摘要
利用心阻抗微分信号的特征点可计算出多个血流动力学参数,进而判别心功能状态,因此特征点的准确提取显得尤为重要。本文应用实验室自行设计开发的KF_ICG型无创心功能检测仪采集了健康人和重庆市大坪医院22例患者数据,应用小波阈值法对采集的数据进行降噪处理,对降噪后的信号采用bior3.7小波进行6层分解后定位特征点。结果表明,该法无论对健康人还是存在诸多噪声干扰的临床患者数据都能有效实现特征点的精确定位,有助于实现阻抗法无创检测血流动力学参数的临床应用。
Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram.According to these parameters,doctors can determine heart conditions,so it is very important to accurately detect the feature point of impedance differential signals.This article presents a process in which we used wavelet threshold method to de-noise signals,and then detected the feature points after six layers wavelet decomposition by using bior3.7.The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument.The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients.This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2015年第2期284-289,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(81371713)
关键词
心阻抗
小波变换
阈值去噪
特征点检测
impedance cardiogram
wavelet transform
threshold de-noising method
feature point detection
