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Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm

文献类型：期刊论文

作者Santanu Sahoo3; Asit Subudhi3; Manasa Dash2; Sukanta Sabut1
刊名International Journal of Automation and Computing
出版日期2020
卷号17期号:4页码:551-561
关键词Electrocardiogram (ECG) cardiac arrhythmias empirical mode decomposition (EMD) variational mode decomposition (VMD) hybrid features decision tree classifier.
ISSN号1476-8186
DOI10.1007/s11633-019-1219-2
英文摘要Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram (ECG) signals. In a life-threatening situation, an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate. In this paper, we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals. The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database. The feature set comprises eight empirical mode decomposition (EMD) based features, three features from variational mode decomposition (VMD) and four features from RR intervals. In total, 15 features are ranked according to a ranker search approach and then used as input to the support vector machine (SVM) and C4.5 decision tree classifiers for classifying six types of arrhythmia beats. The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89% compared to cubic-SVM classifier which achieved an accuracy of 95.35% only. Besides accuracy measures, all other parameters such as sensitivity (Se), specificity (Sp) and precision rates of 95.68%, 99.28% and 95.8% was achieved better in C4.5 classifier. Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM. The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
源URL[http://ir.ia.ac.cn/handle/173211/42278]  
专题自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位1.School of Electronics Engineering, KIIT deemed to be University, Bhubaneswar 751024, India
2.Department of Mathematics, Silicon Institute of Technology, Bhubaneswar 751024, India
3.Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar 751030, India
推荐引用方式
GB/T 7714		 Santanu Sahoo,Asit Subudhi,Manasa Dash,et al. Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm[J]. International Journal of Automation and Computing,2020,17(4):551-561.
APA Santanu Sahoo,Asit Subudhi,Manasa Dash,&Sukanta Sabut.(2020).Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm.International Journal of Automation and Computing,17(4),551-561.
MLA Santanu Sahoo,et al."Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm".International Journal of Automation and Computing 17.4(2020):551-561.

入库方式： OAI收割

来源：自动化研究所

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