Daubechies Wavelet Cepstral Coefficients for Parkinson’s Disease Detection
- 15 September 2020
- journal article
- conference paper
- Published by Wolfram Research, Inc. in Complex Systems
- Vol. 29 (3), 729-739
- https://doi.org/10.25088/complexsystems.29.3.729
Abstract
The aim of this paper is to evaluate the performance of the approach that focuses on support vector machine (SVM) classification of vocal recording to differentiate between patients affected by Parkinson's disease (PD) and healthy patients. Our study was based on the condition of 38 patients, some of whom are healthy and others who suffer from PD. The study was carried out as follows: The extraction of cepstral coefficients was reached through the transformation of the speech signal by discrete wavelet transform (DWT) and also through cepstral analysis by using the mel scale. At the end, a classification was done by the use of the two kernels linear and radial basis function (RBF) of the SVM classifier.Keywords
This publication has 7 references indexed in Scilit:
- A hybrid method for the diagnosis and classifying parkinson's patients based on time–frequency domain properties and K-nearest neighborJournal of Medical Signals and Sensors, 2020
- RNN-based longitudinal analysis for diagnosis of Alzheimer’s diseaseComputerized Medical Imaging and Graphics, 2019
- Optimized cuttlefish algorithm for diagnosis of Parkinson’s diseaseCognitive Systems Research, 2018
- Alzheimer's disease diagnosis based on multiple cluster dense convolutional networksComputerized Medical Imaging and Graphics, 2018
- A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signalsBMC Bioinformatics, 2014
- Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound RecordingsIEEE Journal of Biomedical and Health Informatics, 2013
- The Intonation–Syntax Interface in the Speech of Individuals With Parkinson’s DiseaseJournal of Speech, Language, and Hearing Research, 2011