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

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.

Identification of Electrocardiographic Patterns Related to Mortality with COVID-19

“…However, interpretation is highly dependent on cardiologists' experience and can be affected by limitations in the data or intra-subject and intrarater variability [14][15][16]. In the context of the detection and investigation of cardiac risk associated with COVID-19, the use of machine learning and deep learning methods has already been largely investigated [17][18][19][20][21][22]. The methods proposed in [17][18][19][20][21][22] showed good accuracy, ranging from 85% to 100%, in detecting cardiovascular changes that may be related to COVID-19.…”

“…In the context of the detection and investigation of cardiac risk associated with COVID-19, the use of machine learning and deep learning methods has already been largely investigated [17][18][19][20][21][22]. The methods proposed in [17][18][19][20][21][22] showed good accuracy, ranging from 85% to 100%, in detecting cardiovascular changes that may be related to COVID-19. However, complex deep learning models, such as convolutional neural networks (CNN), are not easily interpretable due to their nature (being black boxes).…”

“…With this aim, the presented paper contributed to revealing the hidden electrocardiographic and vectorcardiographic patterns related to mortality with COVID-19, assessing the reliability of NNs created by AdvRS&LP in electrocardiographic feature interpretation and investigating interpretable solutions to support clinical practice using AI-based methods. In the context of the detection and investigation of cardiac risk associated with COVID-19, the use of machine learning and deep learning methods has already been largely investigated [17][18][19][20][21][22]. The methods proposed in [17][18][19][20][21][22] showed good accuracy, ranging from 85% to 100%, in detecting cardiovascular changes that may be related to COVID-19.…”

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

“…To reliably and automatically detect (identify or predict) COVID-19 in its early phases, various medical imaging techniques, such as chest radiographs, ECG trace images, and CT-scans have been used [7]. Chest radiographs, often known as X-rays, are images of the inside of the chest that are utilized to examine chest problems [1].…”

“…Chest radiographs, often known as X-rays, are images of the inside of the chest that are utilized to examine chest problems [1]. ECG trace images are line graphs that depict variations in the heart's electrical behavior over time [7]. On the other hand, a chest CT scan employs an X-ray scanner to produce a sequence of high-resolution images of locations inside the chest [8].…”