Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

Abstract

Sudden cardiac death remains a significant concern, emphasizing the critical need for intelligent diagnostic techniques in cardiac disease detection. Cardiologic Vascular Diseases (CVD) pose a significant global health challenge, often associated with unhealthy lifestyle habits. Electrocardiogram (ECG) monitoring plays a crucial role in assessing heart function, but its interpretation can be challenging due to signal complexity. This study proposes a novel approach utilizing fused Slantlet-Spatial features alongside Auto Regression (AR) coefficients to classify electrocardiogram (ECG) signals. Leveraging the Slantlet Transform (SLT) for its superior time localization capabilities and the simplicity of AR coefficients, this methodology aims to enhance the accuracy of cardiac illness classification. Nineteen distinct features were extracted from 300 ECG signals, each comprising 512 samples, enriching the classification scheme's efficacy. The fused feature extraction technique demonstrated success in improving overall accuracy. Employing Binary Particle Swarm Optimization (BPSO), irrelevant features were pruned, resulting in a refined feature vector of only 14 relevant features. This reduction in feature dimensionality significantly influenced the classification accuracy. Subsequently, the refined feature vector was introduced to the classification stage, employing four distinct classifiers: Support Vector Machine (SVM) with various kernel functions, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), and Decision Tree (DT). Simulation results revealed the feasibility of achieving an average classification accuracy of 99.67% across ECG signal categories. Notably, the polynomial kernel function-SVM classifier, with only 2-fold cross-validation, yielded optimal performance. This study emphasizes the potential of fused Slantlet-Spatial and AR features in enhancing the accuracy of cardiac disease classification. Moreover, the efficacy of BPSO in feature selection and the superior performance of the SVM classifier highlight promising avenues for further research in intelligent cardiac diagnosis systems. The proposed approach outperforms existing methods and contributes to the early detection of cardiovascular abnormalities, potentially improving patient outcomes.