| 张洪波,庞月强,李小亭,王 博.膝关节声发射信号的统计分析与模式识别[J].电子测量与仪器学报,2021,35(8):198-204 |
| 膝关节声发射信号的统计分析与模式识别 |
| Statistical analysis and pattern recognition ofknee joint acoustic emission signals |
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| DOI： |
| 中文关键词: 膝关节 声发射 主成分分析 支持向量机 |
| 英文关键词:knee joint acoustic emission principal component analysis support vector machine |
| 基金项目: |
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| Author | Institution |
| Zhang Hongbo | 1.College of Quality and Technical Supervision, National & Local Joint Engineering Research Center of Metrology Instrument and
System, Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University |
| Pang Yueqiang | 1.College of Quality and Technical Supervision, National & Local Joint Engineering Research Center of Metrology Instrument and
System, Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University |
| Li Xiaoting | 1.College of Quality and Technical Supervision, National & Local Joint Engineering Research Center of Metrology Instrument and
System, Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University |
| Wang Bo | 1.College of Quality and Technical Supervision, National & Local Joint Engineering Research Center of Metrology Instrument and
System, Hebei Key Laboratory of Energy Metering and Safety Testing Technology, Hebei University |
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| 中文摘要: |
| 为了实现膝关节声发射信号的动态分析和模式识别,以膝关节在坐-立-坐运动的不同阶段产生的声发射信号为研究对
象,进行主成分分析、差异性检验和基于支持向量机的分类测试。 声发射特征参数经过线性变化提取为 2 个主成分;对膝关节
在两个运动阶段产生的声发射信号进行差异性检验,渐进显著性健康组主成分 F1<0. 05、主成分 F2 >0. 05,对照组均小于 0. 05;
支持向量机对膝关节声发射信号的分类准确率达到了 97. 9%。 结果表明,主成分分析的方法能够对膝关节声发射信号成功降
维,对降维后的信号进行差异性检验和诊断识别发现患病膝关节的不同运动阶段存在更为明显的差异,支持向量机的方法能够
对膝关节骨性关节炎做出准确的诊断识别。 |
| 英文摘要: |
| In order to realize dynamic analysis and pattern recognition of knee joint acoustic emission signals, principal component
analysis, difference test and classification test based on support vector machine were carried out to study the acoustic emission signals
generated by knee joints in different stages of sitting-standing-sitting. The characteristic parameters of the acoustic emission signals were
extracted into two principal components after linear changes; the difference test of the acoustic emission signals generated by the knee
joint in the two motion stages shows that the result of the progressive significance of the healthy group was the principal component F1<
0. 05, the principal component F2>0. 05, the progressive significance results of the control group was less than 0. 05; The classification
accuracy of the support vector machine for the acoustic emission signal of the knee joint reached 97. 9%. The results show that principal
component analysis can successfully reduce the dimension of knee acoustic emission signals; the acoustic emission signals at different
stages of movement are different, which is particularly obvious in the diseased knee joint; the support vector machine method can
accurately diagnose and identify. |
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