摘要
针对采集到的脉搏波信号中存在基线漂移和高频噪声等干扰,导致后续病理研究分析困难、测量精度相对较差的问题,提出了一种改进的变分模态分解和非局部均值降噪结合的滤波算法。针对变分模态参数选取不同对结果存在不同影响的问题,采取鲸鱼优化算法自适应选取合适的参数,并根据排列熵结果筛选模态分量,对噪声分量进行非局部均值滤波,最后将信号重构,实现对脉搏波信号的噪声去除。实验结果表明:含噪信号经过改善后的滤波算法处理后,其信噪比与均方根误差均优于其他降噪方法,证明该算法能够有效地滤除信号的噪声,有助于脉搏波的分析处理。
To solve the problem that the collected pulse wave signal is easily disturbed by high frequency noise and baseline drift, which make subsequent pathological research and analysis difficult and the measurement accuracy of inferior quality, an improved filtering algorithm combining variational mode decomposition and non-local mean noise reduction was proposed. Aiming at the problem that the selection of variational modal parameters has different effects on the results, the whale optimization algorithm was used to adaptively select appropriate parameters, and the modal components were screened according to the permutation entropy results. The experimental results show that the signal-tonoise ratio and root mean square error of the improved signal are better than other noise reduction methods after being processed by the filtering algorithm.
作者
杨海马
陈嘉慈
徐笑寒
李福凤
宋智超
金焱
YANG Haima;CHEN Jiaci;XU Xiaohan;LI Fufeng;SONG Zhichao;JIN Yan(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;College of Basic Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China)
出处
《上海理工大学学报》
CAS
CSCD
北大核心
2022年第6期553-561,共9页
Journal of University of Shanghai For Science and Technology
基金
国家自然科学基金资助项目(U1831133)
国家科技部十三五重点研发计划(2019YFC1711701)
中国科学院空间主动光电技术重点实验室基金资助项目(20212DKF4)。
关键词
脉搏信号
鲸鱼优化算法
变分模态分解
排列熵
非局部均值滤波
pulse signal
whale optimization algorithm
variational mode decomposition
permutation entropy
non-local mean filtering
