Pulse wave signal denoising method based on DTCWT-Spline

Abstract

The invention provides a pulse wave signal denoising method based on DTCWT-Spline. The method includes the steps that firstly, original noise-containing pulse wave signals are subjected to double-tree complex wavelet decomposition, wavelet coefficients on all layers are denoised through a Bayes maximum posteriori estimation threshold value, then double-tree complex wavelet inverse transformation is carried out, and pulse wave signals obtained after high-frequency noise is filtered out are obtained; the obtained pulse wave signals obtained after the high-frequency noise is filtered out are detected through a sliding window method, wave trough points in the pulse wave signals obtained after the high-frequency noise is removed are recognized, a wave trough curve is fit through a cubic spline interpolation method to serve as the estimated baseline drifting distance, finally subtracting the estimated baseline drifting distance from the pulse wave signals obtained after the high-frequency noise is removed is carried out, and the pulse wave signals are denoised. The high-frequency noise and baseline drifting can be effectively removed, overall characteristic information of the original pulse wave signals is well kept, the method is simple, low in calculated quantity and low in occupied memory, and a technical foundation is provided for research and development of miniaturized and mobile noninvasive continuous blood pressure detection equipment based on pulse waves.

Description

technical field [0001] The invention relates to the technical field of biomedical signal acquisition and processing, in particular to a DTCWT-Spline-based pulse wave signal denoising processing method. Background technique [0002] The pulse wave is the fluctuation caused by the contraction and diastole of the heart and the ejection of blood and the propagation of blood flow to the periphery. The state of its propagation depends on the function of the heart itself, the elasticity of the blood vessel, the size of the lumen, and the resistance of the inner wall of the blood vessel. The pulse wave signal contains a large amount of physiological and pathological information. Accurately identifying and extracting the characteristic parameters of the pulse wave signal provides a reference and accurate guidance for analyzing the physiological and pathological conditions of the human body and preventing and diagnosing cardiovascular diseases. [0003] In the process of body surface ...

AI Technical Summary

Problems solved by technology

However, when wavelet transform or dual-tree complex wavelet transform is used to eliminate low-frequency noise such as baseline drift, it is necessary to decompose the wavelet to a high scale, and set the approximate wavelet coefficients on the highest scale to zero to filter out baseline drift, and the high-scale decomposition increases The amount of computation for decomposition and reconstruction, while filtering out baseline drift, will also lose some useful information in low-frequency bands

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