A robust QRS detection approach using stationary wavelet transform

Abstract

Accurate QRS detection is crucial for reliable ECG signal analysis and the development of automatic diagnosis tools. In this paper, we propose a simple yet efficient new algorithm for QRS detection using the Stationary Wavelet Transform (SWT). The wavelet transform has been extensively exploited for QRS detection and proved to be an efficient mathematical tool for scale analysis; it provides good frequency components estimation for the input signal and has good localization capability. The proposed procedure exploits solely the first level approximation coefficients of the wavelet transform applied to the bandpass-filtered ECG signal. Therefore, it resulted in a reduced complexity algorithm compared to the existing methods which use many decomposition levels. Thresholding has been implemented using the Pan-Tompkins procedure which is known to be very powerful. Our approach has been assessed over the MIT/BIH benchmark database, the MIT noise stress test database for noise robustness evaluation and the European ST-T database. The obtained results show competitive performance with state-of-the-art algorithms. The proposed scheme achieved a sensitivity of 99.83%, a positive predictivity of 99.94% and a detection error rate of 0.228% using Lead I MIT-BIH Database, this performance is one of the best results over this benchmark, and 99.35% of sensitivity, 99.76% of positive predictivity and detection error rate of 0.9% using the European ST-T Database, hence, our algorithm achieved high performance on Holter environment. Using the MIT noise stress test database, our algorithm achieved 98.77% of sensitivity, 91.01% of positive predictivity, and 10.12% of DER. Thus, our algorithm is robust and outperforms state-of-the-art algorithms on noisy recordings.