The proposed PSO-SVM algorithm achieved a classification accuracy of 97.21% for ECG beats using 11 selected features.
Tasa de eventos absoluta: 97.21% vs 95.197%
This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extract ion module, a classifier module, and an optimization module. In the feature ext raction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a mu lti-class support vector mach ine (SVM )-based classifier is proposed. For the optimizat ion module, a particle swarm optimization algorith m is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simu lation results show that the proposed algorith m has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.
Ali Khazaee (Mon,) conducted a other in Heart arrhythmia (n=30,873). Particle Swarm Optimization with Support Vector Machine (PSO-SVM) vs. Standard Support Vector Machine (SVM) was evaluated on Classification accuracy. The proposed PSO-SVM algorithm achieved a classification accuracy of 97.21% for ECG beats using 11 selected features.