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primary topic: Transformer-based ECG classification for early detection of cardiac arrhythmias

Transformer-based ECG classification for early detection of cardiac arrhythmias
type: AcademicArticle
type: Article
abstract: Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures. Principal component analysis (PCA), correlation analysis, and feature engineering is applied to retain the most informative features. To assess the discriminative quality of the selected features, t-distributed stochastic neighbor embedding (t-SNE) is used for visualization, revealing clear class separability in the transformed feature space. The refined dataset is then input to a Transformer- based model trained with optimized loss functions, regularization strategies, and hyperparameter tuning. The proposed model demonstrates strong performance on the MIT-BIH benchmark dataset, showing results consistent with or exceeding prior studies. However, due to differences in datasets and evaluation protocols, these comparisons are indicative rather than conclusive. The model effectively classifies ECG signals into categories such as Normal, atrial premature contraction (APC), ventricular premature contraction (VPC), and Fusion beats. These results underscore the effectiveness of Transformer-based models in biomedical signal processing and suggest potential for scalable, automated ECG diagnostics. However, deployment in real-time or resource-constrained settings will require further optimization and validation.
date: 2025-08
volume: 12
is part of: Frontiers in Medicine
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Transformer-based ECG classification for early detection of cardiac arrhythmias (Texto)
has document: Transformer-based ECG classification for early detection of cardiac arrhythmias (Otro)
subject: Biomedicine

Transformer-based ECG classification for early detection of cardiac arrhythmias (Texto)
type: Document
has file: fmed-12-1600855.pdf
has part: fmed-12-1600855.pdf

Frontiers in Medicine
type: Collection
issn: 2296858X

Amna Ikram
type: Person
given name: Amna
family name: Ikram

Malik Daler Ali Awan
type: Person
given name: Malik Daler
family name: Ali Awan

Henry Fabian Gongora
type: Person
given name: Henry
family name: Fabian Gongora

Isabel De la Torre Díez
type: Person
given name: Isabel
family name: De la Torre Díez

Sunnia Ikram
type: Person
given name: Sunnia
family name: Ikram

Thania Chio Montero
type: Person
given name: Thania
family name: Chio Montero

Sajid Naveed
type: Person
given name: Sajid
family name: Naveed

Harvinder Singh
type: Person
given name: Harvinder
family name: Singh

Transformer-based ECG classification for early detection of cardiac arrhythmias (Otro)
type: Document
is version of: Transformer-based ECG classification for early detection of cardiac arrhythmias (Texto)
is volatile version of: Transformer-based ECG classification for early detection of cardiac arrhythmias (Texto)
is index codes version of: Transformer-based ECG classification for early detection of cardiac arrhythmias (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17853 Transformer-based ECG classification for early detection of cardiac arrhythmias
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primary topic: Transformer-based ECG classification for early detection of cardiac arrhythmias

Biomedicine
type: Concept
preferred label: Biomedicina @es