A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals

A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals

Abstract Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced data...

Full description

Saved in:
Bibliographic Details
Main Authors: Md. Alamin Talukder, Majdi Khalid, Mohsin Kazi, Nusrat Jahan Muna, Mohammad Nur-e-Alam, Sajal Halder, Nasrin Sultana
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Arrhythmia detection
ConvNeXt models
Data balancing techniques
SMOTE-TomekLink
Deep learning in healthcare
Electrocardiogram classification
Online Access:https://doi.org/10.1038/s41598-024-81992-w
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items