A pattern recognition method for stage classification of Parkinson's disease utilizing voice features

Authors

Wahyu Caesarendra, University of WollongongFollow
Mochammad Ariyanto, Diponegoro University
Joga D. Setiawan, Diponegoro University
Moh Arozi, Diponegoro University
Cindy R. Chang, University of WollongongFollow

RIS ID

99734

Publication Details

Caesarendra, W., Ariyanto, M., Setiawan, J. D., Arozi, M. & Chang, C. R. (2014). A pattern recognition method for stage classification of Parkinson's disease utilizing voice features. Conference Proceeding: 2014 IEEE Conference on Biomedical Engineering and Sciences (pp. 87-92). United States: IEEE.

Abstract

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.