Electrocardiogram (ECG) Reconstruction From Photoplethysmogram (PPG)

Published in Electrical & Computer Engineering, University of Maryland, Collge Park, 2018

Abstract

In this project, we study the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) and infer the waveform of ECG via the PPG signals. Methods: In order to address this inverse problem, a transform is proposed to map the discrete cosine transform (DCT) coefficients of each PPG cycle to those of the corresponding ECG cycle. The resulting DCT coefficients of the ECG cycle are inversely transformed to obtain the reconstructed ECG waveform. Results: The proposed method is evaluated with the different morphologies of the PPG and ECG signals on three benchmark datasets with a variety of combinations of age, weight, and health conditions using different training modes. Experimental results show that the proposed method can achieve a high prediction accuracy greater than 0.92 in averaged correlation for each dataset when the model is trained subject-wise. Conclusion: With a signal processing and learning system that is designed synergistically, we are able to reconstruct ECG signal by exploiting the relation of these two types of cardiovascular measurement. Significance: The reconstruction capability of the proposed method may enable low-cost ECG screening for continuous and long-term monitoring. This work may open up a new research direction to transfer the understanding of clinical ECG knowledge base to build a knowledge base for PPG and data from wearable devices.

Publications

  1. Q. Zhu, X. Tian, C.-W. Wong, and M. Wu, “ECG Reconstruction via PPG: A Pilot Study”, 2019 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019. [43/394=11% acceptance rate for oral presentation of regular paper][IEEE Xplore][arXiv][Slides]
  2. X. Tian, Q. Zhu, Y. Li, and M. Wu, “Cross-domain Joint Dictionary Learning fro ECG Reconstruction From PPG”, submitted for conference publication.
  3. Q. Zhu, X. Tian, C.-W. Wong and M. Wu, “Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG”, submitted for journal publication. [bioRxiv]