QRS wave group detection based on B-Spline wavelet and adaptive threshold

An effective algorithm for detecting QRS wave group was presented. The ECG signal is de-composed with the equivalent filter of a biorthogonal spline wavelet by Mallat pyramid decomposition. The signal singularity´s Lipschitz exponent was used to analyze the relationship between the signal singularity (peak R) and the zero-crossing point of the modulus maximum pair of its wavelet transform,the Biorthogonal spline wavelet can detect Singular point well, Aiming at the defects of different approaches, we choose 2-order B-Spline wavelet as mother wavelet which filter has a small quantity of coefficient and combines the self-adaptation threshold method to improve the detection rate. the results by using the MIT-BIH Arrhythmia database improves this approach could detect the ECG signals with high noise and base-line drift, the detection rate reach more than 99.79%. The detection speed is better than many other detection approaches and has good real time effect.