A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.
| Title: | A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach. |
|---|---|
| Authors: | Oliveira GC; School of Engineering, RMIT University, Victoria, Australia; School of Sciences, São Paulo State University, São Paulo, Brazil. Electronic address: gc.oliveira@unesp.br.; Pah ND; School of Engineering, RMIT University, Victoria, Australia; Electrical Engineering, Universitas Surabaya, Surabaya, Indonesia. Electronic address: nemuel.daniel.pah@rmit.edu.au.; Ngo QC; School of Engineering, RMIT University, Victoria, Australia. Electronic address: quoc.cuong.ngo@rmit.edu.au.; Yoshida A; School of Sciences, São Paulo State University, São Paulo, Brazil. Electronic address: arissa.yoshida@unesp.br.; Gomes NB; School of Sciences, São Paulo State University, São Paulo, Brazil. Electronic address: nicolas.gomes@unesp.br.; Papa JP; School of Sciences, São Paulo State University, São Paulo, Brazil. Electronic address: joao.papa@unesp.br.; Kumar D; School of Engineering, RMIT University, Victoria, Australia. Electronic address: dinesh.kumar@rmit.edu.au. |
| Source: | Computers in biology and medicine [Comput Biol Med] 2025 Feb; Vol. 185, pp. 109565. Date of Electronic Publication: 2024 Dec 21. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Elsevier; Original Publication: New York, Pergamon Press. |
| MeSH Terms: | Parkinson Disease*/physiopathology ; Parkinson Disease*/diagnosis ; Speech*/physiology ; Machine Learning*; Humans ; Pilot Projects ; Male ; Female ; Aged ; Middle Aged ; Severity of Illness Index |
| Abstract: | Background: Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can identify people with PD (PwPD), but its multiclass application to detect the severity of the disease remains difficult.; Method: This study investigated six diadochokinetic (DDK) tasks, four features (phonation, articulation, prosody, and their fusion), and three machine learning models for four severity levels of PwPD. The four binary classifications were: (i) Normal vs Not Normal, (ii) Slight vs Not Slight, (iii) Mild vs Not Mild and (iv) Moderate vs. Not Moderate. The best task and features for each class were identified and the models were ensembled to develop a multiclass model to distinguish between Normal vs. Slight vs. Mild vs. Moderate.; Results: For Normal vs Not-normal, logistic regression (LR) using the prosody from "ka-ka-ka" task, Random Forest (RF) using articulation from "petaka" for Slight vs Not Slight, RF for the fusion from "ka-ka-ka" for Mild vs Not Mild and Gradient Boosting (GB) using prosody from "ta-ta-ta" for Moderate vs Not Moderate gave the best results. Combining these using LR achieved an accuracy of 72%.; Conclusion: Dividing the multiclass problem into four binary problems gives the optimum speech features for each class. This pilot study, conducted on a small public dataset, shows the potential of computerized speech analysis using DDK to evaluate the severity of Parkinson's disease voice symptoms.; (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Ensemble learning; MDS-UPDRS-speech; Parkinson’s disease; Speech analysis |
| Entry Date(s): | Date Created: 20241222 Date Completed: 20250428 Latest Revision: 20250519 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.compbiomed.2024.109565 |
| PMID: | 39709867 |
| Database: | MEDLINE |
Journal Article