Gongzheng Tang
Gongzheng Tang has 2 papers in the ChatPPG research atlas (2024–2026), linked to 1 model, with work using 8 datasets and 11 co-authors in the atlas. Main topics: ppg, ecg, foundation-model, contrastive, ecg-guided, phenome-wide.
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
- A Scoping Review of Deep Learning Methods for Photoplethysmography Data2024 · arXiv preprint
This scoping review covers deep learning methods for PPG data. It finds that deep learning generally beats handcrafted-feature approaches and is more flexible, but notes ongoing problems: few large high-quality datasets, little real-world validation, and concerns about interpretability, scalability and efficiency.
- Published: arXiv preprint, 2024
- Type: scoping review
- Signals: PPG
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)