A Pilot Study: ECG Reconstruction from PPG
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Abstract
In this work, we study the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) signals and infer the waveform of ECG using PPG measurement. As the first work to address this inverse problem, we propose and train a linear transform which maps the discrete cosine transform (DCT) coefficients of the PPG cycle to those of the corresponding ECG cycle. The resulting DCT coefficients of the ECG cycle are then inversely transformed to obtain the predicted ECG waveform. 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. 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.
Framework
Related Publications
Q. Zhu, X. Tian, C-W. Wong, and M. Wu: “ECG Reconstruction via PPG: A Pilot Study,” IEEE International Conf. on Biomedical and Health Informatics (BHI’19), Chicago, IL, May 2019. [43/394=10.9% acceptance rate for oral-presentation of regular paper] [IEEE Xplore] [arXiv] [Slides]
Q. Zhu, X. Tian, C.-W. Wong and M. Wu, “Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG”, IEEE Internet of Things Journal [IEEE Explore][BioRxiv].