Bridging ECG and PPG: Latent-Space Prediction for Robust Physiological Analysis
Summary
Zhaoliang Chen, Saurabh Kataria, Minxiao Wang, Xiao Hu. Nell Hodgson Woodruff School of Nursing, Emory University. KDD '26, DOI 10.1145/3770855.3819000, open access CC BY 4.0.
Key facts
- License
- CC BY 4.0
Reported benchmarks
AFib / arrhythmia
Stanford wrist PPG AF (DeepBeat) · Macro-F1 ↑
Predefined split, frozen encoder; F1 — not comparable to AUROC rows; 11M 0.791
Cuffless blood pressure
pulsedb-vital · MAE (mmHg) ↓
Vital-BP predefined split from PulseDB; PulseDB also in pretraining (overlap not discussed); clinical finger
Cuffless blood pressure
uci-bp · MAE (mmHg) ↓
Nested 5-fold CV (AnyPPG protocol); clinical finger
Heart rate estimation
Brno University of Technology Smartphone PPG Database (BUT PPG) · MAE (bpm) ↓
Nested 5-fold CV (AnyPPG protocol); smartphone PPG
Heart rate estimation
Gyro-Acc-PPG (BAMI-I, Wonkwang) · MAE (bpm) ↓
Nested 5-fold CV (AnyPPG protocol); treadmill wrist PPG
Heart rate estimation
IEEE Signal Processing Cup 2015 (TROIKA) · MAE (bpm) ↓
LOSO, frozen
Heart rate estimation
PPG-DaLiA · MAE (bpm) ↓
LOSO, frozen; PPG-only input
Heart rate estimation
WESAD · MAE (bpm) ↓
LOSO, frozen encoder (KDD'26 Table 2)
Updated: Oct 12, 2026
Related Research
- AFib / arrhythmia leaderboard
Benchmark results for afib / arrhythmia
- Cuffless blood pressure leaderboard
Benchmark results for cuffless blood pressure
- LIMA-Mamba: A lightweight and distribution-robust Mamba network for PPG-based biometrics
Research paper
- A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation
Seven 1D→2D transforms × several CNN variants on the UCI BP database. Best: CWT, errors reported as
- A Decodability-Grounded Audit of Clinical Prediction Claims in PPG Foundation Models
Audits frozen representations of four encoders on 37,784 MC-MED encounters via concept decodability,