This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...

This repository includes the code of the ECG-DualNet for ECG classification proposed in the paper Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in ...

Abstract: Detection and classification of electrocardiogram (ECG) signals is critically linked to the diagnosis of cardiac abnormalities. In this paper, a novel approach for ECG classification is ...

The integration of these technologies ensures that ECG machines are accurate, reliable, and user-friendly, making them indispensable tools in modern healthcare. These techniques and algorithms ensure ...

With the Sabarimala pilgrimage set to begin shortly, Cardiology at Doorstep (CAD) Foundation, floated by a group of health professionals in Mangaluru, donated two ECG machines to ‘Devotee Doctors of ...

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost ...

Artificial intelligence–enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains ...

Right ventricular ejection fraction (RVEF) and end‐diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning–enabled ECG analysis for estimation of right ...

Scripts and modules for training and testing neural network for ECG automatic classification. Companion code to the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network".

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