The use of the Hilbert transform in ECG signal analysis.

This paper presents a new robust algorithm for QRS detection using the first differential of the ECG signal and its Hilbert transformed data to locate the R wave peaks in the ECG waveform. Using this method, the differentiation of R waves from large, peaked T and P waves is achieved with a high degree of accuracy. In addition, problems with baseline drift, motion artifacts and muscular noise are minimised. The performance of the algorithm was tested using standard ECG waveform records from the MIT-BITH Arrhythmia database. An average detection rate of 99.87%, a sensitivity (Se) of 99.94% and a positive prediction (+P) of 99.93% have been achieved against study records from the MIT-BITH Arrhythmia database. A detection error rate of less than 0.8% was achieved in every study case. The reliability of the proposed detector compares very favorably with published results for other QRS detectors.