Open Access

ARTICLE

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Sameer Alshetewi⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
3 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
4 General Information Technology Department, Ministry of Defense, The Executive Affairs, Excellence Services Directorate, Riyadh, 11564, Saudi Arabia
5 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
6 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
7 Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Saudi Arabia
8 Department of Computer Science, Faculty of Specific Education, Mansoura University, Egypt

* Corresponding Author: Faten Khalid Karim. Email:

Computers, Materials & Continua 2023, 74(2), 2379-2395. https://doi.org/10.32604/cmc.2023.032886

Received 01 June 2022; Accepted 05 July 2022; Issue published 31 October 2022

Abstract

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).

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Keywords

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