Learning Generative Model of ECG from PPG and Application to Activity Classification Task

Abstract

Heart information, especially ECG, is often used to estimate a person's internal state and behavior. However, accurate ECG measurement requires specialized equipment and the cooperation of medical staff, and although wearable devices that can measure ECG have appeared, there are some limitations. In contrast, since PPG measures blood flow in the wrist, it can only indirectly obtain heart information, but it can be measured more affordably and continuously with smartwatches and other devices. Therefore, this study focuses on generating ECGs signal from easily measurable PPGs. The method utilizes Rectified Flow to learn the shortest path between two distributions, enhancing ECG signal quality by incorporating peak position information from both signals. Experiments with the dataset in which ECGs and PPGs were measured simultaneously showed that our approach yields higher-quality ECGs than traditional diffusion models. Additionally, using generated ECGs as training data enhances activity classification performance compared to using PPGs.