Artificial intelligence (AI)鈥揺nabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients ...
The ECG images are initially scanned at 300 dpi. The image is then reduced to 76.5 dpi and compressed using the GIF 89a format. The background colour white is made transparent so that the grid can show through. The grid is simply defined as a webpage background and each small square is...
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Cardiovascular diseases are a global health challenge that necessitates improvements in diagnostic accuracy and efficiency. This study examines the potential of deep learning (DL) models for the classification of electrocardiogram (ECG) images to assist
of Distinct ECG ImagesSample RateLeads 1 COVID-19 Patients 250 500 Hz 12 – Leads 2 Normal Person ECG Images 859 3 Myocardial Infarction Patient 77 4 Patients with Previous History of Myocardial Infarction 203 5 Patients with Abnormal Heartbeat 548 1.1. COVID-19 Coronavirus commonly known as ...
Despite the high accuracy of AI-based automated analysis of 12-lead ECG images for classification of cardiac conditions, clinical integration of such tools is hindered by limited interpretability of model recommendations. We aim to demonstrate the feasib
The easiest and best way to learn to read electrocardiograms (ECGs), designed for doctors, nurses, students, and technicians working in the healthcare industry.
disp(['Number of validation images: ',num2str(numel(imgsValidation1.Files))]); 引入网络模型GoogLeNet (net1 = googlenet) net1 = googlenet; lgraphGN = layerGraph(net1); numberOfLayers = numel(lgraphGN.Layers); figure('Units','normalized','Position',[0.1 0.1 0.8 0.8]); ...
The integration of multicycle ECG images and the FL function substantially enhances the model's ability to capture ECG patterns, particularly for minority classes. In addition, our model exhibits satisfactory classification performance on unseen data from new patients. These findings suggest that the ...
Our classification system utilizes the "ECG Images dataset of Cardiac Patients", comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which ...