Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease

Joy, S. Immaculate; Kumar, K. Senthil; Palanivelan, M.; Lakshmi, D. Rajya

doi:10.1109/icjece.2022.3228588

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease

Cited by 8 publications

References 52 publications

Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals

Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals

Detection and classification of electrocardiography using hybrid deep learning models

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology

Contact Info

Product

Resources

About