基于偏序拓扑图的帕金森病语音障碍分析方法

Dysphonic Analysis of Parkinson′s Disease Based on Partially Ordered Topological Graph

从形式概念分析角度,提出将偏序拓扑图用于帕金森病语音障碍分析与诊断。首先,在属性拓扑的基础上,结合偏序结构表示,构造偏序拓扑图的形式背景表示方法,并利用偏序拓扑图进行概念本体计算,获得原始形式背景的层次化概念树结构。进而结合决策属性,对概念树进行着色与约简,获得约简概念树。根据约简概念树的偏序关系,可获得分析对象的概念分类结构。将该方法应用于帕金森病语音特征数据集进行概念提取,实验表明不但可以在概念层面分析帕金森病与语音特征的关系,同时可以作为诊断依据进行数据诊断。将该方法应用于多个帕金森病数据集(样本数分别为197、5 875、1 040、220)进行分类精度对比测试,表明基于偏序拓扑图的帕金森病语音障碍分析在不同的帕金森病语音数据集下的平均诊断精度达到76.64%,高于LDA(67.36%)、QDA(70.83%)、kNN(71.83%)、parzen窗(70.24%)、SVM(74.61%)等经典分类器的诊断精度,高出经典分类器SVM 2.72%,表明该方法能有效应用于帕金森病语音障碍分析。

In this paper, we proposed a novel dysphonic analysis method on Parkinson′s disease based on partially ordered topological graph from the view of formal concept analysis. Firstly, we constructed a representation(method) named partially ordered topological graph(POT graph) from attribute topology and attribute partially ordered graph, which gained the ability of concept searching and hierarchical concept tree structure representation. Coloring and briefing the concept tree could obtain the brief concept tree. The concept classification structure of the analysis object could be obtained according to the partial order relation of the brief concept tree. Applying the method to concept searching in Parkinson′s disease dataset, results showed that the POT graph could not only analyze the relationship between Parkinson′s disease and speech feature in the view of formal concept, but also be used as a diagnostic basis for data analysis. Results obtained from several Parkinson′s disease datasets(the numbers of sample are 197, 5875, 1040 and 220)showed that the average precision was 76.64% by POT graph. Compared with the classical classifier such as LDA(67.36%), QDA(70.83%), kNN(71.83%), Parzen window(70.24%), and SVM(74.61%), our result was higher than SVM 2.72%. In conclusion, the proposed method could be beneficial to the dysphonic analysis of Parkinson′s disease.