Research On Preprocessing Algorithms Of Physiological Signal

Wavelet transform has been applied and developed in many fields, especially in signal processing area. As the signal can be analyzed and processed within both time and frequency domain of wavelet transform, in addition, it can remove the noise while maintain the singularity effectively, the wavelet transform is an appropriate tool for signal processing.Cardiovascular diseases have become the first killer to human health because of the high mortality rate and disability rate. Physiological signal as the most important signal in the detection of cardiovascular diseases contains a lot of information and is of great clinical value for early diagnosis of cardiovascular diseases. Preprocessing physiological signals is the precondition of extracting feature physiological information from the signals, and is of great significance.Physiological signal is a weak signal coupled with strong noises. The best method for Physiological signal preprocessing is a focus for researchers. Most of the traditional preprocessing algorithms are designed for one or two noises, they have long processing time and bad real-time performance, otherwise, every step of procedure may bring the signal deformation; the algorithm in this paper can deal with variety of noises with the same effect of pre-processing algorithms and has broad application prospects. In addition, after the preprocessing of ECQ one important work is the accurate extraction of the feature points of ECQ such as the R wave crest which also is the research of this paper.This paper elaborates the waveform characteristics of ECG and PCQ goes into algorithms at home and abroad about physiological signal preprocessing and analyzes the traditional EMD algorithm in detail. We proposed a physiological signal preprocessing algorithm based on wavelet transform. Preprocessing the acquired physiological signal with our algorithm, we propose an algorithm of R wave extraction by self-adaptive wavelet transform for the subsequent analysis. The proposed algorithm is developed with LabVIEW and Matlab programming environment. By comparing the pretreatment results got by traditional EMD algorithm with those got by our algorithm on clinical trial data, we find that our proposed algorithm is superior to the traditional EMD algorithm.