CardioGPT: An ECG Interpretation Generation Model-Reference-Cited by-同舟云学术

CardioGPT: An ECG Interpretation Generation Model

Published:2024 Issue: Volume:12 Page:50254-50264
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Fu Guohua¹,Zheng Jianwei²^ORCID,Abudayyeh Islam²^ORCID,Ani Chizobam²,Rakovski Cyril³,Ehwerhemuepha Louis⁴,Lu Hongxia⁵^ORCID,Guo Yongjuan⁶,Liu Shenglin¹,Chu Huimin⁶,Yang Bing¹^ORCID

Affiliation:

1. School of Medicine, Tongji University, Shanghai, China

2. Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA

3. Schmid College of Science and Technology, Chapman University, Orange, CA, USA

4. Children's Hospital of Orange County, Orange, CA, USA

5. School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

6. The First Affiliated Hospital of Ningbo University, Zhejiang, Ningbo, China

Funder

Fundamental Public Welfare Research Project of Zhejiang Province

Zhejiang Provincial Foundation for Medical and Health Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10488393.pdf?arnumber=10488393

Reference36 articles.

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2. Guidelines for electrocardiography. A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Committee on Electrocardiography).

3. A confident decision support system for interpreting electrocardiograms

4. ECG pattern recognition and classification using non-linear transformations and neural networks: A review

5. Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations