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A Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application
  • Zhang, Zhen ;
  • Li, Hongqiang ;
  • Gong, Zheng ;
  • Jin, Rize ;
  • Chung, Tae Sun
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dc.contributor.authorZhang, Zhen-
dc.contributor.authorLi, Hongqiang-
dc.contributor.authorGong, Zheng-
dc.contributor.authorJin, Rize-
dc.contributor.authorChung, Tae Sun-
dc.date.issued2020-04-23-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/36612-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85092235196&origin=inward-
dc.description.abstractThe ECG signal analysis and diagnosis algorithms have been studied for decades. There are some state of art algorithms that have been developed. In this paper, we proposed a compatible ECG automatic diagnosis Cloud Computing framework in order to integrate these exist algorithms. On the other hand, there are many studies regarding the IoT based health diagnosis system. But there are few of that aiming at the personal use health monitor and diagnose. Basing on our proposed framework, users can diagnose their heart health status by themselves conveniently anywhere and anytime through the mobile application. The ECG character automatic classification computing algorithm is compatible for Python and MATLAB by introducing the hybrid programming technic on the cloud computing side. So that, it is easy for researchers to integrate their developed algorithm into this framework to build an application quickly. We developed a prototype application as well to verify the availability of this framework.-
dc.description.sponsorshipThis work was supported by the Tianjin Major Project for Civil-Military Integration of Science and Technology under Grant 18ZXJMTG00260 and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1F1A1058548).-
dc.language.isoeng-
dc.publisherAssociation for Computing Machinery-
dc.subject.meshAutomatic classification-
dc.subject.meshAutomatic diagnosis-
dc.subject.meshDiagnosis algorithms-
dc.subject.meshHealth diagnosis-
dc.subject.meshHealth status-
dc.subject.meshHybrid programming-
dc.subject.meshMobile applications-
dc.subject.meshPersonal use-
dc.titleA Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application-
dc.typeConference-
dc.citation.conferenceDate2020.4.23. ~ 2020.4.26.-
dc.citation.conferenceName6th International Conference on Computing and Artificial Intelligence, ICCAI 2020-
dc.citation.editionICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence-
dc.citation.endPage139-
dc.citation.startPage135-
dc.citation.titleACM International Conference Proceeding Series-
dc.identifier.bibliographicCitationACM International Conference Proceeding Series, pp.135-139-
dc.identifier.doi10.1145/3404555.3404640-
dc.identifier.scopusid2-s2.0-85092235196-
dc.identifier.urlhttp://portal.acm.org/-
dc.subject.keywordcloud computing framework-
dc.subject.keywordCompatible-
dc.subject.keywordECG automatic diagnosis-
dc.subject.keywordprototype application-
dc.subject.keywordpython and MATLAB hybrid programming-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaSoftware-
dc.subject.subareaHuman-Computer Interaction-
dc.subject.subareaComputer Vision and Pattern Recognition-
dc.subject.subareaComputer Networks and Communications-
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