摘要
为提高非线性、非平稳心音信号特征提取的准确性和分类识别的高效性,提出一种基于固有模态函数(Intrinsic Mode Function,IMF)复杂度和二叉树支持向量机(Binary Tree Support Vector Machine,BT-SVM)的心音分类识别方法。对心音进行经验模式分解(Empirical Mode Decomposition,EMD),得到若干反映心音本体特征的平稳IMF分量;利用互相关系数准则对其筛选,计算所选IMF分量的复杂度值为信号的特征;将其组成特征向量输入到BT-SVM进行分类识别。临床数据仿真结果表明,该方法能有效提取心音特征,与传统识别方法相比,具有训练时间短,识别率高等优点。
To improve the precision of extracting feature and efficiency of classification and recognition from the non-stationary and non-linear heart sounds, a new method based on complexity feature of Intrinsic Mode Function (IMF) and Binary Tree Sup- port Vector Machine (BT-SVM) is proposed. Original heart sound is decomposed into a finite number of stationary IMFs with EMD; the complexity of IMF component is calculated using mutual correlation coefficient between several criteria which can be quantitatively evaluated as the feature of heart sound; the eigenvectors are input into BT-SVM classifier for recognition. Experi- mental results show that the method not only can effectively extract heart sound feature, but also has shorter training time and high recognition rate compared with traditional recognition network.
出处
《计算机工程与应用》
CSCD
2013年第21期212-215,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.30770551)
重庆市新型医疗器械重大科技专项(CSTC.2008AC5103)
关键词
经验模式分解
心音
复杂度
支持向量机
Empirical Mode Decomposition(EMD)
heart sound
complexity
Support Vector Machine (SVM)