QRS detection in computer-based ECG signal analysis using the Hilbert transform

Abstract

A new robust algorithm for locating the R wave peaks in computer-based ECG analysis using the properties of the Hilbert transform is presented in this paper. The method developed for QRS complex detection allows the differentiation of R waves from large, peaked T and P waves with a high degree of accuracy and minimizes the problems associated with baseline drifts, motion artifacts and muscular noise. The performance of the algorithm was tested using standard noise free and noise contaminated ECG waveform records from the MIT-BIH Arrhythmia Database. A detection error rate of less than 0.5 % was achieved in every studied case. The reliability of the proposed detector is also compared with published results for other QRS detectors. The noise tolerance of the new proposed QRS detector was also tested using standard records from the MIT-BIH Noise Stress Test Database. The sensitivity of the detector remains about 90% even for SNR's as low as 6dB.