Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification.

ABSTRACT:

Background

Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety o

SUBMITTER: Darmawahyuni A 

PROVIDER: S-EPMC8802771 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature