Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

Abstract

The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

“…In this method, the fitness of population has been calculated by applied various optimum values of parameters. Melgani et al [5] explained the classification system of PSO-SVM. Particle Swarm Optimizationisusedtoimprovethe performance rate of the Support Vector Machine classifier.…”

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

“…In this method, the fitness of population has been calculated by applied various optimum values of parameters. Melgani et al [5] explained the classification system of PSO-SVM. Particle Swarm Optimizationisusedtoimprovetheperfrmance rate of the Support Vector Machine classifier.…”

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

“…As can be seen, SVM and decision tree have been used to diagnosis of breast cancer and obtain the considerable classification accuracy. However, we believe that the SVM has really not fully shown its advantage; hence in our work, we optimize SVM with genetic algorithm (GA) and particle swarm optimization (PSO) [33] and compare the classification results with random forest. Following, we shortly introduce these classification methods.…”