National Taiwan Ocean University Institutional Repository:Item 987654321/52580
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28611/40652
Visitors : 779903      Online Users : 44
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item:

Title: Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network
Authors: U. Rajendra Acharya
Hamido Fujita
Shu Lih Oh
Jen Hong Tan
Muhammad Adam
Arkadiusz Gertych
Yuki Hagiwara
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Automated external defibrillator (AED)
ECG signals
Ventricular arrhythmias
Date: 2018
Issue Date: 2019-11-22
Publisher: Future Generation Computer Systems
Abstract: Abstract: Ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening shockable arrhythmias which require immediate attention. Cardiopulmonary resuscitation (CPR) and defibrillation are highly recommended means of immediate treatment of these shockable arrhythmias and to resume spontaneous circulation. However, to increase efficacy of defibrillation by an automated external defibrillator (AED), an accurate distinction of shockable ventricular arrhythmias from non-shockable ones needs to be provided upfront. Therefore, in this work, we have proposed a novel tool for an automated differentiation of shockable and non-shockable ventricular arrhythmias from 2 s electrocardiogram (ECG) segments. Segmented ECGs are processed by an eleven-layer convolutional neural network (CNN) model. Our proposed system was 10-fold cross validated and achieved maximum accuracy, sensitivity and specificity of 93.18%, 95.32% and 91.04% respectively. Its high performance indicates that shockable life-threatening arrhythmia can be immediately detected and thus increase the chance of survival while CPR or AED-based support is performed. Our tool can also be seamlessly integrated with an ECG acquisition systems in the intensive care units (ICUs).
Relation: 79 pp.952-959
Appears in Collections:[Department of Computer Science and Engineering] Periodical Articles

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback