Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

Heean Shin,Sukkyu Sun,Hee Chan Kim,Joonnyong Lee

doi:10.1109/access.2021.3078534

Abstract

Photoplethysmogram (PPG) is one of the most widely measured biosignals alongside electrocardiogram (ECG). Due to the simplicity of measurement and the advent of wearable devices, there have been growing interest in using PPG for a variety of healthcare applications such as cardiac function estimation. However, unlike ECG, there are not many large databases available for clinically significant analyses of PPG. To overcome this issue, a Generative Adversarial Network-based model to generate PPG using ECG as input is proposed. The network was trained using a large open database of biosignals measured from surgical patients and was externally validated using an alternative database sourced from another hospital. The generated PPG was compared with the reference PPG using percent root mean square difference (PRD) and Pearson correlation coefficient to evaluate the morphological similarity. Additionally, heart rate measured from the reference ECG, reference PPG, and generated PPG, and compared through repeated measure analysis of variance to test for any significant differences. The mean PRD was 32± 10% and the mean correlation coefficient was 0.95± 0.05 in the test dataset. The HR from the three biosignals showed no significant difference with a $p$ -value of 0.473. When the optimized GAN model was tested on atrial fibrillation ECG from a third dataset, the mean correlation coefficient between the generated PPG heart rate and the ECG heart rate was 0.94± 0.15, with paired t-test resulting in $p$ -value of 0.64. The results indicate that the proposed method may provide a valuable alternative to augmenting biosignal databases that are abundant in one signal while lacking in another.

Highlights

Photoplethysmogram (PPG) is a blood pulse signal measured using light reflection or transmission, which amplitude and morphology are dependent on the blood volume and vascularity of the measured tissue
By validating the generative adversarial network (GAN)-generated PPG in terms of error, correlation, and atrial fibrillation, this study demonstrates that GAN can be a potential tool to generate synthetic biosignals for data augmentation purposes in low resource settings
The discriminator and generator loss of the GAN optimization process for input ECG length of 1 second could be seen on Fig. 7, and an example of a simultaneously measured ECG-PPG pair and the corresponding GAN-generated PPG can be seen on Fig. 8

Summary

Introduction

Photoplethysmogram (PPG) is a blood pulse signal measured using light reflection or transmission, which amplitude and morphology are dependent on the blood volume and vascularity of the measured tissue. Since 2016, studies on blood pressure estimation [9], [11], [12], biometric identification [13], [14], and atrial fibrillation detection [15]–[18] from PPG signals using deep learning has become popular. Some of these studies were able to use readily available public databases for training the deep learning models [19]–[21], but others required conducting large-scale experiments to produce the necessary data [15], [22], [23].

Methods

Results

Conclusion