A common misperception exists that electrocardiograms (ECGs) simply contain data about heart activity. However, modern ECGs enhanced with artificial intelligence (AI) can contain data about a ...
A machine-learning model based on Transformer architecture, a form of artificial intelligence originally developed for ...
Recognizing emotions objectively and accurately remains challenging because of the limited ecological validity, informational incompleteness, and constrained model performance of conventional ...
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 ...
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 ...
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 ...
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 ...
aDepartment of Biomedical Engineering, Duke University, Durham, NC, USA bDepartment of Computer Science, Duke University, Durham, NC, USA cDepartment of Biostatistics & Bioinformatics, Duke University ...
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 ...
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