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

Abstract

AbstractCoronavirus 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. The third phase is the classification based on the support vector machine. The experimental trials show that the proposed model achieves the highest accuracy possible when it is compared with other models that use different feature extraction and selection models, such as Alexnet and whale optimization algorithms. Also, the proposed model achieves the highest testing accuracy possible with 99.1% while related work that used hexaxial feature mapping and deep learning achieved 96.20% with an improvement of 2.9%. The achieved testing accuracy and its performance metrics such as Precision, Recall, and F1 Score support the research findings that the proposed model, while achieving the highest accuracy possible, it also consumes less time in the training by selecting a minimum number of features if it is compared with other related works which use the same dataset.