ECG feature extraction in Matlab

Abstract
It is a difficult process to detect abnormal heart beats, known as arrhythmia, in long-term ECG recording. Thus, computer-aided diagnosis systems have become a supportive tool for helping physicians improve the diagnostic accuracy of heartbeat detection. This paper explores the bandwidth properties of the modes obtained using variational mode decomposition (VMD) to classify arrhythmia electrocardiogram (ECG) beats. VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm for analyzing non-linear and non-stationary signals. It decomposes the signal into a set of band-limited oscillations called modes. ECG signals from the MITBIH arrhythmia database are decomposed using VMD, and the amplitude modulation bandwidth BAM, the frequency modulation bandwidth BFM and the total bandwidth B of the modes are used as feature vectors to detect heartbeats such as normal (N), premature ventricular contraction (V), left bundle branch block (L), right bundle branch block (R), paced beat (P) and atrial premature beat (A). Bandwidth estimations based on the instantaneous frequency (IF) and amplitude (IA) spectra of the modes indicate that the proposed VMDbased features have sufficient class discrimination capability regarding ECG beats.