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题名: Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network
作者: U. Rajendra Acharya
Hamido Fujita
Shu Lih Oh
Jen Hong Tan
Muhammad Adam
Arkadiusz Gertych
Yuki Hagiwara
贡献者: 國立臺灣海洋大學:資訊工程學系
关键词: Automated external defibrillator (AED)
ECG signals
Ventricular arrhythmias
日期: 2018
上传时间: 2019-11-22
出版者: Future Generation Computer Systems
摘要: 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).
關聯: 79 pp.952-959
显示于类别:[資訊工程學系] 期刊論文


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