A New Automatic Detection Method for Bundle Branch Block Using ECGs

Abstract

The automatic detection of bundle branch block (BBB) using electrocardiogram (ECG) has been attracting more and more attention, which is recognized to be helpful in the diagnosis and treatment of BBB-related heart diseases. In this paper, a novel automatic BBB detection method is developed. We first propose a new R peak detection algorithm which is able to detect both single R peak and multiple R peaks in one ECG beat. Then the number of R peaks and the length of RR interval are calculated to be the extracted features. Finally, linear classification is implemented to differentiate BBB ECG from normal ECG. Simulation results on CPSC2018 Database show that the average accuracy, sensitivity and specificity attain 96.45%, 95.81% and 96.80% respectively, demonstrating that the presented method of automatic BBB detection works well in distinguishing the normal ECG signals and the BBB ECG signals. This research thus provides insights for the automatic detection of BBB.