Literature DB >> 34989833

Parkinson's disease patients with freezing of gait have more severe voice impairment than non-freezers during "ON state".

Qian Yu1, Xiaoya Zou1, Fengying Quan1, Zhaoying Dong1, Huimei Yin1, Jinjing Liu1, Hongzhou Zuo1, Jiaman Xu1, Yu Han1, Dezhi Zou1, Yongming Li2, Oumei Cheng3.   

Abstract

BACKGROUND: Speech disorders and freezing of gait (FOG) in Parkinson's disease (PD) may have some common pathological mechanisms. The purpose of this study was to compare the acoustic parameters of PD patients with dopamine-responsive FOG (PD-FOG) and without FOG (PD-nFOG) during "ON state" and explore the ability of "ON state" voice features in distinguishing PD-FOG from PD-nFOG.
METHODS: A total of 120 subjects, including 40 PD patients with dopamine-responsive FOG, 40 PD-nFOG, and 40 healthy controls (HCs) were recruited. All subjects underwent neuropsychological tests. Speech samples were recorded through the sustained vowel pronunciation tasks during the "ON state" and then analyzed by the Praat software. A set of 27 voice features was extracted from each sample for comparison. Support vector machine (SVM) was used to build mathematical models to classify PD-FOG and PD-nFOG.
RESULTS: Compared with PD-nFOG, the jitter, the standard deviation of fundamental frequency (F0SD), the standard deviation of pulse period (pulse period SD) and the noise-homophonic-ratio (NHR) were increased, and the maximum phonation time (MPT) was decreased in PD-FOG. The above voice features were correlated with the freezing of gait questionnaire (FOGQ). The average accuracy, specificity, and sensitivity of SVM models based on 27 voice features for classifying PD-FOG and PD-nFOG were 73.57%, 75.71%, and 71.43%, respectively.
CONCLUSIONS: PD-FOG have more severe voice impairment than PD-nFOG during "ON state".
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Entities:  

Keywords:  Acoustic analysis; Freezing of gait; Parkinson’s disease; Voice disorder

Mesh:

Substances:

Year:  2022        PMID: 34989833     DOI: 10.1007/s00702-021-02458-1

Source DB:  PubMed          Journal:  J Neural Transm (Vienna)        ISSN: 0300-9564            Impact factor:   3.850


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4.  Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network.

Authors:  Liaqat Ali; Ce Zhu; Zhonghao Zhang; Yipeng Liu
Journal:  IEEE J Transl Eng Health Med       Date:  2019-10-07       Impact factor: 3.316

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Journal:  Parkinsonism Relat Disord       Date:  2015-04-13       Impact factor: 4.891

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7.  Construction of freezing of gait questionnaire for patients with Parkinsonism.

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Journal:  Parkinsonism Relat Disord       Date:  2000-07-01       Impact factor: 4.891

8.  Prediction and Estimation of Parkinson's Disease Severity Based on Voice Signal.

Authors:  Daria Hemmerling; Magdalena Wojcik-Pedziwiatr
Journal:  J Voice       Date:  2020-08-15       Impact factor: 2.009

9.  Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.

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Authors:  Chao Gao; Jun Liu; Yuyan Tan; Shengdi Chen
Journal:  Transl Neurodegener       Date:  2020-04-15       Impact factor: 8.014

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  1 in total

1.  Motor Signatures in Digitized Cognitive and Memory Tests Enhances Characterization of Parkinson's Disease.

Authors:  Jihye Ryu; Elizabeth B Torres
Journal:  Sensors (Basel)       Date:  2022-06-11       Impact factor: 3.847

  1 in total

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