A GAN-Based Approach for ECG Reconstruction from Doppler Sensor Signals

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.