ECG signal analysis based on variational mode decomposition and bandwidth property

Abstract

In this paper, the bandwidth properties of the modes obtained using the variational mode decomposition (VMD) are analyzed to detect arrhythmia electrocardiogram (ECG) beats. The VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm to analyze non-linear and non-stationary signals. It decomposes the signal into a set of band-limited amplitude and frequency modulated oscillations called modes. ECG signals from MIT-BIH arrhythmia database are decomposed using the VMD, and the amplitude modulation bandwidth B-AM, frequency modulation bandwidth B-FM and total bandwidth B of the modes are deployed as feature vector. Heart beats such as normal (N), premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat(PB) and atrial premature beat (APB) are classified using these features. Class discrimination capability of the VMD based features are indicated giving different instantaneous frequency (IF) and amplitude (IA) spectra. Finally, single classifiers such as k-nearest neighbor, artificial neural network and decision tree with their ensemble methods are used to evaluate the performance of the proposed method.