Citation Export
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Zhen | - |
dc.contributor.author | Li, Hongqiang | - |
dc.contributor.author | Gong, Zheng | - |
dc.contributor.author | Jin, Rize | - |
dc.contributor.author | Chung, Tae Sun | - |
dc.date.issued | 2020-04-23 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36612 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85092235196&origin=inward | - |
dc.description.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. | - |
dc.description.sponsorship | 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). | - |
dc.language.iso | eng | - |
dc.publisher | Association for Computing Machinery | - |
dc.subject.mesh | Automatic classification | - |
dc.subject.mesh | Automatic diagnosis | - |
dc.subject.mesh | Diagnosis algorithms | - |
dc.subject.mesh | Health diagnosis | - |
dc.subject.mesh | Health status | - |
dc.subject.mesh | Hybrid programming | - |
dc.subject.mesh | Mobile applications | - |
dc.subject.mesh | Personal use | - |
dc.title | A Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2020.4.23. ~ 2020.4.26. | - |
dc.citation.conferenceName | 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020 | - |
dc.citation.edition | ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence | - |
dc.citation.endPage | 139 | - |
dc.citation.startPage | 135 | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp.135-139 | - |
dc.identifier.doi | 10.1145/3404555.3404640 | - |
dc.identifier.scopusid | 2-s2.0-85092235196 | - |
dc.identifier.url | http://portal.acm.org/ | - |
dc.subject.keyword | cloud computing framework | - |
dc.subject.keyword | Compatible | - |
dc.subject.keyword | ECG automatic diagnosis | - |
dc.subject.keyword | prototype application | - |
dc.subject.keyword | python and MATLAB hybrid programming | - |
dc.type.other | Conference Paper | - |
dc.description.isoa | false | - |
dc.subject.subarea | Software | - |
dc.subject.subarea | Human-Computer Interaction | - |
dc.subject.subarea | Computer Vision and Pattern Recognition | - |
dc.subject.subarea | Computer Networks and Communications | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.