用于心脏骤停检测的智能自动体外除颤器的特征强化

M. Nguyen, Huu-Thang Nguyen, Hai-Chau Le
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引用次数: 0

摘要

心脏骤停是由被称为心室颤动和室性心动过速的震荡节律引起的。通过自动体外除颤实现的快速诊断可导致电击,从而提高生存机会。本文提出了一种新的方法来设计一种有效的体外除颤的冲击建议算法。特征选择算法采用k近邻算法和模糊c均值聚类算法,选出15个最优特征,生成增强特征。使用交叉验证程序,考虑了各种机器学习方法用于最优特征集和整个输入特征的性能估计。仿真结果表明,该算法的准确率为99.01%,灵敏度为99.14%,特异性为98.97,表明该算法具有在临床环境中实际应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Reinforcement in Intelligent Automated External Defibrillators for Sudden Cardiac Arrest Detection
Sudden cardiac arrests are caused by shockable rhythms known as ventricular fibrillation and ventricular tachycardia. Rapid diagnosis implemented by the automated external defibrillation results in electrical shock, which improves the chance of survivals. In this paper, a novel method is developed to design an effective shock advice algorithm in the automated external defibrillation. An optimal set of 15 features are selected carefully by the feature selection algorithm using K-nearest neighbors and the fuzzy C-mean clustering, which produces reinforced features. Various machine learning methods are considered for the performance estimation of the optimal feature set and entire input features using cross validation procedure. The simulation results, which are accuracy of 99.01%, sensitivity of 99.14%, specificity of 98.97, show that the proposed shock advice algorithm for the automated external defibrillation is potential for practical application in real clinic environment.
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