95% CI, and the paired t -test P value are shown. APPe, approximate entropy; CV, coefficient of variation; ODI 3 , oxygen desaturation index.
Figure 6: Proof-of-concept wearable workflow for home ECG-SDI reporting. A, Illustration of overnight single-lead ECG acquisition using a chest-worn device in a modified Lead II configuration. B, Smartphone connection interface and automated analysis workflow comprising ECG-SDI inference and biomarker extraction. C, Illustrative report displaying an overnight ECG-SDI trajectory and summary values for AP, PR, RB, and CV. Interface elements, trajectories, and numerical values illustrate the workflow and do not constitute a home-validation dataset or validated normal/abnormal classifications.
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Cohort
Number
Age (years)
Female (%)
BMI (kg/m 2 )
AHI
Timeframe
CFS
726
41.4 ± 19.4
55%
32.4 ± 9.5
10.2 ± 18.5
2001–2006
MrOS
2,806
76.4 ± 5.5
0%
27.2 ± 3.8
13.7 ± 14.4
2003–2012
MESA
2,055
69.4 ± 9.1
54%
28.7 ± 5.5
14.8 ± 16.7
2010–2013
SHHS
5,791
63.1 ± 11.2
52%
28.2 ± 5.1
10.2 ± 13.6
1995–2010
BZ-CPAP
507
47.0 ± 11.8
9%
30.7 ± 4.8
pre: 52.38 ± 25.04 post: 5.68 ± 7.28
2019–2025
Appendix
Table 2: Baseline characteristics of the study cohorts included in this work. Continuous variables are reported as mean ± standard deviation unless otherwise specified. In the BZ-CPAP cohort, pre and post in the AHI column denote measurements obtained before and after CPAP treatment, respectively.
Figure 7: Stage-wise distribution of ECG-SDI across conventional sleep stages in the SHHS, CFS, MESA, and MrOS cohorts. Boxplots show the distributions of ECG-SDI for wake (W), N1, N2, N3, and REM sleep. The center line denotes the median, the box denotes the interquartile range, and the whiskers indicate the distribution spread.
Metric
pre-CPAP
post-CPAP
Change
P value
Sig
AP
0.5909
0.6269
+6.10%
<0.001
RB
0.0926
0.0820
-11.45%
0.0368
RB 0.3
0.1580
0.1327
-16.01%
<0.001
CV
0.5277
0.5021
-4.85%
<0.001
SK
-0.2192
-0.4483
-104.5%
<0.001
APPe
1.1257
0.9243
-17.89%
<0.001
Appendix
Table 4: Detailed paired comparison of ECG-SDI-derived biomarkers before and after CPAP treatment in the BZ-CPAP cohort. pre-CPAP, before treatment; post-CPAP, after treatment; Change, post-CPAP minus pre-CPAP. P values were obtained from two-sided paired tests. Sig indicates significance level: * P<0.05 , ** P<0.01 , *** P<0.001 , ns not significant.
Figure 8: Detailed architecture of the proposed ECG-SDI framework. The proposed ECG-SDI framework is built upon a pretrained and frozen two-stage single-lead ECG sleep-staging backbone. In Stage 1, each 30-s ECG epoch is preprocessed and passed through a CNN-based time-domain encoder together with a handcrafted-feature branch, where 39 frequency- and time–frequency-domain features are projected through two fully connected layers and fused with the CNN representation to form an epoch-level sleep embedding. In Stage 2, the frozen Stage-1 encoder generates sequential multi-epoch representations, which are further processed by a Transformer-based temporal module to capture inter-epoch contextual dependencies and refine sleep representations. During ECG-SDI adaptation, the original refined sleep-staging head is replaced with two lightweight task-specific heads: one for REM-related binary classification and the other for continuous sleep-depth estimation, producing REM-related outputs and the continuous ECG-SDI score, respectively. This design repurposes a pretrained sleep-staging backbone for continuous sleep-depth biomarker learning while preserving multi-epoch temporal modeling.
Metric
Category
Brief description
AP
Depth burden
Normalized area under the whole-night ECG-SDI curve. Higher values indicate greater cumulative sleep-depth burden across the night.
RB
Shallow-sleep burden
Proportion of epochs with ECG-SDI values below 0.2, reflecting the overall dominance of shallow sleep.
