Fusion of WPT and MFCC feature extraction in Parkinson's disease diagnosis

Abstract

Background: Parkinson's disease (PD) is a neurological disorder, progressive in nature. In order to provide customized patient care, diagnosis and monitoring using smart gadgets, smartphones, and smartwatches, there is a need for a system that works in natural as well as controlled environments.

Objective and methods: The primary purpose is to record speech signal, and identify whether the speech signal is Parkinson or not. For this work, a comparison of three feature extraction methods, i.e. Wavelet Packets, MFCC, and a fusion of MFCC and WPT, were carried out. Apart from the feature extraction, two classifiers were used, i.e. HMM and SVM.

Results: In this study, a fusion of MFCC, WPT with HMM shows the best performance parameters.

Conclusion: The best of the three feature extraction and classifier results are described in this paper.

Keywords: Classifier; MFCC; Wavelet Packet Transforms; feature extraction; speech signal.