IMF复杂度特征在心音信号分类识别中的应用

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。

Study on classification and recognition of heart sound using IMF complexity feature

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.