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

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dc.contributor.author김병남-
dc.contributor.author유선국-
dc.date.accessioned2020-04-13T17:09:42Z-
dc.date.available2020-04-13T17:09:42Z-
dc.date.issued2020-05-
dc.identifier.issn2168-2194-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/175672-
dc.description.abstractRecently, 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%.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.isPartOfIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleSynthesis of Electrocardiogram V Lead Signals from Limb Lead Measurement using R peak Aligned Generative Adversarial Network-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학교실)-
dc.contributor.googleauthorJeeEun Lee-
dc.contributor.googleauthorKyeongTaek Oh-
dc.contributor.googleauthorByeongnam Kim-
dc.contributor.googleauthorSun Kook Yoo-
dc.identifier.doi10.1109/JBHI.2019.2936583-
dc.contributor.localIdA00495-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ03267-
dc.identifier.eissn2168-2208-
dc.identifier.pmid31443057-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8808874-
dc.contributor.alternativeNameKim, Byeong Nam-
dc.contributor.affiliatedAuthor김병남-
dc.contributor.affiliatedAuthor유선국-
dc.citation.volume24-
dc.citation.number5-
dc.citation.startPage1265-
dc.citation.endPage1275-
dc.identifier.bibliographicCitationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol.24(5) : 1265-1275, 2020-05-
dc.identifier.rimsid64737-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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