A Hybrid TD-PSO Feature Selection Approach for Accurate Arrhythmia Classification based on ECG Heart Signals

Abstract

Electrocardiogram (ECG) classification is a critical task for early detection of cardiac abnormalities. However, standard machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) face challenges when handling high-dimensional data. These classifiers often struggle to model complex relationships among features, leading to reduced classification performance. Existing metaheuristic-based feature selection methods typically search across the entire feature space, which increases computational complexity and often fails to isolate only the most informative features. This paper introduces a novel hybrid feature selection approach based on a two-phase framework combining Topsoe Distance (TD) and Particle Swarm Optimization (PSO). In the first phase, TD is used to eliminate irrelevant and redundant features, thereby reducing the feature space from 4000 to 1600 and preserving only the most relevant and harmonized features. In the second phase, PSO is applied to the reduced space, enabling efficient optimization and feature subset selection. Experimental results using the MIT-BIH Arrhythmia dataset demonstrate that the proposed TD-PSO method significantly improves classification accuracy compared to using PSO alone. Specifically, LR accuracy improved from 87% to 95%, KNN from 92% to 97%, SVM from 92% to 96%, and RF from 93% to 99%. These results confirm that narrowing the search space before optimization allows classifiers to perform more effectively while reducing computational cost. The proposed TD-PSO framework provides a robust and scalable solution for high-dimensional ECG classification and holds strong potential for broader applications in biomedical data analysis.