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Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

S. R. Deepa1, M. Subramoniam2,*, R. Swarnalatha3, S. Poornapushpakala2, S. Barani2

1 Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India
2 School of EEE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India
3 Department of EEE, BITS Pilani, Dubai Campus, Dubai International Academic City, P.O Box No. 345055, Dubai, UAE

* Corresponding Author: M. Subramoniam. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 745-761. https://doi.org/10.32604/iasc.2023.034211

Abstract

The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and Tompkins algorithm is employed to detect the envelope of Q, R and S waves in the preprocessing stage. It uses a recursive filter to eliminate muscle noise, T-wave interference and baseline wander. As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal, the feature extraction involves using frequency contents obtained from multiple wavelet filters; bi-orthogonal, Symlet and Daubechies at different resolution levels in the feature extraction stage. Then, Black Widow Optimization (BWO) is applied to optimize the hybrid wavelet features in the feature optimization stage. Finally, a kernel based Support Vector Machine (SVM) is employed to classify heartbeats into five classes. In SVM, Radial Basis Function (RBF), polynomial and linear kernels are used. A total of ∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database for performance evaluation of the proposed CAHRD system. Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis. It correctly classifies five classes of heartbeats with 99.91% accuracy using an RBF kernel with 2nd level wavelet coefficients. The CAHRD system achieves an improvement of ∼6% over random projections with the ensemble SVM approach and ∼2% over morphological and ECG segment based features with the RBF classifier.

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APA Style
Deepa, S.R., Subramoniam, M., Swarnalatha, R., Poornapushpakala, S., Barani, S. (2023). Meta-heuristic optimized hybrid wavelet features for arrhythmia classification. Intelligent Automation & Soft Computing, 37(1), 745-761. https://doi.org/10.32604/iasc.2023.034211
Vancouver Style
Deepa SR, Subramoniam M, Swarnalatha R, Poornapushpakala S, Barani S. Meta-heuristic optimized hybrid wavelet features for arrhythmia classification. Intell Automat Soft Comput . 2023;37(1):745-761 https://doi.org/10.32604/iasc.2023.034211
IEEE Style
S.R. Deepa, M. Subramoniam, R. Swarnalatha, S. Poornapushpakala, and S. Barani, “Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification,” Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 745-761, 2023. https://doi.org/10.32604/iasc.2023.034211



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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