COVID-19 Electrocardiograms Classification using CNN Models

TL;DR

This study explores using various CNN models, especially VGG16, to automatically diagnose COVID-19 from ECG data, achieving up to 85.92% accuracy, with potential improvements through dataset expansion and hyperparameter tuning.

Contribution

It introduces a novel deep learning framework utilizing multiple CNN architectures for COVID-19 diagnosis from ECG signals, highlighting VGG16's superior performance.

Findings

VGG16 achieved 85.92% accuracy in COVID-19 ECG classification.

Other CNN models showed lower accuracy due to small dataset size.

Hyperparameter optimization improved VGG16 performance.

Abstract

With the periodic rise and fall of COVID-19 and numerous countries being affected by its ramifications, there has been a tremendous amount of work that has been done by scientists, researchers, and doctors all over the world. Prompt intervention is keenly needed to tackle the unconscionable dissemination of the disease. The implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the fundamentals of deep learning algorithms. In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models. Several CNN models have been utilized in this proposed framework, including VGG16, VGG19, InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has outperformed the rest of the models, with an accuracy of 85.92%. Our results show a relatively low accuracy in the rest of the models compared to the VGG16 model, which is due to the small size of the utilized dataset, in addition to the exclusive utilization of the Grid search hyperparameters optimization approach for the VGG16 model only. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of all models by further expanding the dataset and adapting a suitable hyperparameters optimization technique.