eprintid: 17853 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/53 datestamp: 2025-09-22 23:30:08 lastmod: 2025-09-22 23:30:10 status_changed: 2025-09-22 23:30:08 type: article metadata_visibility: show creators_name: Ikram, Sunnia creators_name: Ikram, Amna creators_name: Singh, Harvinder creators_name: Ali Awan, Malik Daler creators_name: Naveed, Sajid creators_name: De la Torre Díez, Isabel creators_name: Fabian Gongora, Henry creators_name: Chio Montero, Thania creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: henry.gongora@uneatlantico.es creators_id: title: Transformer-based ECG classification for early detection of cardiac arrhythmias ispublished: pub subjects: uneat_bm divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: public keywords: cardiac monitoring, ECG classification, electrocardiogram analysis, PCA, t-SNE, Transformer-based model, VPC, feature engineering 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 publication: Frontiers in Medicine volume: 12 id_number: doi:10.3389/fmed.2025.1600855 refereed: TRUE issn: 2296-858X official_url: http://doi.org/10.3389/fmed.2025.1600855 access: open language: en citation: Artículo Materias > Biomedicina Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Abierto Inglés 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. metadata Ikram, Sunnia; Ikram, Amna; Singh, Harvinder; Ali Awan, Malik Daler; Naveed, Sajid; De la Torre Díez, Isabel; Fabian Gongora, Henry y Chio Montero, Thania mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, henry.gongora@uneatlantico.es, SIN ESPECIFICAR (2025) Transformer-based ECG classification for early detection of cardiac arrhythmias. Frontiers in Medicine, 12. ISSN 2296-858X document_url: http://repositorio.unib.org/id/eprint/17853/1/fmed-12-1600855.pdf