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. 2022 Nov 4:9:1005920.
doi: 10.3389/fmed.2022.1005920. eCollection 2022.

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging

Marriam Nawaz  1   2 , Tahira Nazir  1   3 , Ali Javed  2 , Khalid Mahmood Malik  4 , Abdul Khader Jilani Saudagar  5 , Muhammad Badruddin Khan  5 , Mozaherul Hoque Abul Hasanat  5 , Abdullah AlTameem  5 , Mohammed AlKhathami  5
Affiliations

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging

Marriam Nawaz et al. Front Med (Lausanne). .

Abstract

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

Keywords: COVID-19; ECG; Efficient-ECGNet; computer vision; deep learning; medical imaging.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow diagram of ECG image-based COVID-19 classification using Efficient-ECGNet.
FIGURE 2
FIGURE 2
Sample images of all classes.
FIGURE 3
FIGURE 3
Visual demonstration of MBConv4, Fused-MBConv4, and SE blocks.
FIGURE 4
FIGURE 4
Pictorial demonstration of training loss.
FIGURE 5
FIGURE 5
Training accuracy curve.
FIGURE 6
FIGURE 6
Evaluation of proposed method through (A) Precision and (B) Recall.
FIGURE 7
FIGURE 7
F1_score and Error rate of the proposed technique.
FIGURE 8
FIGURE 8
Confusion matrix of the presented work.
FIGURE 9
FIGURE 9
Evaluation through AUC measure.
FIGURE 10
FIGURE 10
Heatmaps attained with the proposed approach.
FIGURE 11
FIGURE 11
Similarity with Healthy class.
FIGURE 12
FIGURE 12
Similarity with AHB class.
FIGURE 13
FIGURE 13
Similarity with MI class.
FIGURE 14
FIGURE 14
Similarity with PMI class.
FIGURE 15
FIGURE 15
COVID similarity with other classes.
See this image and copyright information in PMC

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