Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG

Abstract

This article studies the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) and investigates the inference of the ECG waveforms from the PPG signals that can be obtained from affordable wearable Internet-of-Things (IoT) devices for mobile health. 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 based on the proposed cardiovascular signal model. The proposed method is evaluated with different morphologies of the PPG and ECG signals on three benchmark data sets with a variety of combinations of age, weight, and health conditions under several training setups. The experimental results show that the proposed method can achieve a high prediction accuracy greater than 0.92 in averaged correlation for each data set when the model is trained subjectwise. With a signal processing and learning system that is designed synergistically, we are able to reconstruct ECG signals by exploiting the relation of these two types of cardiovascular measurement. The reconstruction capability of the proposed method can enable low-cost ECG screening from affordable wearable IoT devices for continuous and long-term monitoring. This work opens up a new research direction to transfer the clinical ECG knowledge base to build a knowledge base for PPG and sensing data from wearable devices.