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- Title
Fusion of WPT and MFCC feature extraction in Parkinson's disease diagnosis.
- Authors
Kuresan, Harisudha; Samiappan, Dhanalakshmi; Masunda, Sam
- Abstract
<bold>Background: </bold>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.<bold>Objective and Methods: </bold>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.<bold>Results: </bold>In this study, a fusion of MFCC, WPT with HMM shows the best performance parameters.<bold>Conclusion: </bold>The best of the three feature extraction and classifier results are described in this paper.
- Subjects
PARKINSON'S disease; SMARTWATCHES; FEATURE extraction; NEUROLOGICAL disorders; PATIENT monitoring
- Publication
Technology & Health Care, 2019, Vol 27, Issue 4, p363
- ISSN
0928-7329
- Publication type
journal article
- DOI
10.3233/THC-181306