COECG-resnet-GWO-SVM: an optimized COVID-19 electrocardiography classification model based on resnet50, grey wolf optimization and support vector machine

Paper ID : 1000-CUAINEXUS2026-FULL

Authors

Nour Eldeen Khalifa * Cairo University

Abstract

Coronavirus disease 2019 (COVID-19) has swiftly spread throughout the globe, causing widespread infection in various countries and regions, and was declared a pandemic by World Health Organization (WHO) in 2020. Computer algorithms and models can help in the identification and classification of the COVID-19 virus in the medical domain, especially in CT, and X-rays and Electrocardiography tests with rapid and accurate results. In this paper, a COVID-19 electrocardiography classification model based on grey wolf optimization and support vector machine will be presented. A public online electrocardiography dataset was investigated in this paper with two classes (COVID-19, and Normal. The proposed model consists of three phases. The first phase is the feature extraction based on Resnet50. The second phase is the feature selection based on grey wolf optimization. A public online electrocardiography dataset was investigated in this paper with two classes (COVID-19, and Normal. The proposed model consists of three phases. The first phase is the feature extraction based on Resnet50. The second phase is the feature selection based on grey wolf optimization.

Keywords

COVID-19

Status: Accepted