基于CEEMDAN的ICG信号预处理与特征识别方法研究

Research on ICG Signal Preprocessing and Feature Recognition Method Based on CEEMDAN

提出一种基于自适应噪声完备集合经验模态分解(CEEMDAN)结合差分、阈值迭代处理和信号分段对心阻抗血流图(ICG)信号进行预处理和多个特征点的识别方法。首先对ICG信号做CEEMDAN分解,获取多个模态函数分量(IMF),根据ICG存在的高频与低频噪声频率,利用相关系数法去除干扰噪声,再对去除噪声后的ICG信号进行差分与分段,寻找特征点B、C、X,并对通过临床采集的20名志愿者的信号进行以上处理,对该算法准确性进行评估。最终结果表明该方法对特征点的定位准确率达到95.8%,特征定位效果较好。

A method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)combined with differential,threshold iterative processing and signal segmentation is proposed to preprocess the impedance cardiogram(ICG)signal and identify multiple feature points.First,CEEMDAN decomposes the ICG signal to obtain multiple modal function components(IMF).According to the high-frequency and low-frequency noise frequencies existing in the ICG,the correlation coefficient method is used to remove the interference noise,and then the ICG signal after noise removal is differentiated and segmented.Looking for feature points B,C,X,and perform the above processing on the signals of 20 volunteers collected clinically to evaluate the accuracy of the algorithm.Thefinal results show that the method can locate the feature points with an accuracy rate of 95.8%,the feature positioning effect is good.