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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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Publication Year
2020-04-23
Journal
ACM International Conference Proceeding Series
Publisher
Association for Computing Machinery
Citation
ACM International Conference Proceeding Series, pp.135-139
Keyword
cloud computing frameworkCompatibleECG automatic diagnosisprototype applicationpython and MATLAB hybrid programming
Mesh Keyword
Automatic classificationAutomatic diagnosisDiagnosis algorithmsHealth diagnosisHealth statusHybrid programmingMobile applicationsPersonal use
All Science Classification Codes (ASJC)
SoftwareHuman-Computer InteractionComputer Vision and Pattern RecognitionComputer Networks and Communications
Abstract
The 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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36612
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85092235196&origin=inward
DOI
https://doi.org/10.1145/3404555.3404640
Journal URL
http://portal.acm.org/
Type
Conference
Funding
This 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).
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