TY  - JOUR
N2  - 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.
AV  - public
UR  - http://doi.org/10.3389/fmed.2025.1600855
A1  - Ikram, Sunnia
A1  - Ikram, Amna
A1  - Singh, Harvinder
A1  - Ali Awan, Malik Daler
A1  - Naveed, Sajid
A1  - De la Torre Díez, Isabel
A1  - Fabian Gongora, Henry
A1  - Chio Montero, Thania
Y1  - 2025/08//
TI  - Transformer-based ECG classification for early detection of cardiac arrhythmias
KW  - cardiac monitoring
KW  -  ECG classification
KW  -  electrocardiogram analysis
KW  -  PCA
KW  -  t-SNE
KW  -  Transformer-based model
KW  -  VPC
KW  -  feature engineering
ID  - uninipr17853
VL  - 12
SN  - 2296-858X
JF  - Frontiers in Medicine
ER  -