Affiliations: Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
Correspondence:
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Corresponding author: Harisudha Kuresan, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattangulathur, Kancheepuram, Tamil Nadu 603203, India. Tel.: +91 7397459209; E-mail: harisudha.k@ktr.srmuniv.ac.in.
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.
