Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study-Reference-Cited by-同舟云学术

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

Published:2019-06-06 Issue:6 Volume:7 Page:e12770
ISSN:2291-5222
Container-title:JMIR mHealth and uHealth
language:en
Short-container-title:JMIR Mhealth Uhealth

Author:

Kwon Soonil^ORCID,Hong Joonki^ORCID,Choi Eue-Keun^ORCID,Lee Euijae^ORCID,Hostallero David Earl^ORCID,Kang Wan Ju^ORCID,Lee Byunghwan^ORCID,Jeong Eui-Rim^ORCID,Koo Bon-Kwon^ORCID,Oh Seil^ORCID,Yi Yung^ORCID

Publisher

JMIR Publications Inc.

Subject

Health Informatics

Reference29 articles.

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3. Usefulness of Continuous Electrocardiographic Monitoring for Atrial Fibrillation

4. Stroke as the Initial Manifestation of Atrial Fibrillation

5. Stroke patients with atrial fibrillation have a worse prognosis than patients without: data from the Austrian Stroke registry

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2. Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms;IEEE Journal of Biomedical and Health Informatics;2024-05

3. Photoplethysmography based atrial fibrillation detection: a continually growing field;Physiological Measurement;2024-04-01

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