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Fusion: Practice and Applications
Volume 9 , Issue 1, PP: 47-58 , 2022 | Cite this article as | XML | Html |PDF


Intelligent Data Fusion Model for Electrocardiogram Classification for Efficient Decision Making in the Healthcare Sector

Authors Names :   Mahmoud A. Zaher   1 *     Nabil M. Eldakhly   2  

1  Affiliation :  Faculty of Artificial Intelligence, Egyptian Russian University (ERU), Cairo, Egypt

    Email :  Mahmoud.zaher@eru.edu.eg

2  Affiliation :  Department of Computer and Information Systems Sadat Academy for Management Sciences (SAMS), Cairo, Egypt


Doi   :   https://doi.org/10.54216/FPA.090104

Received: April 15, 2022 Accepted: August 20, 2022

Abstract :

Automatic classification of biomedical signals helps to perform decision making in the healthcare sector. Electrocardiogram (ECG) is a commonly employed 1-dimensional biomedical signal that can be utilized for the detection and classification of cardiovascular diseases. The recently developed deep learning (DL) models find useful for the detection and classification of ECG signals for cardiovascular diseases. With this motivation, this study develops an intelligent electrocardiogram classification using sailfish optimization algorithm with gated recurrent unit (SFOA-GRU) technique. The goal of the SFOA-GRU model is to detect the existence of cardiovascular disease by the classification of ECG signals. The SFOA-GRU model initially undergoes data pre-processing step to transform the actual values into useful format. Besides, GRU model is applied for the detection and classification of ECG signals. For improving the classification outcomes of the GRU model, the SFOA has been utilized to optimally adjust the hyper parameters involved in it. A wide-ranging experimental analysis is carried out to demonstrate the enhanced outcomes of the SFOA-GRU model. A comprehensive comparative study highlighted the promising performance of the SFOA-GRU model over the other recent approaches using different measures parameters.

Keywords :

ECG signals; Intelligent models; Data Fusion; Decision making; Cardiovascular disease; Deep learning.

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Cite this Article as :
Mahmoud A. Zaher , Nabil M. Eldakhly, Intelligent Data Fusion Model for Electrocardiogram Classification for Efficient Decision Making in the Healthcare Sector, Fusion: Practice and Applications, Vol. 9 , No. 1 , (2022) : 47-58 (Doi   :  https://doi.org/10.54216/FPA.090104)