Heart Sound Recognition Based on Least Squares Support Vector Machines
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
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 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.
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.
XU Li-li, SHI Wei,
GUO Xue-qian, QU Dian. Heart Sound Recognition Based on Least Squares Support Vector Machines. China Medical Devices, 2017, 32(4): 38-41.