Phonemes based detection of parkinson's disease for telehealth applications.
| Title: | Phonemes based detection of parkinson's disease for telehealth applications. |
|---|---|
| Authors: | Pah ND; Electrical Engineering Department, Universitas Surabaya, Surabaya, Indonesia.; School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.; Motin MA; School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.; Kumar DK; School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia. dinesh@rmit.edu.au. |
| Source: | Scientific reports [Sci Rep] 2022 Jun 11; Vol. 12 (1), pp. 9687. Date of Electronic Publication: 2022 Jun 11. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH Terms: | Parkinson Disease*/diagnosis ; Telemedicine* ; Voice*; Databases, Factual ; Humans ; Support Vector Machine |
| Abstract: | Dysarthria is an early symptom of Parkinson's disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan's dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.; (© 2022. The Author(s).) |
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| Entry Date(s): | Date Created: 20220611 Date Completed: 20220614 Latest Revision: 20220810 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC9188600 |
| DOI: | 10.1038/s41598-022-13865-z |
| PMID: | 35690657 |
| Database: | MEDLINE |
Journal Article