Frontiers

Virtual reality-induced emotion recognition with deep learning-based multimodal physiological feature fusion

Recognizing emotions objectively and accurately remains challenging because of the limited ecological validity, informational incompleteness, and constrained model performance of conventional ...
The Lancet

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study

aDepartment of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA bComputational Health Informatics Program, Boston Children's Hospital, ...
GitHub

ECG-DualNet: Atrial Fibrillation Classification in Electrocardiography using Deep Learning

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 ...
GitHub

ECGTwinMentor simulates a digital twin of an ECG diagnostic system. Users can input or generate ECG parameters, choose a diagnosis they believe is correct, and compare it with ...

Version 1.0.0 consolidates the architecture, machine learning model, and multiplatform deployment strategy for educational and predictive use of ECG data. ECGTwinMentor simulates a digital twin of an ...
Europe PMC

Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model.

The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity ...
The Lancet

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study

aDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China bSchool of Artificial Intelligence ...
BMJ

Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine

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