Novel QRS Detection by CWT for ECG Sensor

Abstract

Existing wavelet transform methods usually realize the QRS detection by sourcing for two modulus maxima with opposite sign and locating the zero crossing point between them at high decomposition scale. However high scale wavelet transform is often contaminated with severe baseline drift. In addition, common sense indicates that detecting zero crossing is not an easy task compared to the detection of maximum point. In this paper, a novel algorithm based on continuous wavelet transform (CWT) is proposed to accurately detect QRS. It employs a first-order derivative-based differentiator to suppress noise and baseline drift and uses high scale continuous wavelet transform to peak the zero crossing R point produced by differentiator to ease the task of QRS detection. It is shown by simulation that the proposed algorithm outperforms many existing methods and achieves an average detection rate of 99.69%, a sensitivity of 99.87%, and a positive prediction of 99.82% against the lead II of study records from the MIT-BIH Arrhythmia database.