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

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Abstract

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 classier 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.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar, 751030, India

    Santanu Sahoo & Asit Subudhi

  2. Department of Mathematics, Silicon Institute of Technology, Bhubaneswar, 751024, India

    Manasa Dash

  3. School of Electronics Engineering, KIIT deemed to be University, Bhubaneswar, 751024, India

    Sukanta Sabut

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  1. Santanu Sahoo
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  2. Asit Subudhi
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  3. Manasa Dash
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Correspondence to Sukanta Sabut.

Additional information

Santanu Sahoo received the B.Eng. degree in electronics & communication engineering from Utkal University, India in 2004, the M. Eng. degree in communication system engineering and the Ph. D. degree in electronics engineering from Siksha “O” Anusandhan University, India in 2009 and 2018, respectively. He is working as an associate professor in Department of ECE, Institute of Technical Education & Research, Siksha “O” Anusandhan University, India. He has over 15 years of teaching and research experience. He has published research papers in journals and conferences.

His research interests include biomedical signal and machine learning approaches.

Asit Subudhi received the B. Eng. degree in electronics & communication from BPUT, India in 2005, the M. Eng. degree in communication system engineering from BPUT, India in 2010, and received the Ph. D. degree in electronic engineering from SOA University, India in 2018. Presently, he is working as associate professor in Department of ECE, ITER, SOA University, India. He has over 12 years of experience in teaching and his research expertise focuses on signal and image processing, VLSI design. He is a member of IEEE.

His research interests include signal and image processing.

Manasa Dash received the M. Sc., M. Phil and Ph. D. degrees in mathematics in 1995, 1997 and 2014 respectively from Utkal University, India. Presently, she is working as an associate professor in Silicon Institute of Technology, India. She has published many papers in referred internal journals and conferences. She has over 18 years of experience in both teaching and research.

Her research interests include optimization engineering, linear algebra, numerical methods, random fourier series, and probability & statistics.

Sukant Sabut received the B.Eng. in electronics & communication engineering from Visvesvaraya Technological (VT) University of Karnataka, India in 2001, the M. Tech. degree in biomedical instrumentation engineering from the VT University of Karnataka, India in 2005, and the Ph. D. degree in medical science & technology from IIT Kharagpur, India in 2011. He was working as an associate professor, SOA University, India from 2013 to 2017. Presently, he is working as an associate professor in School of Electronics Engineering, KIIT Deemed to be University, India. He is a member of IEEE, IET, IFESS, Rehabilitation council of India and the Institution of Engineers (India). He is the author of more 63 articles in reputed internal journals and conferences. He has over 19 years of experience in both teaching and research.

His research interests include biomedical signal and image analysis, machine learning for medical, neural and rehabilitation engineering.

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Sahoo, S., Subudhi, A., Dash, M. et al. Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm. Int. J. Autom. Comput. 17, 551–561 (2020). https://doi.org/10.1007/s11633-019-1219-2

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