Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

Abstract

As per the report of the World Health Organization (WHO), the mortalities due to cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is essential to have an efficient diagnosis of CVDs to enhance the healthcare in the clinical cardiovascular domain. The ECG signal analysis of a patient is a very popular tool to perform diagnosis of CVDs. However, due to the non-stationary nature of ECG signal and higher computational burden of the existing signal processing methods, the automated and efficient diagnosis remains a challenge.This paper presents a new feature extraction method using the sparse representation technique to efficiently represent the different ECG signals for efficient analysis. The sparse method decomposes an ECG signal into elementary waves using an overcomplete gabor dictionary. Four features such as time delay, frequency, width parameter, and square of expansion coefficient are extracted from each of the significant atoms of the dictionary. These features are concatenated and analyzed to determine the optimal length of discriminative feature vector representing each of the ECG signal. These extracted features representing the ECG signals are further classified using machine learning techniques such as least-square twin SVM, k-NN, PNN, and RBFNN. Further, the learning parameters of the classifiers are optimized using ABC and PSO techniques. The experiments are carried out for the proposed methods (i.e. feature extraction along with all classifiers) using benchmark MIT-BIH data and evaluated under category and personalized analysis schemes.Experimental results show that the proposed ECG signal representation using sparse decomposition technique with PSO optimized least-square twin SVM (best classifier model among k-NN, PNN and RBFNN) reported higher classification accuracy of 99.11% in category and 89.93% in personalized schemes respectively than the existing methods to the state-of-art diagnosis.