Rec-AUNet: Attentive UNet for Reconstruction of ECG from BCG

Electrocardiogram(ECG) is commonly utilized in clinical diagnosis and health monitoring. However, ECG acquisition is cumbersome because ECG electrodes must be attached to the skin tightly. Ballistocardiogram(BCG), originating from the heartbeat, can be captured without attaching the BCG sensors to the skin, making BCG collecting convenient and non-feeling. However, BCG diagnostic experience is limited compared with ECG. In this paper, we propose a model, called Rec-AUNet, to reconstruct ECG from BCG so that we can combine the convenient measurement of BCG and diagnostic experience on ECG. Rec-AUNet utilizes an attentive UNet-based neural network with encoding paths to better capture the temporal features and with attentive paths to preserve the spatial features. To better evaluate the coherence of the global waveform and the fidelity of the local physiological features between the synthetic ECG and the original ECG, we deliberately design the person identification task as the semantic metric. The synthetic ECG from our proposed model achieves an accuracy of 93.75% and 94.05% in person identification on the Kansas-Dataset and selfcollected data, respectively, outperforming existing algorithms.