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.

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.

scholarly journals A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis

2018 ◽  
Vol 7 (3.29) ◽  
pp. 153 ◽  
Author(s):  
Satyabrata Aich ◽  
Pyari Mohan Pradhan ◽  
Jinse Park ◽  
Hee Cheol Kim
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Support Vector ◽  
Svm Classifier ◽  
3D Motion ◽  
Intelligent Technique ◽  
Gait Parameters

In recent times the adverse impact of Parkinson’s disease (PD) getting worse and worse with the increasing rate of old age population through out the world. This disease is the second common neurological disorder and has a tremendous economical and social impact because the cost associated with the healthcare as well as service is extremely high. The diagnosis process of this disease mostly done by closely observing the patient in the clinic as well as using the rating scale. However, this kind of diagnosis is subjective in nature and usually takes long time and assessment of this disease is complicated and cannot replicated in other patients. This kind of diagnosis method is also not suitable for the early detection of the PD. So, with this shortcoming it is necessary to find a suitable method that can automate the process as well as useful in the initial phase of diagnosis of PD. Recently with the invention of motion capture equipment’s and artificial intelligent technique, the feasibility of the objective nature-based diagnosis is getting lot of attention, especially the objective quantification of gait parameters. Shuffling of gait is one of the important characteristics of PD patients and it is usually defined y shorter stride length and low foot clearance. In this study a novel method is proposed to quantify the gait parameters using 3D motion captures and then various feature selection algorithm have used to select the effective features and finally machine learning based techniques were implemented to automate the classification process of two groups composed of PD patients as well as older adults. We have found maximum accuracy of 98.54 %by using support vector machine (SVM) classifier with radial basis function coupled with minimum redundancy and maximum relevance (MRMR) algorithm-based feature set. Our result showed that the proposed method can help the clinicians to distinguish PD patients from the older adults. This method helps to detect the PD at early stage.  

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 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 Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Atiqur Rahman ◽  
Sanam Shahla Rizvi ◽  
Aurangzeb Khan ◽  
Aaqif Afzaal Abbasi ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dimensionality Reduction ◽  
Intelligent System ◽  
Neurological Diseases ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant

Parkinson’s disease (PD) is one of the most common and serious neurological diseases. Impairments in voice have been reported to be the early biomarkers of the disease. Hence, development of PD diagnostic tool will help early diagnosis of the disease. Additionally, intelligent system developed for binary classification of PD and healthy controls can also be exploited in future as an instrument for prodromal diagnosis. Notably, patients with rapid eye movement (REM) sleep behaviour disorder (RBD) represent a good model as they develop PD with a high probability. It has been shown that slight speech and voice impairment may be a sensitive marker of preclinical PD. In this study, we propose PD detection by extracting cepstral features from the voice signals collected from people with PD and healthy subjects. To classify the extracted features, we propose to use dimensionality reduction through linear discriminant analysis and classification through support vector machine. In order to validate the effectiveness of the proposed method, we also developed ten different machine learning models. It was observed that the proposed method yield area under the curve (AUC) of 88%, sensitivity of 73.33%, and specificity of 84%. Moreover, the proposed intelligent system was simulated using publicly available multiple types of voice database. Additionally, the data were collected from patients under on-state. The obtained results on the public database are promising compared to the previously published work.

Feasibility of Smartphone-Based Testing of Interference in Parkinson’s Disease

2018 ◽  
Vol 18 (2-3) ◽  
pp. 133-142 ◽  
Author(s):  
Will Lee ◽  
David R. Williams ◽  
Andrew Evans
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convergent Validity ◽  
Rating Scale ◽  
Intraclass Correlation ◽  
Motor Impairment ◽  
Smartphone Application ◽  
Novel Approach ◽  
Comparison Results ◽  
Interference Test

