Clustering of Voice Pathologies based on Sustained Voice Parameters

Oliveira, Alessa Anjos de; Dájer, María Eugenia; Fernandes, Paula Odete; Teixeira, João Paulo

doi:10.5220/0009146202800287

Cited by 5 publications

(1 citation statement)

References 10 publications

(14 reference statements)

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“… \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$R_{xx}[T_{0}]$ \end{document} is the peak next to the centre of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$R_{xx}$ \end{document} at a distance corresponding to the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$T_{0}$ \end{document} of the recording. The HNR and NHR were calculated as described in equations 10 and 11 [45] , [46] : \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{align*} HNR=&10\ast log\frac {R_{xx}[T_{0}]}{1-R_{xx}[T_{0}]}\tag{10}\\ NHR=&1-R_{xx}[T_{0}]\tag{11}\end{align*} \end{document} The scatter plots on Fig. 1 illustrate the distribution of the features for the different classes.…”

Section: Methodsmentioning

confidence: 99%

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Pah

Motin

Kempster

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Background: Parkinson’s disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. Method: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). Results: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD- off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD- off versus PD- on achieved an AUC of 0.81. Conclusion: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.

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Section: Methodsmentioning

confidence: 99%

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Pah

Motin

Kempster

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Statistical Analysis of Voice Parameters in Healthy Subjects and with Vocal Pathologies - HNR

André

Fernandes

Teixeira

2022

Communications in Computer and Information Science

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Phonemes based detection of parkinson’s disease for telehealth applications

Pah

Motin

Kumar

2022

Sci Rep

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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.

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Clustering of Voice Pathologies based on Sustained Voice Parameters

Cited by 5 publications

References 10 publications

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Statistical Analysis of Voice Parameters in Healthy Subjects and with Vocal Pathologies - HNR

Phonemes based detection of parkinson’s disease for telehealth applications

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