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Machine Learning-based Arrhythmia Diagnosis Algorithm

Authors:
Donghyun Kim
Jae Min Lee
Yeong Joon Gil
Jong Deok Kim

Keywords: Arrhythmia; Atrial fibrillation; Machine Learning.

Abstract:
As interest in cardiovascular disease increases, there is significant development in real-time healthcare services and devices. This paper suggests a machine learning-based arrhythmia diagnosis algorithm for mobile healthcare systems linked to a wearable electrocardiogram measurement device. The system monitors electrocardiograms in real time and distinguishes among arrhythmia, normal, and noise signals. By regular monitoring using a mobile healthcare system linked to a wearable bio-signal measurement device, users can minimize the risk of chronic disease and maintain a healthy standard of living. In this paper, an arrhythmia diagnosis algorithm is suggested, realized, and evaluated based on bio-signals collected from a watch-type electrocardiogram device. In the process, the efficacy of the algorithm is established

Pages: 42 to 45

Copyright: Copyright (c) IARIA, 2018

Publication date: July 22, 2018

Published in: conference

ISSN: 2519-8459

ISBN: 978-1-61208-658-3

Location: Barcelona, Spain

Dates: from July 22, 2018 to July 26, 2018