Background: Interference refers to learned associations and established behaviors “interfering” with response to new material. It forms a core pillar of executive functions, which are commonly affected in Parkinson’s disease (PD). Cognitive interference test (CIT) forms part of a smartphone application designed for ambulatory assessment in PD. Objective: The aims of this study were to establish that CIT could effectively demonstrate interference and would perform comparably to the Stroop Color-Word Test Victoria version (VST) despite PD-related motor impairment. Methods: Ninety-nine patients with PD were recruited. Initial evaluation included CIT, VST, Montreal cognitive assessment (MOCA), and Movement Disorders Society-sponsored revision of the ­Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-III). A group of patients underwent repeat assessment within 2 weeks. Thirty-four healthy controls were recruited for comparison. Results: Patients’ mean age was 66.2 years, disease duration was 8.7 years, on-state MDS-UPDRS-III was 22, and MOCA total score was 27. CIT effectively generated interference, whereby the total time taken to complete the incongruent task was 20% longer compared to that of the baseline task. CIT key test items demonstrated convergent validity to VST (r = 0.478–0.644, p < 0.0001) and satisfactory repeatability (intraclass correlation coefficient 0.46–0.808, p ≤ 0.0002). Performance on key CIT test parameters deteriorated with increasing age (r = 0.225–0.478, p < 0.01) and MDS-UPDRS-III total score (r = 0.354–0.481, p < 0.0001). When compared to controls and patients with less motor impairment, patients MDS-UPDRS-III > 30 took longer to complete CIT and VST and had lower MOCA-attention sub-score, implying that the degree of motor impairment could not be the sole explanation for reduced CIT performance. Conclusions: We established that despite motor impairment, the novel approach of using smartphone technology to test interference in PD patients is feasible.

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).

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.

scholarly journals Serum N-Glycosylation in Parkinson’s Disease: A Novel Approach for Potential Alterations

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2220 ◽  
Author(s):  
Csaba Váradi ◽  
Károly Nehéz ◽  
Olivér Hornyák ◽  
Béla Viskolcz ◽  
Jonathan Bones
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Label Free ◽  
Dynamic Coating ◽  
Elimination Algorithm ◽  
Novel Approach ◽  
Label Free Quantitation

In this study, we present the application of a novel capillary electrophoresis (CE) method in combination with label-free quantitation and support vector machine-based feature selection (support vector machine-estimated recursive feature elimination or SVM-RFE) to identify potential glycan alterations in Parkinson’s disease. Specific focus was placed on the use of neutral coated capillaries, by a dynamic capillary coating strategy, to ensure stable and repeatable separations without the need of non-mass spectrometry (MS) friendly additives within the separation electrolyte. The developed online dynamic coating strategy was applied to identify serum N-glycosylation by CE-MS/MS in combination with exoglycosidase sequencing. The annotated structures were quantified in 15 controls and 15 Parkinson’s disease patients by label-free quantitation. Lower sialylation and increased fucosylation were found in Parkinson’s disease patients on tri-antennary glycans with 2 and 3 terminal sialic acids. The set of potential glycan alterations was narrowed by a recursive feature elimination algorithm resulting in the efficient classification of male patients.

scholarly journals MicroRNA Candidate Biomarkers for Parkinson’s Disease and Idiopathic REM Sleep Behavior Disorder

2021 ◽  
Author(s):  
Ligang Wu ◽  
Jun Liu ◽  
Yuanyuan Li ◽  
Ying Cao ◽  
Wei Liu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Individuals ◽  
Mirna Signature ◽  
Sleep Behavior

Abstract Background: Parkinson’s disease (PD), a severe neurodegenerative disorder, and idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD), a parasomnia recognized as the prodromal stage of synucleinopathies (including PD), both lack reliable, non-invasive biomarker tests for early intervention and management. Objectives: To investigate whether plasma extracellular vesicle (EV)-associated sncRNAs could discriminate PD and/or iRBD patients from healthy individuals.Methods: We optimized a cDNA library construction method, EVsmall-seq, for high throughput sequencing of sncRNAs associated with plasma EVs. We profiled EV-sncRNAs from the plasma of 60 normal controls, 56 iRBD patients, and 53 PD patients, and constructed a support vector machine (SVM) classifier to identify the informative miRNA features to distinguish PD and/or iRBD patients from healthy individuals. Results: First, a sixteen-miRNA signature was found to distinguish PD patients from healthy individuals with 88% sensitivity, 90.43% specificity, and 89.13% accuracy. Second, a three-miRNA signature was found to distinguish iRBD patients from healthy individuals with 96% sensitivity, 86.36% specificity, and 91.49% accuracy. Third, tweenty 20 miRNAs were found consistently increased or decreased in expression from healthy subjects to iRBD to PD patients, which might be linked to PD development through iRBD.Conclusions: Current study provides a valuable and highly informative dataset of EV-associated sncRNAs from plasma of iRBD and PD patients. We identified miRNA signature features that could serve as minimally-invasive, blood-based surveillance biomarkers for distinguishing iRBD or PD from healthy individuals with high sensitivity, specificity, and accuracy.

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