COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

Rahman, Tawsifur; Akinbi, Alex; Chowdhury, Muhammad E. H.; Rashid, Tarik A.; Şengür, Abdulkadir; Khandakar, Amith; Islam, Khandaker Reajul; Ismael, Aras Masood

doi:10.1007/s13755-021-00169-1

Cited by 60 publications

(19 citation statements)

References 54 publications

(77 reference statements)

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“…Rahman et al [54] presented various approaches for the diagnosis of ECG image reports from the dataset in Khan and Hussain [35]. Numerous pre-trained models were applied, such as Resnet18, Resnet50, Resnet101, InceptionV3, DenseNet201, and MobileNetv2.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed an accuracy of 100% and 99.8% on the two experiments, respectively, whereas the authors in Ozdemir et al [50] achieved an accuracy of 96.2% and 93.0%, respectively. The experiments performed by authors in Rahman et al [54] and Anwar et al [4] were conducted with balanced data using the same dataset in our study. The results obtained by the ECGConvnet using SVM on the former experiments showed higher performance than their results.…”

Section: Discussionmentioning

confidence: 99%

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Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

Bassiouni

Hegazy

Rizk

et al. 2022

Circuits Syst Signal Process

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One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

Bassiouni

Hegazy

Rizk

et al. 2022

Circuits Syst Signal Process

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“…Attia et al [27] performed a proof of concept study on non-invasive drug assessment based on ECG signals. Rahman et al [28,29] tried to early diagnose COVID-19 using ECG trace images.…”

Section: Related Workmentioning

confidence: 99%

LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

Le¹,

Pham²,

Nguyen³

et al. 2022

Preprint

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Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads. Specifically, we use three separate Onedimensional Convolutional Neural Networks (1D-CNNs) as backbones to extract features from three input ECG leads separately. The architecture of 1D-CNNs is redesigned for high performance and low computational cost. A novel Lead-wise Attention module is then introduced to aggregate outputs from these three backbones, resulting in a more robust representation which is then passed through a Fully-Connected (FC) layer to perform classification. Moreover, to make the system's prediction clinically explainable, the Grad-CAM technique is modified to produce a high meaningful lead-wise explanation. Finally, we employ a pruning technique to reduce system size, forcing it suitable for deployment on hardware-constrained platforms. The proposed lightweight, explainable system is named LightX3ECG. We got classification performance in terms of F1 scores of 0.9718 and 0.8004 on two large-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which surpassed current state-of-the-art methods while achieving higher computational and storage efficiency. Visual examinations and a sanity check were also performed to demonstrate the strength of our system's interpretability. To encourage further development, our source code is publicly available at https://github.com/lhkhiem28/LightX3ECG.

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“…In the first one, the combination of ECG data from both positive COVID-19 and no-findings (normal) has been classified, obtaining an accuracy of 96.20%, while, in the second method, the ECG combination that is comprised of negative (normal, abnormal, and myocardial-infraction) and positive (COVID-19) have been evaluated, obtaining an accuracy of 93%. Tawsifur et al [4] In this paper, we propose a system for detecting the COVID-19 based on the ECG analysis of the distinct acquired images using different CNN models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The virus has attracted an intimidating concern, due to its unbelievable spread among people, in addition to its critical damages that have targeted the respiratory system [3]. Moreover, it has been observed that it has a vital effect on the cardiovascular system, resulting in multi-organ failure [4]. Thus, the Electrocardiograms (ECG) analysis can provide a fast and cost-efficient technique to detect the presence of COVID-19 infection.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Electrocardiograms Classification using CNN Models

Shahin

Nassif

Alsabek

2021

2021 14th International Conference on Developments in eSystems Engineering (DeSE)

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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.

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COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

Cited by 60 publications

References 54 publications

Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

COVID-19 Electrocardiograms Classification using CNN Models

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