Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages
| Autor: | Elmehdi Benmalek, Abdelilah Jilbab, Jamal Elmhamdi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Parkinson's disease
General Computer Science business.industry 0206 medical engineering Linear svm Pattern recognition 02 engineering and technology medicine.disease 020601 biomedical engineering Cross-validation Support vector machine 030507 speech-language pathology & audiology 03 medical and health sciences Rating scale Principal component analysis Signal processing algorithms Medicine Artificial intelligence Electrical and Electronic Engineering 0305 other medical science business |
| Zdroj: | International Journal of Electrical and Computer Engineering (IJECE). 8:4265 |
| ISSN: | 2088-8708 |
| Popis: | Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM. |
| Databáze: | OpenAIRE |
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