Title :
Dynamic feature selection for detecting Parkinson´s disease through voice signal
Author :
Meilin Su;Keh-Shih Chuang
Author_Institution :
Department of Electrical and Computer Engineering Oriental Institute of Technology, New Taipei City, 22061 Taiwan, Republic of China
Abstract :
Parkinson´s disease (PD) is a disorder of the central nervous system and about 89% of the people with PD suffering from speech and voice disorders. In this paper, we adopted a dynamic feature selection based on fuzzy entropy measures for speech pattern classification of Parkinson´s diseases. To investigate the effect of feature selection, Linear Discriminant Analysis (LDA) was applied to distinguish voice samples between PD patients and health people. The data set of this research is composed of voice signals from 40 people, 20 with Parkinson´s disease and 20 health people. The results show that various voice samples need different feature selection. We applied dynamic feature selection can get higher rate of classification accuracy than all features selected.
Keywords :
"Feature extraction","Entropy","Parkinson´s disease","Biomedical measurement","Accuracy","Speech","Jitter"
Conference_Titel :
RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), 2015 IEEE MTT-S 2015 International Microwave Workshop Series on
DOI :
10.1109/IMWS-BIO.2015.7303822