QRS detection algorithm based on the quadratic filter

Abstract

QRS detection in the electrocardiogram signal is very crucial as a preliminary step for obtaining QRS complex, beat segmentation, and beat-to-beat intervals. Two main problems in QRS detection are a variety of noise types and various types of abnormal morphologies. We propose a QRS detection algorithm consisting of the quadratic filter for enhancing QRS signal to noise ratio. Results show that significant improvement in QRS signal to noise ratio can be obtained from challenging situations including low amplitude QRS complexes corrupted by baseline drift and abnormal morphologies such as an aberrated atrial premature beat, a premature ventricular contraction beat, a fusion of ventricular and normal beat, and a fusion of paced and normal beat. The enhancements in QRS signal to noise ratio allow us to use a single fixed threshold without any additional post-processing techniques in beat detection step. The performance of proposed algorithm was evaluated with the electrocardiogram data from MIT-BIH arrhythmia database. Results show that the quadratic filter is capable of enhancing QRS signal to noise very well leading to the average detection error rate of 0.38% from 48 records.