Efficient ECG classification based on Chi-square distance for arrhythmia detection  

作　　者：Dhiah Al-Shammary Mustafa Noaman Kadhim Ahmed M.Mahdi Ayman Ibaida Khandakar Ahmedb 

机构地区：[1]College of Computer Science and Information Technology,University of Al-Qadisiyah,Al Diwaniyah,58001,Iraq [2]Intelligent Technology Innovation Lab,Victoria University,Melbourne,3011,Australia

出　　处：《Journal of Electronic Science and Technology》2024年第2期1-15,共15页电子科技学刊（英文版）

摘　　要：This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

关 键 词：Arrhythmia classification Chi-square distance Electrocardiogram(ECG)signal Particle swarm optimization(PSO) 

分 类 号：R737.33[医药卫生—肿瘤] TP181[医药卫生—临床医学]