A novel K-GWO-SVM algorithm for the analysis of ECG signals

This paper presents a robust yet efficient Grey Wolf Optimizer-Support Vector Machine algorithm, termed K-GWO-SVM, for the analysis of ECG signals in smart healthcare systems, aiming to improve classification accuracy and computational efficiency. The proposed model introduces three main contributions: (1) the use of GWO to automatically search for the optimal hyperparameters of SVM tailored to each dataset, (2) a mini-batch strategy guided by K-means clustering to improve the efficiency and convergence of GWO by selecting representative subsets of data, and (3) an enhanced regulation function integrated into GWO that prevents premature convergence by improving the balance between exploration and exploitation. A convergence study is conducted to demonstrate the influence of mini-batch size on both classification accuracy and computational efficiency, showing that using mini-batches as small as 10% of the training data significantly improves computational efficiency without compromising classification accuracy. The K-GWO-SVM framework is evaluated on two benchmark datasets: WESAD for emotion recognition and MIT-BIH Arrhythmia for cardiac classification. The proposed model achieves 99.02% accuracy on WESAD with over a 90% reduction in computational time (10% mini-batch), and 100% accuracy on MIT-BIH with over a 50% reduction in computational time (50% mini-batch), validating its effectiveness, robustness, and suitability for deployment in resource-constrained smart healthcare environments. • Robust yet efficient K-GWO-SVM algorithm is presented for ECG signal analysis. • A novel mini-batch technique is introduced to reduce computational complexity. • K-means defines centroids to form mini-batches for faster GWO convergence. • Convergence study demonstrates mini-batch size effects on accuracy and efficiency. • Dual validation on WESAD and MIT-BIH datasets proves clinical applicability.