Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Technol Health Care. 2023;31(S1):551-563. doi: 10.3233/THC-236049.
The wrist pulse wave under the optimal pulse pressure plays an important role in detecting human body's physiological and pathological information. Wavelet threshold filtering is a common method for pulse wave de-noising. However, traditional filtering methods cannot smoothen the whole pulse wave well and highlight the details.
In view of this, an attempt is made in this paper to propose the pulse wave denoising algorithm for pulse wave under optimal pulse pressure according to the translation invariant wavelet transform (TIWT) and the new threshold function.
Firstly, by using hyperbolic tangent curve and combining the advantages of soft threshold function and hard threshold function, the new threshold function is derived. Secondly, based on the TIWT, pseudo-Gibbs phenomenon gets suppressed.
The experiments show that in comparison to the traditional wavelet filtering algorithm, the novel algorithm can better maintain the pulse wave geometric characteristics and has a higher signal to noise ratio (SNR).
The TIWT with improved new threshold compensates the shortcomings of the traditional wavelet threshold denoising methods in a better way. It lays a foundation for extracting time-domain characteristics of pulse wave.
最优脉压下的腕部脉搏波在检测人体生理病理信息方面发挥着重要作用。小波阈值滤波是脉搏波去噪的常用方法。但是，传统的滤波方法不能很好地平滑整个脉搏波并突出细节。
针对这一点，本文尝试根据平移不变小波变换（TIWT）和新的阈值函数，提出最优脉压下脉搏波的去噪算法。
首先，通过使用双曲正切曲线，并结合软阈值函数和硬阈值函数的优点，推导出新的阈值函数。其次，基于 TIWT，抑制了伪吉布斯现象。
实验表明，与传统的小波滤波算法相比，新算法可以更好地保持脉搏波的几何特征，具有更高的信噪比（SNR）。
改进后的新阈值 TIWT 更好地弥补了传统小波阈值去噪方法的不足。为提取脉搏波的时域特征奠定了基础。
