基于多重分形及其关联特征组合的心电信号分类  

ECG signal classification based on combination of multifractal and its correlation features

作　　者：卢清[1] 李秋生[1] 叶莉华 许德鹏[1] Lu Qing;Li Qiusheng;Ye Lihua;Xu Depeng(School of Physics&Electronic Information,Gannan Normal University,Ganzhou Jiangxi 341000,China)

机构地区：[1]赣南师范大学物理与电子信息学院,江西赣州341000

出　　处：《计算机应用研究》2023年第10期3016-3021,共6页Application Research of Computers

基　　金：江西省教育厅科技项目(190772);江西省研究生创新专项基金项目(YC2021-S739)。

摘　　要：多重分形理论只是对分形体几何支集上任意一点观察到的奇异指数作统计分析。多重分形关联研究的是具有不同奇异指数的两点之间的空间关联特性,是对多重分形单点统计的推广,两者特性具有一定的互补性。为此研究了一种多重分形及其关联特征进行组合的心电信号分类方法。首先对四种类型心电信号的多重分形及其关联特性进行分析并获得各自的特征。然后结合概率分布以选择合适的特征进行组合,组合后的特征送入支持向量机中分类。该方法在MIT-BIH心律失常数据库上进行了测试,经过20次训练测试得到97.90%的平均准确率。相比独立运用多重分形特征,该方法获得的分类准确率有明显提高。The multifractal theory only performs statistical analysis on the singular exponents observed at any point on the geometric support of the fractal body.Multifractal correlation studies the spatial correlation characteristics between two points with different singular exponents.It is a generalization of single point statistics in multifractals.The two characteristics have a certain degree of complementarity.This paper investigated a method that combined the multifractal and its correlation features in the electrocardiogram(ECG)signal classification.Firstly,the method analyzed the multifractal and correlation characteristics of four types of ECG signals and obtained their respective features.Then it combined the probability distribution to select appropriate features for combination.The combined features fed into a support vector machine(SVM)for classification.This method tested on the MIT-BIH arrhythmia database and achieved an average accuracy of 97.90%after 20 training tests.Compared to independently using multifractal features,this method significantly improves the classification accuracy.

关 键 词：心电分类 多重分形 多重分形关联 支持向量机 

分 类 号：TP911.7[自动化与计算机技术]