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

doi:10.1007/s11633-019-1219-2

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	Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm
	Santanu Sahoo 2; Asit Subudhi 2; Manasa Dash 3; Sukanta Sabut 1
刊名	International Journal of Automation and Computing
	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
DOI	10.1007/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.
内容类型	期刊论文
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 Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar 751030, India 3.Department of Mathematics, Silicon Institute of Technology, Bhubaneswar 751024, 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.