Document Type : Research Paper

Authors

• Harisudha Kuresan
• Dhanalakshmi Samiappan

Department of Electronics and Communication Engineering, College of Engineering and Technology, Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar, Kattankulathur 603203. Kanchipuram, Chennai T.N, India

Abstract

Parkinson's Disease (PD) is a neurodegenerative disorder that affects predominantly neurons in the brain. The main purpose of this paper is to define a way in detecting the PD in its early stages. This has been achieved through the use of recorded speech, a biomarker in the natural environment in its original state.  In this paper, the Mel-Frequency Cepstral Coefficients (MFCC)  method is utilized to extract features from the recorded speech. The principal component analysis (PCA) and Genetic algorithm (GA) are then applied for feature extraction/selection. Once the features are selected, multiple classifiers are then applied for classification. Performance metrics such as accuracy, specificity, and sensitivity are measured. The result shows that Support Vector Machine (SVM) along with the GA has shown optimal performance.

Keywords

• Parkinson's disease
• support vector machine
• Mel Frequency Cepstral Coefficient
• principal component analysis
• accuracy
• sensitivity
• specificity
• Genetic Algorithm