Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …

The PhysioNet/Computing in Cardiology Challenge 2021 focused on the identification of
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …

Heart disease is one of the leading causes of mortality throughout the world. Among the
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …

This paper introduces a winning solution (team ISIBrno-AIMT) to the PhysioNet Challenge
2021. The method is based on the ResNet deep neural network architecture with a multi …

In PhysioNet/Computing in Cardiology Challenge 2020, we developed an ensembled model
based on SE-ResNet to classify cardiac abnormalities from 12-lead electrocardiogram …

Objective. This work tries to provide answers to several critical questions on varying-
dimensional electrocardiography (ECG) raised by the PhysioNet/Computing in Cardiology …

Over the past few years, research has witnessed the advancement of deep learning models
trained on large datasets, some even encompassing millions of examples. While these …

Objective Chest pain is common, and current risk-stratification methods, requiring 12-lead
electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly …

The ECG classification is a critical task in the early and correct diagnosis of cardiovascular
diseases. Although various models have been developed to tackle the heartbeat …

This paper proposes a deep neural network architecture to perform multi-label classification
of 26 cardiac abnormalities from 12-lead and reduced lead ECG data. The model was …