RB 0.3
Shallow-sleep burden
Proportion of epochs with ECG-SDI values below 0.3. This is a less stringent shallow-sleep index used mainly for treatment-response analysis.
CV
Variability
Coefficient of variation of the overnight ECG-SDI trajectory, describing the relative extent of night-long fluctuation.
SK
Distribution shape
Skewness of the ECG-SDI distribution, reflecting whether the depth profile is shifted toward shallower or deeper values.
APPe
Complexity
Approximate entropy, used to characterize the regularity and predictability of the ECG-SDI time series. Lower values indicate a more regular trajectory.
Appendix
Table 5: Brief description of ECG-SDI-derived digital biomarkers used in clustering and treatment-response analyses. Descriptions are intended to provide intuitive physiological interpretation rather than formal mathematical definitions. RB 0.3 was additionally used in the CPAP treatment-response analysis.
Setting
CNN staging
Transformer staging
ECG-SDI heads
Optimizer
Adam
Adam
Adam
Initial learning rate
10−3
10−4
10−3
Weight decay
10−5
0
0
Maximum training epochs
50
100
100
Early-stopping patience
5
5
5
Batch size
256 ECG epochs
64 recordings a
1 recording
Appendix
Table 6: Training settings in the released research scripts.
Cohort
Poor sleep quality
Insomnia symptoms
SHHS
ltdp10 ≤2 and rest10 ≤2
diffa10 ≥1
CFS
slpqua ≥5 and desslp ≥4
easlp >4 and difbak =1
MrOS
poxqual1 ≤2 and poxqual3 ≤2
poxfall >40 min
Appendix
Table 7: Operational definitions of questionnaire-derived binary outcomes.
Sleep stage classification assigns sleep epochs to physiological stages from signals such as EEG, helping quantify sleep architecture and support clinical monitoring.
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.
Donglin Xie, Qingshuo Zhao, Jingyu Wang +9
National Institute of Health Data Science, Peking University, Beijing, China · Institute of Medical Technology, Peking University Health Science Center, Beijing, China · Department of Computer Science, Tianjin University of Technology, Tianjin, China +6
Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care. Across centers, polysomnography (PSG) recordings are traditionally scored in 30-s epochs for pragmatic, not physiological, reasons and vary in electrode count, montage, and subject characteristics. These constraints challenge harmonized multi-center studies and the discovery of robust biomarkers on shorter timescales. We present AnySleep, a deep neural network that scores sleep from any electroencephalography (EEG) or electrooculography (EOG) data at adjustable temporal resolutions. We trained and validated the model on over 20,000 overnight recordings (> 200,000 hours of EEG and EOG) from 28 datasets across multiple clinics to promote robust generalization across sites. The model attains state-of-the-art performance and surpasses or equals established baselines at 30-s epochs. Performance improves with more channels, yet remains strong when EOG is absent or only EOG or single EEG derivations (frontal, central, or occipital) are available. On sub-30-s timescales, the model captures short wake intrusions consistent with arousals and improves prediction of pathophysiological conditions (obstructive sleep apnea, narcolepsy type 1, insomnia) over 30-s scoring. We make the model publicly available to facilitate large-scale studies with heterogeneous electrode setups and accelerate biomarker discovery in sleep.
Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428 Jülich, Germany · Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands · Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428 Jülich, Germany
Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep stages (wake, rapid eye movement (REM), light sleep, deep sleep) using a single-lead electrocardiogram (ECG) signal. We evaluate various machine learning and deep learning models, introducing two windowing strategies: (1) a 5-minute window with 30-second steps for machine learning and (2) a 30-second window with 10-second steps for deep learning, enabling 10-second temporal resolution for real-time predictions. While deep learning models like MobileNet-v1 achieve high accuracy (92%) and F1-score (91%), their energy demands make them unsuitable for wearables. To address this, we design SleepLiteCNN, optimized for ECG-based sleep staging, achieving 89% accuracy and 89% F1-score while minimizing energy use. Applying 8-bit quantization further reduces energy consumption to 5.48 microJ per inference, with 90% accuracy and F1-score. Additionally, field-programmable gate array (FPGA) deployment shows significant reductions in resource usage. This approach provides a practical, energy-efficient solution for continuous ECG-based sleep monitoring in resource-constrained wearable devices.
Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi
School of Electrical and Computer Engineering, University of Tehran.