IR-ECG: Invertible Reconstruction of ECG

Abstract

Electrocardiogram(ECG) reconstruction is studied to rebuild ECG from heterogeneous biosignal, such as Photoplethys-mography(PPG), to combine the diagnosis experience of ECG and the convenient collection of PPG. Heterogeneous biosignals are information-lost channels compared with ECG, so simple mapping from the heterogeneous biosignals to ECG leads to unsatisfactory performance. In this paper, we present an invertible neural network, called IR-ECG(invertible reconstruction of ECG), to model the processes of ECG reconstruction. We deliberately design the invertible block, called TimeFlow, to construct the flow-based model. In the forward process, IR-ECG produces the heterogeneous biosignal and captures the distribution of lost information from ECG. In the backward process, ECG reconstruction is finished using a randomly-drawn latent vector and heterogeneous biosignal. Experimental results show that IR-ECG outperforms existing works in quantitative, visual and semantic evaluations.