摘要
为了更有效地对心音按成分进行分割,实验采用一种基于Teager-Kaise能量算子(Teager-Kaise Energy Operator,TKEO)以及多包络特征融合的心音分割算法。首先,利用多尺度小波软阈值对PCG信号进行去噪,然后进行TKEO运算,由于TKEO对瞬时能量变化极其敏感,可以有效提取包络峰值,得到TKEO信号。其次,对TKEO信号提取归一化香农能量包络和维奥拉积分包络,计算出两者包络与TKEO信号之间的皮尔逊相关系数,根据相关关系进行融合。然后,用区间搜索法对包络进行峰值搜索,并且对搜索结果的方差进行比较。最后,根据S1和S2的最大持续时间消除伪峰。用PhysioNet-2016数据集对所提算法进行测试,实验结果显示平均精确度为0.922,证实了该算法能较有效地对心音信号进行分割,为临床环境下采集的心音信号的特征提取与分析提供了新方法。
In order to segment heart sounds by component more effectively,a kind of heart sound segmentation algorithm based on Teager-Kaise energy operator(TKEO)and multi-envelope feature fusion is proposed in experiment.Firstly,the PCG signal is denoised by using the multi-scale wavelet soft threshold.Then TKEO operation is carried out.Since TKEO is extremely sensitive to the instantaneous energy change,the envelope peak can be extracted effectively and the TKEO signal can be obtained.Secondly,the normalized Shannon energy envelope and Viola integral envelope are extracted from the TKEO signal.The Pearson correlation coefficient between each envelope and TKEO signal is calculated.And then the fusion envelope is carried out according to the correlation.Next,the interval search method is used to search the peak envelopes.The variance of the search results is compared.Finally,false peaks are eliminated according to the maximum duration of S1and S2.The proposed algorithm is tested using PhysioNet2016data set.Experimental results show that an average accuracy of 0.922is achieved by using this method.It is proved that this algorithm can be used to segment the heart sound signals effectively.It provides a new method for feature extraction and analysis of heart sound signals collected in clinical environment.
作者
张欣
孙静
杨宏波
潘家华
郭涛
王威廉
ZHANG Xin;SUN Jing;YANG Hong-bo;PAN Jia-hua;GUO Tao;WANG Wei-lian(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;Yunnan Fuwai Cardiovascular Disease Hospital,Kunming 650102,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期461-466,共6页
Computer Science
基金
国家自然科学基金(81960067)
2018云南省重大科技专项(2018ZF017)
云南省基础研究计划(昆医联合专项)(2018FE001)(-105)
