Jun Li
Jun Li has 3 papers in the ChatPPG research atlas (2024–2026), linked to 2 models, with work using 8 datasets and 23 co-authors in the atlas. Main topics: cross-modal, ECG, ppg, ecg, foundation-model, contrastive.
Papers
- AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling2026 · KDD 2026
AnyPPG pretrains a PPG encoder (Net1D/ResNet-style, 512-dim) using a CLIP-style cross-modal contrastive objective against a frozen ECG foundation model (ECGFounder) instead of PPG-only self-supervised learning. Pretraining data is 109,909 hours / 58,796 subjects of synchronized PPG+ECG from five clinical sources (MC-MED, PulseDB [MIMIC+VitalDB derived], MESA, HSP, CFS) — largely ICU and sleep-lab, not wearable. At inference only PPG is needed (ECG branch is pretraining scaffolding).
- Venue: KDD 2026 (ACM SIGKDD V.2)
- Subjects: 58796
- Signals: ppg, ecg
- PPG Heart Age via ECG-Guided Cross-Modal Pretraining (arXiv:2608.01620)2026 · arXiv preprint
This study estimates heart age from smartwatch PPG, using synchronized smartwatch ECG to guide pretraining so that only PPG is needed in use. It draws on three OPPO cohorts in China with 581,804 participants and 7,452,131 recordings. The PPG-only model estimated age with errors of 5.9 years (r = 0.82) and 4.3 years (r = 0.80) in two external cohorts, and a larger heart-age gap was linked to higher pulse wave velocity.
- Published: arXiv preprint, 2026
- Cohort: 581,804 participants, 7,452,131 recordings (3 OPPO cohorts)
- Signals: smartwatch PPG (ECG-guided pretraining)
- ECGFounder: An ECG Foundation Model Built on 10+ Million Recordings2024 · arXiv preprint
ECGFounder is a general-purpose ECG foundation model trained on more than 10 million recordings with real-world annotations from cardiology experts. On held-out validation it reached expert-level performance, with AUROC above 0.95 for 80 diagnoses.
- Published: arXiv preprint, 2024
- Training data: 10M+ ECG recordings with expert annotations
- Signals: ECG
Models
Datasets used in this work
- Brno University of Technology Smartphone PPG Database (BUT PPG)
- Cleveland Family Study (CFS): NSRR family sleep-apnea PSG
- Gyro-Acc-PPG (BAMI-I, Wonkwang): 3-channel green wrist PPG + accel + gyro, treadmill
- Human Sleep Project (BDSP): multi-site clinical PSG
- PPG-DaLiA: PPG dataset for motion compensation / HR
- WenXinWuYang: HeartVoice handheld single-lead ECG + finger PPG recordings (private; AnyPPG eval)
- MESA Sleep: NSRR polysomnography + wrist actigraphy (Multi-Ethnic Study of Atherosclerosis)
- UCI Cuff-Less Blood Pressure Estimation (UCI 340; Kachuee 2015)