Acoustic analysis of the voice in patients with Parkinson's disease and hypokinetic dysarthria

Abstract Purpose Investigate the association between levodopa therapy and vocal characteristics in Parkinson’s disease patients. Search strategy Studies published at MEDLINE, LILACS, and SciELO, from 1960 to December 2016. A systematic review and meta-analysis was performed using the following keywords: Parkinson’s disease; levodopa; L-dopa; voice; speech disorders; dysphonia; dysarthria. After analyzing titles and abstracts, two independent reviewers selected all clinical trials that met the eligibility criteria and selected the articles and the data recorded in a previously standardized table. Selection criteria Trials published in English between 1960 and December 2016 individuals with clinical diagnosis of Parkinson’s disease; use of levodopa therapy in stable doses; acoustic analysis combined or not with auditory-perceptual analysis to evaluate the vocal parameters under investigation. Data analysis The following vocal parameters were analyzed: fundamental frequency (F 0), jitter, and vocal intensity. Standardized mean differences (SMD) were calculated using the Comprehensive Meta-analysis V2 software. Results Nine articles met the eligibility criteria and were selected, with a total of 119 individuals. From these, six articles with 83 individuals were included in the meta-analysis. During the levodopa therapy “on” state, modifications in F 0 (SMD=0.39; 95% CI - 0.21-0.57) and jitter (SMD=0.23; 95% CI - 0.02-0.45) were observed. Vocal intensity was not affected (SMD=0.09; 95% CI - 0.22-0.39) by levodopa ingestion. Data of the included studies were controversial in the auditory-perceptual analysis of voice. Conclusion Levodopa therapy modifies F0 and jitter. No changes in vocal intensity were observed in either the “on” or “off” states of levodopa therapy.

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A comparison of the voice difference of persons with Idiopathic Parkinson's disease and a normal group in five vowels

Phonetics and Speech Sciences ◽

10.13064/ksss.2012.4.1.119 ◽

2012 ◽

Vol 4 (1) ◽

pp. 119-124 ◽

Cited By ~ 1

Author(s):

In-Ae Lee ◽

Moon-Jeoung Kim ◽

Young-Jin Hwang

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Normal Group ◽

Idiopathic Parkinson’S Disease ◽

Idiopathic Parkinson's Disease ◽

The Voice

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Classification of Parkinson's Disease from Smartphone Recording Data using Time-Frequency Analysis and Convolutional Neural Network (Preprint)

10.2196/preprints.35339 ◽

2021 ◽

Author(s):

Denchai Worasawate ◽

Warisara Asawaponwiput ◽

Natsue Yoshimura ◽

Apichart Intarapanich ◽

Decho Surangsrirat

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Fourier Transform ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Non Invasive ◽

The Central Nervous System ◽

Short Time ◽

The Voice

BACKGROUND Parkinson’s disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affect most patients over the course of their illness is voice impairment. OBJECTIVE Voice is one of the non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible disease biomarker. The dataset is from one of the largest mobile PD studies, the mPower study. METHODS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. CONCLUSIONS Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CLINICALTRIAL ClinicalTrials.gov NCT02696603; https://www.clinicaltrials.gov/ct2/show/NCT02696603

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Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

Applied Sciences ◽

10.3390/app8122339 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2339 ◽

Cited By ~ 5

Author(s):

Zoltan Galaz ◽

Jiri Mekyska ◽

Vojtech Zvoncak ◽

Jan Mucha ◽

Tomas Kiska ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Scientific Community ◽

Partial Correlation ◽

Acoustic Analysis ◽

Partial Correlation Analysis ◽

Clinical Scales ◽

Clinical Scores ◽

The Relationship

Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients’ voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11–26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.

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Acoustic analysis of speech prosody in Huntington's and Parkinson's disease: A preliminary report

Clinical Linguistics & Phonetics ◽

10.1080/02699209308985568 ◽

1993 ◽

Vol 7 (4) ◽

pp. 285-297 ◽

Cited By ~ 13

Author(s):

I. Hertrich ◽

H. Ackermann

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Preliminary Report ◽

Acoustic Analysis ◽

Speech Prosody

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Acoustic and hearing-perceptual voice analysis in individuals with idiopathic Parkinson's disease in "on" and "off" stages

Arquivos de Neuro-Psiquiatria ◽

10.1590/s0004-282x2010000500006 ◽

2010 ◽

Vol 68 (5) ◽

pp. 706-711 ◽

Cited By ~ 9

Author(s):

Luiza Lara M Santos ◽

Larissa Oliveira dos Reis ◽

Iara Bassi ◽

Clara Guzella ◽

Francisco Cardoso ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Acoustic Analysis ◽

Voice Quality ◽

Control Group ◽

Idiopathic Parkinson’S Disease ◽

Acoustic Parameters ◽

Perceptual Analysis ◽

Idiopathic Parkinson's Disease ◽

Vocal Quality

OBJECTIVE: To evaluate the voice quality of patients with idiopathic Parkinson's disease, at the "on" and "off" moments of the disease. METHOD: Five individuals with Parkinson's disease and five of the control group were assessed. All of them underwent the recording of voice and speech. The acoustic parameters analyzed were: fundamental frequency, jitter, shimmer, harmonic noise proportion and index of tremor, besides performing the hearing-perceptual analysis by means of GRBASI scale. The findings were analyzed using statistics through t test and the level of significance adopted was p<0.05. RESULTS: There was no difference in the acoustic parameters in the three analyzed groups. In the hearing-perceptual analysis, patients with idiopathic Parkinson's disease showed altered voice quality and the ones from the control group, neutral vocal quality. CONCLUSION: Patients with idiopathic Parkinson's disease present rough, breathy and unstable vocal quality in both stages. In the acoustic analysis, there are no differences in the studied parameters.

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