Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine

• ECG signal used to investigate the heart function for the different cardiac conditions.
• Empirical mode decomposition and singular value decomposition have been used for feature extraction/selection.
• Directed Acyclic Graph (DAG) approach of binary SVM has been proposed for ECG classification.
• Fivefold cross-validation and particle swarm optimization has been used for optimal selection of SVM model parameters.
• The proposed approach outperforms few conventional ECG classification approaches.

In this paper, a classifier motivated from statistical learning theory, i.e., support vector machine, with a new approach based on multiclass directed acyclic graph has been proposed for classification of four types of electrocardiogram signals. The motivation for selecting Directed Acyclic Graph Support Vector Machine (DAGSVM) is to have more accurate classifier with less computational cost. Empirical mode decomposition and subsequently singular value decomposition have been used for computing the feature vector matrix. Further, fivefold cross-validation and particle swarm optimization have been used for optimal selection of SVM model parameters to improve the performance of DAGSVM. A comparison has been made between proposed algorithm and other two classifiers, i.e., K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The DAGSVM has yielded an average accuracy of 98.96% against 95.83% and 96.66% for the KNN and the ANN, respectively. The results obtained clearly confirm the superiority of the DAGSVM approach over other classifiers.