A Multidimensional Feature Extraction and Selection Method for ECG Arrhythmias Classification

Abstract

This article presents a novel multidimensional feature extraction and selection method for electrocardiogram (ECG) arrhythmias classification. First, a novel multi-interval symmetrized dot pattern method (MSDP) is proposed to extract image shape features, which are combined with time-frequency features, morphological features and RR interval features to form a new feature set. Then a novel average weight based ReliefF method (WReliefF) is proposed to integrate with genetic algorithm (GA) and support vector machine (SVM) and form a feature selection method named as WReliefF-GA-SVM. The WReliefF-GA-SVM is a hybrid type feature selection method, and it can be divided into Filter stage and Wrapper stage to select features. In Filter stage, the algorithm uses WReliefF to sort the features according to the average weight. In Wrapper stage, GA and SVM are combined, and the classification performance of SVM is used as the fitness function to search the best feature subsets. Finally, the five types of ECG beats from the MIT-BIH arrhythmia database are classified by a one against all (OAA) SVM model. The results show that the proposed method achieves average sensitivity, specificity and accuracy of 99.42%, 99.83%, and 99.74%, respectively.