arXiv subjects

Yelei Li

Publications and source records attributed to Yelei Li.

4 recordsLinked to original sources

arXiv2026

Smartwatch Photoplethysmography-Derived Heart Age via ECG-Guided Cross-Modal Pretraining as a Digital Biomarker of Vascular Aging

Digital biomarkers of cardiovascular aging, often termed heart or vascular age, have been widely studied, but most rely on resting electrocardiography (ECG), imaging, or specialized vascular assessments. Evidence linking wearable photoplethysmography (PPG) to arterial stiffness and hypertension remains limited. We developed an ECG-guided cross-modal framework that uses synchronized smartwatch ECG to enhance PPG representation learning during pretraining while requiring only PPG at inference. The study included three OPPO cohorts across China, comprising 581,804 participants and 7,452,131 recordings. The Vascular Health Study cohort supported ECG-PPG self-supervised pretraining, fine-tuning, and internal validation, while two external cohorts assessed associations with pulse wave velocity (PWV) and prevalent hypertension. Combining subject-aware learning with ECG-PPG contrastive alignment, the PPG-only model achieved subject-level mean absolute errors of 5.895 years (Pearson r=0.819) in the PWV cohort and 4.344 years (r=0.800) in the home blood pressure monitoring cohort. Aggregating repeated recordings further improved short-term stability. After adjustment for chronological age, heart age gap was associated with PWV (partial r=0.2627, P<0.001); each 1-year increase corresponded to 0.062 m/s higher PWV, and accelerated versus decelerated heart aging was associated with 0.91 m/s higher adjusted PWV. Each 1-SD increase in adjusted heart age gap was associated with greater odds of prevalent hypertension (OR 1.72, 95% CI 1.49-1.99), while the highest versus lowest quartile had an OR of 4.25. These findings support smartwatch PPG-derived heart age gap as a scalable digital biomarker of arterial stiffness and prevalent hypertension.

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arXiv2025

Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation

Translating non-invasive signals such as photoplethysmography (PPG) and ballistocardiography (BCG) into clinically meaningful signals like arterial blood pressure (ABP) is vital for continuous, low-cost healthcare monitoring. However, temporal misalignment in multimodal signal transformation impairs transformation accuracy, especially in capturing critical features like ABP peaks. Conventional synchronization methods often rely on strong similarity assumptions or manual tuning, while existing Learning with Noisy Labels (LNL) approaches are ineffective under time-shifted supervision, either discarding excessive data or failing to correct label shifts. To address this challenge, we propose ShiftSyncNet, a meta-learning-based bi-level optimization framework that automatically mitigates performance degradation due to time misalignment. It comprises a transformation network (TransNet) and a time-shift correction network (SyncNet), where SyncNet learns time offsets between training pairs and applies Fourier phase shifts to align supervision signals. Experiments on one real-world industrial dataset and two public datasets show that ShiftSyncNet outperforms strong baselines by 9.4%, 6.0%, and 12.8%, respectively. The results highlight its effectiveness in correcting time shifts, improving label quality, and enhancing transformation accuracy across diverse misalignment scenarios, pointing toward a unified direction for addressing temporal inconsistencies in multimodal physiological transformation.

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arXiv2024

Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning

Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios.

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arXiv2024

Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure

Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative models. Despite these advances, the efficacy of these models is curtailed by the latent space shift predicament, stemming from alterations in PPG data distribution across disparate hardware and individuals, potentially leading to distorted ABP waveforms. To tackle this problem, we present an innovative solution named the Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to yield robust latent spaces by employing multiple discretizing bases. To facilitate improved reconstruction, the Correlation-boosted Attention Module (CAM) is introduced to systematically query pertinent bases on a global scale. Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum Enhancement Knowledge (MSEK), which fosters local information flow within the channels of latent code and provides additional embedding for reconstruction. Through comprehensive experimentation on both publicly available datasets and a private downstream task dataset, the proposed approach demonstrates noteworthy performance enhancements compared to existing methods. Extensive ablation studies further substantiate the effectiveness of each introduced module.

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