CardioFlow: Learning to Generate ECG from PPG with Rectified Flow

Abstract

This study tackles the problem of generating electrocardiograms (ECG) from photoplethysmograms (PPG) data collected by wearable devices such as smartwatches. Existing methods based on diffusion models successfully generate high-quality ECGs but suffer from slow inference since diffusion models iterate neural network processing to denoise noisy data. In this study, we propose a new solution, called CardioFlow, to this PPG-to-ECG translation problem; based on the recently proposed rectified flow framework, it can be interpreted as a one/few-step generative model. Furthermore, CardioFlow can computationally efficiently generate ECG-specific structures using masks highlighting PPG and ECG signal peaks. We conduct the experiments on two real-world biosignal datasets, WESAD and DALIA, and confirm that our method can generate high-quality ECGs faster than the existing diffusion-model-based methods.