IEEE Access | |
Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network | |
Sukkyu Sun1  Hee Chan Kim2  Heean Shin2  Joonnyong Lee3  | |
[1] Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea;Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea;Mellowing Factory Company Ltd., Seoul, Republic of Korea; | |
关键词: Data augmentation; deep learning; electrocardiogram; generative adversarial networks; photoplethysmogram; | |
DOI : 10.1109/ACCESS.2021.3078534 | |
来源: DOAJ |
【 摘 要 】
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
【 授权许可】
Unknown