ComEngApp : Computer Engineering and Applications Journal
Vol 10 No 1 (2021)

Cloud-based ECG Interpretation of Atrial Fibrillation Condition with Deep Learning Technique

Bambang Tutuko (Unknown)
Rossi Passarella (Unknown)
Firdaus Firdaus (Unknown)
Muhammad Naufal Rachmatullah (Sriwijaya University)
Annisa Darmawahyuni (Unknown)
Ade Iriani Sapitri (Unknown)
Siti Nurmaini (Unknown)

Article Info

Publish Date
01 Feb 2021


The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99%. The overall satisfaction performance for the process of AF screening, and it is feasible to conduct AF screening by using a telehealth monitoring system containing an embedded cloud-computing algorithm.

Copyrights © 2021

Journal Info





Computer Science & IT Engineering


ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal ...