scholarly journals Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages

2018 ◽  
Vol 8 (6) ◽  
pp. 4265
Author(s):  
Elmehdi Benmalek ◽  
Jamal Elmhamdi ◽  
Abdelilah Jilbab
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Principal Component ◽  
Support Vector ◽  
Speech Signal Processing ◽  
Parkinson’S Diseases ◽  
Wide Range ◽  
Signal Processing Algorithms ◽  
Linear Svm

<p class="IJASEITParagraph">Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM.</p>

scholarly journals Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

2010 ◽  
Vol 8 (59) ◽  
pp. 842-855 ◽  
Author(s):  
Athanasios Tsanas ◽  
Max A. Little ◽  
Patrick E. McSharry ◽  
Lorraine O. Ramig
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Signal Processing ◽  
Symptom Severity ◽  
Large Scale ◽  
Rating Scale ◽  
Signal Processing Algorithm ◽  
Wide Range ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

scholarly journals Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mansu Kim ◽  
Hyunjin Park
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Support Vector ◽  
Svm Classifier ◽  
Research Database ◽  
Significant Group

Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches.Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography.Methods. Diffusion magnetic resonance images of SWEDD (n=37) and PD (n=40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences.Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections.Conclusions. The connections and regions identified represent candidates for future research investigations.

Classification of Postural Response in Parkinson’s Patients Using Support Vector Machines

2013 ◽  
Author(s):  
Amit Shukla ◽  
Ashutosh Mani ◽  
Amit Bhattacharya ◽  
Fredy Revilla
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Support Vector Machines ◽  
Rating Scale ◽  
Neuronal Cell ◽  
Postural Instability ◽  
Pharmacologic Therapy ◽  
Support Vector ◽  
Vector Machines

Parkinson’s disease (PD) is a neurodegenerative condition with neuronal cell death in the substantia nigra and striatal dopamine deficiency that produces slowness, stiffness, tremor, shuffling gait and postural instability. More than 1 million people in North America are affected by PD resulting in balance problems and falls. It is observed that postural instability and gait problems become resistant to pharmacologic therapy as the disease progresses. Furthermore, studies suggest that postural sway abnormalities are worsened by levodopa, the mainstay of therapy for PD. This paper presents a classification of postural balance test data using Support Vector Machines (SVM) to identify the effect of medicine (levodopa) as well as dyskinesia. It is demonstrated that SVM is a useful tool and can complement the widely accepted (but very resource intensive) Unified Parkinson’s Disease Rating Scale (UPDRS).

Apathy and impulse control disorders association: A study in a sample of Parkinson's disease patients

2016 ◽  
Vol 33 (S1) ◽  
pp. S396-S396
Author(s):  
N. Sáez-Francàs ◽  
N. Ramirez ◽  
J. Alegre-Martin ◽  
O. De Fabregues ◽  
J. Alvarez-Sabin ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Trait Anxiety ◽  
Impulse Control ◽  
Rating Scale ◽  
Neurodegenerative Disorder ◽  
Linear Regression Analysis ◽  
Impulse Control Disorders ◽  
Hamilton Depression Scale ◽  
Wide Range

