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Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.

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Bibliographic Details
Title:	Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study.
Authors:	Kwon S; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Hong J; School of Electrical Engineering, KAIST, Daejeon, Republic of Korea., Choi EK; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Lee E; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Hostallero DE; School of Electrical Engineering, KAIST, Daejeon, Republic of Korea., Kang WJ; School of Electrical Engineering, KAIST, Daejeon, Republic of Korea., Lee B; Sky Labs Inc, Seongnam, Republic of Korea., Jeong ER; Department of Information and Communication Engineering, Hanbat National University, Daejeon, Republic of Korea., Koo BK; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Oh S; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Yi Y; School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
Source:	JMIR mHealth and uHealth [JMIR Mhealth Uhealth] 2019 Jun 06; Vol. 7 (6), pp. e12770. Date of Electronic Publication: 2019 Jun 06.
Publication Type:	Journal Article; Research Support, Non-U.S. Gov't
Journal Info:	Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101624439 Publication Model: Electronic Cited Medium: Print ISSN: 2291-5222 (Print) Linking ISSN: 22915222 NLM ISO Abbreviation: JMIR Mhealth Uhealth Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2291-5222
DOI:	10.2196/12770