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 ...
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 ...
LinkedIn

Laplace Transforms and ECG Signal Analysis: Bridging Engineering Mathematics with Real-Time Healthcare Applications

Mathematics is often seen by first-year engineering students as a theoretical subject disconnected from real-world applications. However, when we bring real-time relevance into the ...
Nature

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on ...
The Lancet

Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database

aDepartment of Biomedical Engineering, Duke University, Durham, NC, USA bDepartment of Computer Science, Duke University, Durham, NC, USA cDepartment of Biostatistics & Bioinformatics, Duke University ...
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 ...
Nature

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from ...
BMJ

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

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be ...
ahajournals.org

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful ...