Open Access

ARTICLE

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

by Marwa Obayya¹, Nadhem NEMRI², Lubna A. Alharbi³, Mohamed K. Nour⁴, Mrim M. Alnfiai⁵, Mohammed Abdullah Al-Hagery⁶, Nermin M. Salem⁷, Mesfer Al Duhayyim^8,*

1 Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Science and Art at Mahayil, King Khalid University, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Tabuk University, Saudi Arabia
4 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
5 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
6 Department of Computer Science, College of Computer, Qassim University, Saudi Arabia
7 Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt
8 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email:

Computers, Materials & Continua 2023, 74(2), 3151-3166. https://doi.org/10.32604/cmc.2023.032765

Received 28 May 2022; Accepted 14 July 2022; Issue published 31 October 2022

Abstract

With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECG Signal Classification (IBADL-BECGC) approach. To accomplish this, the proposed IBADL-BECGC model initially pre-processes the input signals. Besides, IBADL-BECGC model applies NasNet model to derive the features from test ECG signals. In addition, Improved Bat Algorithm (IBA) is employed to optimally fine-tune the hyperparameters related to NasNet approach. Finally, Extreme Learning Machine (ELM) classification algorithm is executed to perform ECG classification method. The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset. The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

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Keywords

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