Premature Ventricular Contraction Detection from Ambulatory ECG Using Recurrent Neural Networks

Abstract

Premature ventricular contraction (PVC) is usually considered to as benign arrhythmia in the absence of structural heart diseases. However, frequent premature beats may significantly increase the risk of heart failure and even death by an arrhythmia-induced cardiomyopathy. Therefore, high PVC counts have been considered as an approach to predict the risk of severe arrhythmias. Progress of wearable devices provides a convenient tool for the detection of premature contraction in casual life. Considering the huge quantities of data recorded by wearable devices, reliable and low-cost data analysis programs should be developed for real time PVC detection. In this research, we use recurrent neural networks with, long short-term memory to detect PVC. Through validating with MIT-BIH arrhythmia database, the detection accuracy of this method is 96%-99%.