A Methodology to Differentiate Parkinson’s Disease and Aging Speech Based on Glottal Flow Acoustic Analysis

2020 ◽  
Vol 30 (10) ◽  
pp. 2050058
Author(s):  
Andrés Gómez-Rodellar ◽  
Daniel Palacios-Alonso ◽  
José M. Ferrández Vicente ◽  
Jiri Mekyska ◽  
Agustín Álvarez-Marquina ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Acoustic Analysis ◽  
Support Vector ◽  
Healthy Controls ◽  
Male And Female ◽  
Highly Sensitive ◽  
Glottal Flow ◽  
Improved Accuracy

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation.

scholarly journals Kinematic and Kinetic Patterns Related to Free-Walking in Parkinson’s Disease

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4224 ◽  
Author(s):  
Martín Martínez ◽  
Federico Villagra ◽  
Juan Castellote ◽  
María Pastor
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Clinical Status ◽  
Automatic Classification ◽  
Healthy Controls ◽  
Double Support ◽  
Gait Asymmetry ◽  
Double Support Time ◽  
Support Time

The aim of this study is to compare the properties of free-walking at a natural pace between mild Parkinson’s disease (PD) patients during the ON-clinical status and two control groups. In-shoe pressure-sensitive insoles were used to quantify the temporal and force characteristics of a 5-min free-walking in 11 PD patients, in 16 young healthy controls, and in 12 age-matched healthy controls. Inferential statistics analyses were performed on the kinematic and kinetic parameters to compare groups’ performances, whereas feature selection analyses and automatic classification were used to identify the signature of parkinsonian gait and to assess the performance of group classification, respectively. Compared to healthy subjects, the PD patients’ gait pattern presented significant differences in kinematic parameters associated with bilateral coordination but not in kinetics. Specifically, patients showed an increased variability in double support time, greater gait asymmetry and phase deviation, and also poorer phase coordination. Feature selection analyses based on the ReliefF algorithm on the differential parameters in PD patients revealed an effect of the clinical status, especially true in double support time variability and gait asymmetry. Automatic classification of PD patients, young and senior subjects confirmed that kinematic predictors produced a slightly better classification performance than kinetic predictors. Overall, classification accuracy of groups with a linear discriminant model which included the whole set of features (i.e., demographics and parameters extracted from the sensors) was 64.1%.

scholarly journals Endocannabinoid levels in patients with Parkinson’s disease with and without levodopa-induced dyskinesias

2020 ◽  
Author(s):  
Camila Marchioni ◽  
Bruno Lopes Santos-Lobato ◽  
Maria Eugênia Costa Queiroz ◽  
José Alexandre S. Crippa ◽  
Vitor Tumas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Endocannabinoid System ◽  
Column Switching ◽  
Healthy Controls ◽  
Highly Sensitive ◽  
Liquid Chromatography Tandem Mass ◽  
Csf Levels ◽  
Ultrahigh Performance Liquid Chromatography ◽  
Arachidonoyl Glycerol

AbstractBackgroundLevodopa-induced dyskinesias (LID) in Parkinson’s disease (PD) are frequent complications, and the endocannabinoid system has a role on its pathophysiology.ObjectiveTo test the hypothesis that the functioning of the endocannabinoid system would be altered in PD and in LID by measuring plasma and CSF levels of α-N-arachidonoylethanolamine (AEA) and 2-arachidonoyl-glycerol (2-AG) in patients with PD with and without LID and in healthy controls.MethodsBlood and CSF samples were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. The levels of AEA and 2-AG were measured using a highly sensitive column switching ultrahigh-performance liquid chromatography-tandem mass spectrometry method.ResultsWhen pooled together, patients with PD had lower plasma and CSF levels of 2-AG and higher CSF levels of AEA compared to healthy controls (Mann-Whitney statistics = 303.0, p = 0.02). Patients with PD without LID had lower CSF levels of 2-AG (Kruskal-Wallis statistics = 7.76, p = 0.02) and higher CSF levels of AEA levels than healthy controls (Kruskal-Wallis statistics = 8.81, p = 0.01).ConclusionsThe findings suggest that the endocannabinoid system participates in the pathophysiology of PD symptoms, but its role in the pathophysiology of LID is still unclear.

scholarly journals Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37718-37734 ◽  
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Asad Malik ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Recognition System ◽  
Support Vector ◽  
L1 Norm

Intelligent approach of score-based artificial fish swarm algorithm (SAFSA) for Parkinson's disease diagnosis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Haroon Abdul Gafoor ◽  
Padma Theagarajan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Content Type ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Medical Symptoms

