CardioFlow: Learning to Generate ECG from PPG with Rectified Flow

Yuta Nambu, Masahiro Kohjima, Ryuji Yamamoto

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.