Premature Ventricular Contraction Recognition using a Fuzzy Maximum Approaching Degree

Eder Pereira Neves, B. R. Oliveira, M. A. Q. Duarte, J. Vieira Filho
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Abstract

This work presents a new methodology for ventricular premature contraction arrhythmias recognition using a set of geometrical attributes recently proposed and a fuzzy maximum approaching degree.  Pattern models based on triangular and trapezoidal membership functions are proposed and a committee comprising these functions is composed using some statistical data, beyond a mechanism for manual selection of attributes and automatic weighting for each attribute. The obtained results show the efficiency and validity of the proposed approach, with 99.07%, 98.36% and 99.79% of accuracy, sensibility and specificity, respectively, as good as the ones obtained by the state-of-art methods.
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利用模糊最大逼近度识别室性早搏
这项工作提出了一种新的方法室性早搏心律失常识别使用一组几何属性最近提出的模糊最大接近度。提出了基于三角形和梯形隶属函数的模式模型,并利用统计数据组成了一个由这些函数组成的委员会,超越了手动选择属性和每个属性自动加权的机制。结果表明,该方法的准确性、敏感性和特异性分别为99.07%、98.36%和99.79%,与现有方法相当。
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