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.
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.
Entities:
Keywords:
Classifier; MFCC; Wavelet Packet Transforms; feature extraction; speech signal
Authors: S Hannah Pauline; Samiappan Dhanalakshmi; R Kumar; R Narayanamoorthi; Khin Wee Lai Journal: J Healthc Eng Date: 2022-04-30 Impact factor: 3.822