Automatic identification of premature ventricular contraction using ECGs

Abstract

Premature ventricular contraction (PVC) is one of the most common arrhythmia diseases. The traditional diagnosis of PVC by visual inspection of PVC beats in electrocardiogram (ECG) is a time-consuming process. Hence, there has been an increasing interest in the study of automatic identification of PVC using ECGs in recent years. In this paper, a novel automatic PVC identification method is proposed. We first design a new approach to detect peak points of QRS complex. Then nine features are extracted from ECG according to the detected peak points, which are used to measure the morphological characteristics of PVC beats from different points of view. Finally, the key features are selected and fed into back propagation neural network (BPNN) to differentiate PVC ECGs from normal ECGs. Simulation results on the China Physiological Signal Challenge 2018 (CPSC2018) Database verify the feasibility and efficiency of the proposed method. The average accuracy attains 97.46%, as well as the average false detection rate and omission ratio are 3.41% and 1.37% respectively, which implies that the proposed method does a good job in identifying PVC automatically.