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

Bambang Tutuko; Rossi Passarella; Firdaus Firdaus; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Siti Nurmaini

doi:10.18495/comengapp.v10i1.356

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Affiliations

Bambang Tutuko
Affiliation not stated

Rossi Passarella
Affiliation not stated

Firdaus Firdaus
Affiliation not stated

Muhammad Naufal Rachmatullah
Sriwijaya University

Annisa Darmawahyuni
Affiliation not stated

Ade Iriani Sapitri
Affiliation not stated

Siti Nurmaini
Affiliation not stated

Content Top

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

Bambang Tutuko ,
Rossi Passarella ,
Firdaus Firdaus ,
Muhammad Naufal Rachmatullah ,
Annisa Darmawahyuni ,
Ade Iriani Sapitri ,
Siti Nurmaini

Vol 10 No 1 (2021)

DOI 10.18495/comengapp.v10i1.356

Submitted: Jan 20, 2021

Published: Feb 1, 2021

Abstract

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.