CLASSIFICATION OF PARKINSON'S DISEASE PATIENTS USING NONLINEAR PHONETIC FEATURES AND MEL-FREQUENCY CEPSTRAL ANALYSIS

Abstract

This paper presents a combinational feature extraction approach using voice utterances for discriminating Parkinson's disease (PD) patients from healthy people. The proposed feature set consists of seven nonlinear phonetic features and 13 usual Mel-frequency cepstral coefficients (MFCCs). In this research, two new features — EDC-PIS (energy distribution coefficient of peak index series) and EDC-PMS (energy distribution coefficient of peak magnitude series) — were introduced, which are robust to many uncontrollable confounding effects such as noisy environments. The nonlinear phonetic features comprise recurrent period density entropy (RPDE), detrended fluctuation analysis (DFA), noise-to-harmonic ratio (NHR), fractal dimension (FD), pitch period entropy (PPE), EDC-PIS, and EDC-PMS. MFCC features have been widely used in voice processing tasks and therefore are good candidates to be used for the voice processing of PD subjects. The dataset used was composed of a range of 200 voice utterances from 25 PD subjects with different severity levels, and 10 normal persons. Using voice utterances from healthy and PD subjects, a 20-dimensional final feature set using MFCCs and nonlinear features is composed. Finally, a multilayer perceptron (MLP) neural network classifier with one hidden layer was used to discriminate PD subjects. Also, the proposed system was used for classification of mild and severe PD subjects. We obtained 97.5% overall correct classification performance for the discrimination of PD. In addition, we obtained 95.5% overall accuracy for the discrimination of mild and severe PD subjects.