Research And Implementation Of Adaptive Denoising In Arterial Pulse Wave Based On Lifting Scheme Discrete Wavelet

Pulse wave is one of the important physiological signals that contain essential information about human body. The waveform of pulse and its change can be a useful tool that reveals one’s situation in cardiovascular system. Implementing scientific methods, series of physiological parameters like heart rate, blood density and arterial flexibility can be gained through the pulse wave. These parameters can be regarded as reference in clinical analysis.However, in the process of collecting pulse wave signals, many kinds of noise are also added, which leads to aberrance in signal shape and other changes. These deflections may influence the final analyzing results badly. So how to separate the clean pulse wave signal and the noise perfectly is a challenging problem that this paper mainly focuses on.The wavelet analysis can process signals both in time and frequency domain, this merit leads to various applications in engineering. In terms of the newly proposed Lifting Scheme Wavelets’advantages in running speed, memory saving and integer transform et al, compared to the traditional wavelets, this paper designed two methods based on lifting scheme wavelet to get the pulse wave separated from noisy signals, and tested these methods through practical clinical data.The first method uses soft threshold and selects the wavelet base adaptively. Compared to the one which uses fixed wavelet base, this method considers the partial characters in pulse wave and processes different parts using different base functions. The second method focuses mainly on the structure of the lifting scheme-split, predict and update. In order to avoid the computing of polyphase matrix to speed up the algorithm, this means uses LMS method to make the predicting operators selected adaptively. This method can improve the intellectual processing and can adapt different pulse wave signals automatically.Results of these two denoising methods show that they both can eliminate the unwanted noise and separate the clean signals to some extent. These methods can be used in practical medical apparatus, especially the clinical monitoring system, to denoise the signals before its further use.