Data Generation Strategies for Enhanced Atrial Fibrillation Prediction in Pacemaker Patients-Reference-Cited by-同舟云学术

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Data Generation Strategies for Enhanced Atrial Fibrillation Prediction in Pacemaker Patients

Published:2023-11-15 Issue: Volume:13 Page:1-10
ISSN:
Container-title:2023 First International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC)
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Author:

Ngom Charles Abdoulaye¹,Ba Mandicou¹,Sarr Simon Antoine²,Diop Idy¹,Bah Alassane¹,Diao Maboury²

Affiliation:

1. Cheikh Anta Diop University of Dakar,Polytechnic Higher School Research Institute for Development Unit of Mathematical Modeling and Computer Science of Complex Systems,Dakar,Senegal

2. Cheikh Anta Diop University,Department of Cardiology, University Hospital Aristide Le Dantec Medical Department,Dakar,Senegal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10464383/10465269/10465446.pdf?arnumber=10465446

Reference24 articles.

1. Prospective National Study of the Prevalence, Incidence, Management and Outcome of a Large Contemporary Cohort of Patients With Incident Non‐Valvular Atrial Fibrillation

2. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study.

3. Atrial fibrillation from an engineering perspective;L,2019

4. DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine

5. Generation of 12-Lead Electrocardiogram with Subject-Specific, Image-Derived Characteristics Using a Conditional Variational Autoencoder

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