A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification-Reference-Cited by-同舟云学术

A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification

Published:2024 Issue: Volume:12 Page:76003-76021
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Abbas Sidra¹^ORCID,Ojo Stephen²^ORCID,Krichen Moez³^ORCID,Alamro Meznah A.⁴,Mihoub Alaeddine⁵^ORCID,Vilcekova Lucia⁶^ORCID

Affiliation:

1. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan

2. Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC, USA

3. FCSIT, Al-Baha University, Al-Baha, Saudi Arabia

4. Department of Information Technology, College of Computer and Information Science, Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia

5. Department of Management Information Systems, College of Business and Economics, Qassim University, Buraidah, Saudi Arabia

6. Faculty of Management, Comenius University Bratislava, Bratislava, Slovakia

Funder

Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, through the Princess Nourah Bint Abdulrahman University Researchers Supporting Project,

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10531288.pdf?arnumber=10531288

Reference42 articles.

1. Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction

2. Detection of myocardial infarction in 12 lead ECG using support vector machine

3. Genes, Environment, and Cardiovascular Disease

4. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads

5. Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks