A Method Of ECG Signal Processing Based On The Lifting Wavelet Transform

At present,the application of wavelet analysis is a wide use in nature science,application science and economic fields,the lifting scheme is a research hotspot with its flexibility and other advantage,and researching the basic theory and exploring the application aspect is one of the important problems in mathematic and information science fields.This paper analyses some important theory of traditional wavelet such as multi-resolution,the Mallat algorithm and biorthogonal filter sets. Then the lifting scheme using to construct the second generation wavelet is inducted.We compare the first generation wavelet based on the traditional filters with the second generation wavelet based on the lifting scheme,give the procedure of realizing the Mallat with lifting scheme and the method of constructing the new wavelet by adaptively choosing the lifting coefficient, and analyze the advantage of lifting scheme in arithmetic complexity and constructing.ECG signal contains abundant information about human heart,it is the foundation of observing the function and status of heart,diagnosing cardiovascular disease,evaluating the validity of various of treatment scheme,so the ECG detection and analysis is important for clinical diagnosis.With development of the information technology, using computer to achieve automatic and accurate analysis for ECG has been a subject fervently pursued by both internal and external researchers.So,on the basis of lifting scheme theory,the following two aspects have been further studied:First,using lifting scheme to the ECG denoising, and find that the lifting scheme denoising can keep the characteristic of signal and restrain noise and raise SNR.The denoising effect is excellent than traditional method.Secondly,using lifting scheme to QRS complexes,a biorthogonal splinewavelet filter banks is designed and its lifting scheme is used to detect the R-peak, then the onset and offset of QRS complexes are confirmed farther. This method reduces the complexity, speeds up the programme's operation and achieves high detection rate of R-peaks. It is proved well by the American MIT database.