IR-ECG: Invertible Reconstruction of ECG

Electrocardiogram(ECG) reconstruction is studied to rebuild ECG from heterogeneous biosignal, such as Photoplethysmography(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.