A Pattern Recognition Method for Stage Classification of Parkinson's Disease Utilizing Voice Features

被引：0

作者：

Caesarendra, Wahyu ^{[1
]}

Ariyanto, Mochammad ^{[1
]}

Setiawan, Joga D. ^{[1
]}

Arozi, Moh. ^{[1
]}

Chang, Cindy R. ^{[2
]}

机构：

[1] Diponegoro Univ, Dept Mech Engn, Fac Engn, Semarang 50275, Indonesia

[2] Univ Wollongong, Sch Nursing & Midwifery, Fac Sci, Wollongong, NSW 2500, Australia

来源：

2014 IEEE CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES) | 2014年

关键词：

DIAGNOSIS; SYSTEM; GAIT;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper presents a pattern recognition method for multi-class classification of Parkinson's disease based on PCA, LDA and SVM. 22 voice features which are extracted and reduced using PCA and LDA. SVM is then used during the classification step. The classification accuracy between single features and PCA and LDA features are presented and the results show that the PCA features have greater accuracy than LDA features and the single features.

引用

页码：87 / 92

页数：6

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