Abstract
Electrocardiogram (ECG) classification is essential for accurately detecting and tracking heart rhythm disorders. This study proposes a multi-class ECG classification framework for identifying cardiac arrhythmias like Atrial Fibrillation (AF), Ventricular Fibrillation (VF), Normal Rhythm (NR), and Ventricular Tachycardia (VT). The ECG signals were decomposed using Variational Mode Decomposition (VMD), and higher-order statistics as well as entropy-based features were extracted from each mode. Multi-task Particle Swarm Optimization (MT-PSO) was employed to reduce redundant features and enhance the discriminative capability of the dataset. Multiple machine-learning models were evaluated, and optimized feature set led to clear performance improvements. The best results were obtained using LightGBM (ACC 0.993), HistGradientBoost (0.991), XGBoost (0.990), and ExtraTrees (0.990). Execution time also decreased for several models after optimization. Confusion-matrix and ROC analyses confirmed reliable detection across all four cardiac classes, and comparison with reported works shows that the proposed framework offers competitive or improved performance for ECG classification.
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Data availability
The data used to support the findings of this study are available from publicly available third-party repositories hosted on PhysioNet (https://www.physionet.org/). No new datasets were generated during the current study.
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Conceptualization: Y Murali Krishna; Data curation: K Padma Vasavi; Formal analysis: Y Murali Krishna; Investigation: M Krishna Chaitanya; Methodology: Y Murali Krishna; Resources: N Udaya Kumar; Supervision:Beebi Naseeba; Validation: Jammisetty Yedukondalu; Writing original draft: M Krishna Chaitanya; Writing review & editing: Jammisetty Yedukondalu;
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Krishna, Y.M., Vasavi, K.P., Chaitanya, M.K. et al. Automated multi-class ECG arrhythmia detection using VMD and multi-task optimization. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44103-5
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DOI: https://doi.org/10.1038/s41598-026-44103-5
