ArticleBiosensors2019
Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease.
Article in Biosensors, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.Journal of central nervous system disease · 2026Article
- On the complexity matching and multiscale nonlinear perspective of voice restoration via fat injection laryngoplasty in unilateral vocal fold paralysis.Scientific reports · 2025Article
- Vocal Feature Changes for Monitoring Parkinson's Disease Progression-A Systematic Review.Brain sciences · 2025Review
- AFM signal model for dysarthric speech classification using speech biomarkers.Frontiers in human neuroscience · 2024Article
- Classification of Dysphonic Voices in Parkinson's Disease with Semi-Supervised Competitive Learning Algorithm.Biosensors · 2022Article
- Phonemes based detection of parkinson's disease for telehealth applications.Scientific reports · 2022Article
- Detecting Effect of Levodopa in Parkinson's Disease Patients Using Sustained Phonemes.IEEE journal of translational engineering in health and medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD-CO, PD-PD and CO-CO. Four features reported in the literature-normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)-were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.