Electrocardiogram QRS Detection Using Multiscale Filtering Based on Mathematical Morphology

Abstract

A novel QRS detection algorithm based on multi-scale mathematical morphology (3M) and multi-frame differential modulus cumulation is proposed in this paper. The algorithm introduces the multi-stage filtering based on mathematical morphology from image processing field into ECG analysis to suppress the impulsive noise, and adopts multi-frame differential modulus cumulation to remove the baseline drift and enhance the signal. An average QRS detection rate of 99.67%, a sensitivity of 99.86% and a positive prediction of 99.80% have been achieved against the MIT/BIH Arrhythmia Database. Comparison with existing methods reveals that the proposed algorithm achieves better detection accuracy.