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A new BAT optimization algorithm based feature selection method for electrocardiogram heartbeat classification using empirical wavelet transform and Fisher ratio
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2020-04-20 , DOI: 10.1007/s13042-020-01128-0
Atul Kumar Verma , Indu Saini , Barjinder Singh Saini

In this paper, a novel feature selection method is proposed for the categorization of electrocardiogram (ECG) heartbeats. The proposed technique uses the Fisher ratio and BAT optimization algorithm to obtain the best feature set for ECG classification. The MIT-BIH arrhythmia database contains sixteen classes of the ECG heartbeats. The MIT-BIH ECG arrhythmia database divided into intra-patient and inter-patient schemes to be used in this study. The proposed feature selection methodology works in following steps: firstly, features are extracted using empirical wavelet transform (EWT) and then higher-order statistics, as well as symbolic features, are computed for each decomposed mode of EWT. Thereafter, the complete feature vector is obtained by the conjunction of EWT based features and RR interval features. Secondly, for feature selection, the Fisher ratio is utilized. It is optimized by using BAT algorithm so as to have maximal discrimination of the between classes. Finally, in the classification step, the k-nearest neighbor classifier is used to classify the heartbeats. The performance measures i.e., accuracy, sensitivity, positive predictivity, specificity for intra-patient scheme are 99.80%, 99.80%, 99.80%, 99.987% and for inter-patient scheme are 97.59%, 97.589%, 97.589%, 99.196% respectively. The proposed feature selection technique outperforms the other state of art feature selection methods.



中文翻译:

基于经验小波变换和Fisher比的基于BAT优化算法的心电图心跳分类特征选择新方法

本文针对心电图(ECG)心跳的分类提出了一种新的特征选择方法。所提出的技术使用Fisher比率和BAT优化算法来获得用于ECG分类的最佳特征集。MIT-BIH心律失常数据库包含16种心电图心律。MIT-BIH心电图心律失常数据库分为患者内和患者间方案。提出的特征选择方法按以下步骤工作:首先,使用经验小波变换(EWT)提取特征,然后为EWT的每种分解模式计算高阶统计量以及符号特征。此后,通过结合基于EWT的特征和RR间隔特征获得完整的特征向量。其次,对于特征选择,利用费舍尔比率。通过使用BAT算法对其进行优化,以最大程度地区分类别之间。最后,在分类步骤中,k最近邻居分类器用于对心跳进行分类。病人内计划的绩效指标,即准确性,敏感性,阳性预测性,特异性分别为99.80%,99.80%,99.80%,99.987%和病人间计划的绩效指标分别为97.59%,97.589%,97.589%,99.196%。所提出的特征选择技术优于其他最新的特征选择方法。

更新日期:2020-04-21
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