Variational Mode Decomposition Features for Heartbeat Classification

In software applications to analyse Heart Rate Variability (HRV) or to detect heart rhythm disorders, automatic heartbeat classification is a first step to expose abnormalities in the electrical activity of the heart. We propose a new morphological description of heartbeats based on Variational Mode Decomposition (VMD) to classify them as normal, supraventricular or ventricular. The proposed approach combines the features extracted from the different modes with time features, and it is designed for single-lead applications. The features are fed to an LS-SVM classifier, using an RBF kernel, 10-fold cross-validation and 50% of balanced data as training. In this study, two different approaches were tested: one considering an intra-patient approach inspired by a semi-supervised application, in which the same patients form the training and the test set; and a second inter-patient approach, in which the training and the testing signals belong to different patients. The method reports an average accuracy of 92.17% and sensitivities of 92.84%, 72.56% and 91.25% for normal, supraventricular and ventricular beats respectively, which is in line with the state of the art.