# COVID-19 Electrocardiograms Classification using CNN Models

arXiv: https://arxiv.org/abs/2112.08931 · HTML: https://mlsanity.com/paper/2112.08931/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation](https://mlsanity.com/paper/1804.00516/) (2018)
- [Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC](https://mlsanity.com/paper/2010.08770/) (2020)
- [COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network](https://mlsanity.com/paper/2106.00436/) (2021)
- [Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview](https://mlsanity.com/paper/2011.14445/) (2020)
- [CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions](https://mlsanity.com/paper/2102.05894/) (2021)
- [Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions](https://mlsanity.com/paper/2008.07343/) (2020)
- [COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation](https://mlsanity.com/paper/2110.06123/) (2021)

## Abstract (arXiv)

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.