IntroductionParkinson's disease (PD) is a neurodegenerative disorder that is associated with a wide range of motor symptoms, cognitive deficits and behavioral disorders. Apathy and impulse control disorders (ICDs) are common in these patients and have been considered opposite ends of a reward and motivation disorders continuum.AimTo evaluate the association and impact of ICDs presence on apathy symptoms in PDs patients, considering the influence of other psychopathological symptoms on this association.MethodsThis is a cross-sectional, observational study in which 115 consecutive medicated PD patients without dementia (mean age 61.22 ± 13.5 years; 63.5% men) were recruited. All the patients underwent a psychiatric and neurologic evaluation. Motor dysfunction was assessed with the Unified Parkinson's disease Rating Scale (UPDRS), ICDs were evaluated with the Minnesota Impulse Control Disorders Inventory (MIDI) and apathy with the Lille Apathy Scale (LARS). The Hamilton Depression scale (HAM-D). The State-Trait Anxiety Inventory (STAI-S) and Barrat Impulsivity Scale (BIS) were also administrated.ResultsTwenty-seven (23.5%) patients showed an ICD. Patients with an ICD scored higher in apathy (P = 0.012), trait anxiety (P = 0.003) and impulsivity (P = 0.008). There were no differences in depressive symptoms. In the linear regression analysis, TCI was associated with more severe apathy (b = 4.20, t = 2.15, P = 0.034).ConclusionsICDs and apathy are frequent in PD. Although ICDs have been related with a hyperdopaminergic state and apathy with low dopamine levels, the observed frequent association suggests common etiopathological mechanisms.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Task-Oriented Intelligent Solution to Measure Parkinson’s Disease Tremor Severity

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ghayth AlMahadin ◽  
Ahmad Lotfi ◽  
Marie Mc Carthy ◽  
Philip Breedon
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Collection ◽  
Rating Scale ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Common Symptom ◽  
Average Rule ◽  
Novel Approach

Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.

A novel method for automatic classification of Parkinson gait severity using front-view video analysis

2020 ◽  
pp. 1-11
Author(s):  
Taha Khan ◽  
Ali Zeeshan ◽  
Mark Dougherty
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Automatic Classification ◽  
Rank Test ◽  
Support Vector ◽  
Front View ◽  
Gait Impairment ◽  
Gait Assessment

BACKGROUND: Gait impairment is an essential symptom of Parkinson’s disease (PD). OBJECTIVE: This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis. METHODS: Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson’s disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal. Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation. RESULTS: Features significantly (p< 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%. CONCLUSION: Findings support the feasibility of this model for Parkinson’s gait assessment in the home environment.

020 Postural sway as a measure of disease severity in parkinson’s disease

2018 ◽  
Vol 89 (6) ◽  
pp. A9.2-A9
Author(s):  
Christian J Lueck ◽  
Susanne Ilschner ◽  
Alex Smith ◽  
Robin Vlieger ◽  
Chandi P Das ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Severity ◽  
Cognitive Assessment ◽  
Verbal Fluency ◽  
Rating Scale ◽  
Postural Sway ◽  
Clinical Status ◽  
Parkinson’S Diseases ◽  
Motor Component

IntroductionThe severity of Parkinson’s disease (PD) is difficult to assess accurately owing to the lack of a robust biological marker of disease progression, with consequent implications for prognosis and treatment. The current standard measure is the Unified Parkinson’s Disease Rating Scale (UPDRS) but this is hampered by considerable variability between observers and within subjects. Postural sway correlates well with complex brain functioning in other conditions. This study aimed to investigate the correlation of postural sway with the UPDRS and other non-motor measures of disease severity in patients with PD.Methods28 patients with PD (mean age 68 years, range 54–91; 18 male) underwent tests of cognition and quality of life [Montreal Cognitive Assessment (MoCA), Neuropsychiatry Unit Cognitive Assessment (NUCOG) and Parkinson’s Diseases Questionnaire (PDQ-39–1)], assessment of postural sway using a force plate, and assessment of clinical status using the motor component of the UPDRS.ResultsSway path length showed strong correlations with PDQ-39–1, MoCA and the verbal fluency component of the NUCOG (r=0.63,–0.75 and −0.57, respectively; p=0.002,<0.001 and 0.002, respectively) and, to a lesser degree, with the UPDRS III (r=0.45, p=0.018).ConclusionPostural sway shows potential as a sensitive measure of disease severity and brain function in PD, either alone or in combination with other measures. It appears to correlate better with measures of cognition, both general and executive (verbal fluency), and the PDQ measure of disease severity than with the motor component of the UPDRS.

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