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Synthesis of Electrocardiogram V Lead Signals from Limb Lead Measurement using R peak Aligned Generative Adversarial Network

Authors
 JeeEun Lee  ;  KyeongTaek Oh  ;  Byeongnam Kim  ;  Sun Kook Yoo 
Citation
 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, : epub., 2019 
Journal Title
 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 
ISSN
 2168-2194 
Issue Date
2019
Abstract
Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefore, in this study, V-lead ECG signals were synthesized from limb leads using an R-peak aligned generative adversarial network (GAN). The data used the Physikalisch-Technische Bundesanstalt (PTB) dataset provided by PhysioNet. First, R-peak alignment was performed to maintain the physiological information of the ECG. Second, time domain ECG was converted to bi-dimensional space by ordered time-sequence embedding. Finally, the GAN was learned through the pairs between the modified limb II (MLII) lead and each chest (V) lead. The result showed that the mean structural similarity index (SSIM) was 0.92, and the mean error rate of the percent mean square difference (PRD) of the chest leads was 7.21%.
Full Text
https://ieeexplore.ieee.org/document/8808874
DOI
10.1109/JBHI.2019.2936583
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Byeong Nam(김병남) ORCID logo https://orcid.org/0000-0003-2718-8616
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/175672
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