中国医疗设备2017，Vol.32Issue(4)：38-41,4.DOI:10.3969/j.issn.1674-1633.2017.04.011
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
摘要
Abstract
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 andγ=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Key words
heart sound/wavelet packets decomposition/support vector machine/least squares support vector machine/parameter optimization分类
医药卫生引用本文复制引用
许莉莉,师炜,郭学谦,曲典..基于最小二乘支持向量机的心音分类识别研究[J].中国医疗设备,2017,32(4):38-41,4.基金项目
首都医科大学基础-临床一般课题(15JL17). （15JL17）
