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

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.