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.

Authors: Dong-hyun Kim, Jaemin Lee, Yeong Joon Gil and Jong Deok Kim

Conference: ACCSE 2018

Publisher: IARIA XPS Press

Conference Date: 2018. 7. 22

Proceeding: PDF