基于主成分分析的帕金森量表优化  

Optimization of Parkinson′s Scale Using Principal Component Analysis

作　　者：雷少正 王崇骏[1,2] 谢俊元[1,2] 

机构地区：[1]南京大学计算机科学与技术系,南京210023 [2]南京大学软件新技术国家重点实验室,南京210023

出　　处：《数据采集与处理》2015年第5期1020-1027,共8页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61105069)资助项目;江苏省科技支撑计划(BE2012161)资助项目

摘　　要：西医量表是评估帕金森病(Parkinson′s disease,PD)的重要依据,而这些量表包含大量交叉重复问题,不利于快速评估帕金森病。因此,优化这些西医量表对快速诊断帕金森病有非常重要的意义。针对该问题,提出了基于主成分分析(Principal component anaysis,PCA)的量表问题的优化算法。本文提出的算法先是基于主成分分析提取出加权投影向量,然后在投影向量的基础上采用基于大津阈值(Otsu)局部递归分割算法划分量表,最后基于贡献度因子(Contribution factor,CF)设计新量表。实验通过采用支持向量机(Support vector machine,SVM)识别帕金森病,发现用仅占原西医量表总问题数的21%的新量表能达到与原量表相当的识别水平。Western scales are a significant basis for assessment of Parkinson′s disease（PD）,while these scales contain a large number of cross-duplicates scales,which hampers rapid assessment of PD.Therefore,optimizing these wetern scales is significant for rapid diagnosis of PD.And the method of the optimization of Parkinsons scale based on principal component analysis（PCA）is raised.The weighted projective vector is extracted based on principal component analysis,and scale problems are divided on the basis of the projected vector using local recursive segmentation algorithm based on Ostu threshold,Finally,based on contribution factors（CF）,a new scale is designed.Experiment results confirm that the new combinations of scale which accounts for 21% of the original western scales is highly comparable to original western scales for identifying PD support vector machine（SVM）.

关 键 词：帕金森病 量表优化 主成分分析 大津阈值局部递归分割 加权投影向量 贡献度因子 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]