Research on Heartbeat Classification Algorithm Based on CART Decision Tree

Abstract

Premature ventricular contraction (PVC) is a widespread condition of arrhythmia that can be life-threatening at any time. Fast and accurate use of computers to diagnose PVC is critical for both doctors and patients. In this paper, we propose a new method for PVC detection based on abnormal eigenvalues and decision tree. We choose composite areas, amplitudes and intervals as feature parameters to identify heartbeat types. The method was tested in the published MITBIH arrhythmia database with accuracy, sensitivity and specificity of 99.6%, 97.3% and 99.5%, respectively. The effectiveness of the proposed method is proved by comparison with other methods.