A QRS detection based on hilbert transform and wavelet bases

In this study, the Hillbert transform pairs of wavelet bases is used for QRS detection. From the properties of these mathematical tools, it was possible to develop an algorithm which is able to differentiate the R waves from the others (P, Q, S, T and U waves). The choice of a wavelet base was done after observing its frequency response in order to achieve a passband filter containing the characteristic frequencies of the QRS complex. The RR interval duration signal is obtained from the ECG, then it is calculated the cardiac rhythm and done the automatic discrimination between normal and pathological heart activity. The performance of the algorithm was verified using the records MIT-BIH arrhythmia and normal databases. A QRS detection rate of 99.92% was achieved against MIT-BIH arrhythmia database. The noise tolerance of the proposed method was also tested using standard records from the MIT-BIH noise stress test database. The detection rate of the detector remains about 99.35% even for signal-to-noise ratios (SNR) as low as 6 dB