PurposeConventional diagnostic techniques, on the other hand, may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify, potentially resulting in misdiagnosis. Meanwhile, early nonmotor signs of Parkinson’s disease (PD) can be mild and may be due to variety of other conditions. As a result, these signs are usually ignored, making early PD diagnosis difficult. Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD (like, movement disorders or other Parkinsonian syndromes).Design/methodology/approachMedical observations and evaluation of medical symptoms, including characterization of a wide range of motor indications, are commonly used to diagnose PD. The quantity of the data being processed has grown in the last five years; feature selection has become a prerequisite before any classification. This study introduces a feature selection method based on the score-based artificial fish swarm algorithm (SAFSA) to overcome this issue.FindingsThis study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database. Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant. According to a few objective functions, features subset chosen should provide the best performance.Research limitations/implicationsIn many situations, this is an Nondeterministic Polynomial Time (NP-Hard) issue. This method enhances the PD detection rate by selecting the most essential features from the database. To begin, the data set's dimensionality is reduced using Singular Value Decomposition dimensionality technique. Next, Biogeography-Based Optimization (BBO) for feature selection; the weight value is a vital parameter for finding the best features in PD classification.Originality/valuePD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor, kernel support vector machines, fuzzy convolutional neural network and random forest. The suggested classifiers are trained using data from UCI ML repository, and their results are verified using leave-one-person-out cross validation. The measures employed to assess the classifier efficiency include accuracy, F-measure, Matthews correlation coefficient.

scholarly journals Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

2021 ◽  
Author(s):  
Natalia Pelizari Novaes ◽  
Joana Bisol Balardin ◽  
Fabiana Campos Hirata ◽  
Luciano Melo ◽  
Edson Amaro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Healthy Controls ◽  
Global Efficiency ◽  
Motor Network

Vestibular semicircular canal function as detected by video Head Impulse Test (vHIT) is essentially unchanged in people with Parkinson’s disease compared to healthy controls

2021 ◽  
pp. 1-9
Author(s):  
Kim E. Hawkins ◽  
Elodie Chiarovano ◽  
Serene S. Paul ◽  
Ann M Burgess ◽  
Hamish G. MacDougall ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semicircular Canal ◽  
Neurodegenerative Disorder ◽  
Vestibular Function ◽  
Head Impulse Test ◽  
Healthy Controls ◽  
Video Head Impulse Test ◽  
Head Impulse ◽  
Vestibulo Ocular Reflex

BACKGROUND: Parkinson’s disease (PD) is a common multi-system neurodegenerative disorder with possible vestibular system dysfunction, but prior vestibular function test findings are equivocal. OBJECTIVE: To report and compare vestibulo-ocular reflex (VOR) gain as measured by the video head impulse test (vHIT) in participants with PD, including tremor dominant and postural instability/gait dysfunction phenotypes, with healthy controls (HC). METHODS: Forty participants with PD and 40 age- and gender-matched HC had their vestibular function assessed. Lateral and vertical semicircular canal VOR gains were measured with vHIT. VOR canal gains between PD participants and HC were compared with independent samples t-tests. Two distinct PD phenotypes were compared to HC using Tukey’s ANOVA. The relationship of VOR gain with PD duration, phenotype, severity and age were investigated using logistic regression. RESULTS: There were no significant differences between groups in vHIT VOR gain for lateral or vertical canals. There was no evidence of an effect of PD severity, phenotype or age on VOR gains in the PD group. CONCLUSION: The impulsive angular VOR pathways are not significantly affected by the pathophysiological changes associated with mild to moderate PD.

scholarly journals Oral Factors That Impact the Oral Microbiota in Parkinson’s Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1616
Author(s):  
Natalia S. Rozas ◽  
Gena D. Tribble ◽  
Cameron B. Jeter
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Oral Health ◽  
Soft Tissue ◽  
Oral Hygiene ◽  
Beta Diversity ◽  
Oral Microbiota ◽  
Healthy Controls ◽  
Microbiota Composition ◽  
Salivary Ph

Patients with Parkinson’s disease (PD) are at increased risk of aspiration pneumonia, their primary cause of death. Their oral microbiota differs from healthy controls, exacerbating this risk. Our goal was to explore if poor oral health, poor oral hygiene, and dysphagia status affect the oral microbiota composition of these patients. In this cross-sectional case-control study, the oral microbiota from hard and soft tissues of patients with PD (n = 30) and age-, gender-, and education-matched healthy controls (n = 30) was compared using 16S rRNA gene sequencing for bacterial identification. Study participants completed dietary, oral hygiene, drooling, and dysphagia questionnaires, and an oral health screening. Significant differences in soft tissue beta-diversity (p < 0.005) were found, and a higher abundance of opportunistic oral pathogens was detected in patients with PD. Factors that significantly influenced soft tissue beta-diversity and microbiota composition include dysphagia, drooling (both p < 0.05), and salivary pH (p < 0.005). Thus, patients with PD show significant differences in their oral microbiota compared to the controls, which may be due, in part, to dysphagia, drooling, and salivary pH. Understanding factors that alter their oral microbiota could lead to the development of diagnostic and treatment strategies that improve the quality of life and survivability of these patients.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

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