基于最小二乘支持向量机的心音分类识别研究  

Heart Sound Recognition Based on Least Squares Support Vector Machines

作　　者：许莉莉[1] 师炜[1] 郭学谦[1] 曲典[1] XU Li-li;SHI Wei;GUO Xue-qian;QU Dian(School of Biomedical Engineering,Capital Medical University, Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application,Capital Medical University, Beijing 100069, China)

机构地区：[1]首都医科大学生物医学工程学院临床生物力学应用基础研究北京市重点实验室,北京100069

出　　处：《中国医疗设备》2017年第4期38-41,共4页China Medical Devices

基　　金：首都医科大学基础-临床一般课题(15JL17)

摘　　要：目的将最小二乘支持向量机引入心音的分类识别,优化其参数设置,获得最优的分类结果。方法本文通过医院采集和网络下载获得99例心音信号,每个信号提取两个长度为5 s的样本,共198个样本,均分为3个集合。对每个样本采用sym6小波基进行小波包3层分解,根据Parseval定理计算每个样本的能量谱特征。以训练集数据送入支持向量机和最小二乘支持向量机进行机器学习,采用不同步长相结合的搜索法,根据测试集1的分类结果对向量机的参数进行优化。结果以高斯径向基函数为核的支持向量机,其惩罚因子C和核函数宽度σ均为20.086时,对测试集1的分类正确率最高,为79.7%;对测试集2的分类正确率为84.5%,分类计算使用的时间分别为0.108 s和0.117 s。对最小二乘支持向量机,高斯径向基函数宽度平方σ2取1,正则化参数γ取20.086时,对测试集1的分类正确率最高,为94.2%;对测试集2的分类正确率为89.6%,分类计算使用的时间分别为0.0638 s和0.0692 s。结论采用求解线性方程法寻找局部最优解的最小二乘支持向量机运算速度快,更适合心音样本的分类识别。Objective To introduce the least square support vector machine (LS-SVM) into the recognition of heart sound, as well as optimizing its parameters setting to obtain the optimal classification results.Methods 99 heart sounds were obtained from our hospital and the internet. Two samples of 5 s were extracted from each heart sound to construct one training set and two test sets. 3-layer wavelet packets decomposition of sym6 was applied to each sample to extract feature. Then, the training set was used to machine learning of SVM and LS-SVM. One test set was used to parameters optimization, the other was for test of optimized SVM and LS-SVM. Results The C and σ of the SVM that examined by Gaussian radial basis function were both 20.086. The accuracy for first test set was the highest (79.7%). For second test set,the accuracy was 84.5%, and the running times were 0.108 s and 0.117 s, respectively. For the LS-SVM, the accuracy for first test set was the highest (94.2%) while σ2=1 and γ=20.086. For second test set, the accuracy was 89.6%, and the running times were 0.0638 s and 0.0692 s, respectively. Conclusion The LS-SVM that find local optimal solution based on the linear equation method can operate faster, and it is more suitable for recognition of heart sound samples.

关 键 词：心音 小波包分解 支持向量机 最小二乘支持向量机 参数优化 

分 类 号：R318[医药卫生—生物医学工程] TP181[医药卫生—基础医学]