Open AccessFactor Analysis of Speech Signal for Parkinson’s Disease Prediction using Support Vector Machine
Author(s) -
Biswajit Karan,
Sitanshu Sekhar Sahu
Publication year - 2022
Publication title -
international journal of electrical and electronics engineering
Language(s) - English
Resource type - Journals
ISSN - 2231-5284
DOI - 10.47893/ijeee.2022.1181
Subject(s) - support vector machine , pattern recognition (psychology) , speech recognition , signal (programming language) , computer science , artificial intelligence , linear discriminant analysis , jitter , identification (biology) , set (abstract data type) , telecommunications , botany , biology , programming language
Speech signal can be used as marker for identification of Parkinson’s disease. It is neurological disorder which is progressive in nature mainly effect the people in old age. Identification of relevant discriminant features from speech signal has been a challenge in this area. In this paper, factor analysis method is used to select distinguishing features from a set of features. These selected features are more effective for detection of the PD. From an empirical study on existing dataset and a generated dataset, it was found that the jitter, shimmer variants and noise to harmonic ratio are dominant features in detecting PD. Further, these features are employed in support vector machine for classifying PD from healthy subjects. This method provides an average accuracy of 85 % with sensitivity and specificity of about 86% and 84%. Important outcome of this study is that sustained vowels phonation captures distinguishing information for analysis and detection of PD.