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 Motoric impairment versus iron deposition gradient in the subthalamic nucleus in Parkinson’s disease

2020 ◽  
pp. 1-7
Author(s):  
Weiyuan Huang ◽  
Richard Ogbuji ◽  
Liangdong Zhou ◽  
Lingfei Guo ◽  
Yi Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subthalamic Nucleus ◽  
Rating Scale ◽  
Statistical Tests ◽  
Motor Impairment ◽  
Iron Deposition ◽  
Severe Motor ◽  
Severe Motor Impairment ◽  
The Right

OBJECTIVEThe objective of this study was to investigate the correlation between the quantitative susceptibility mapping (QSM) signal gradient of the subthalamic nucleus (STN) and motor impairment in patients with Parkinson’s disease (PD).METHODSAll PD patients who had undergone QSM MRI for presurgical deep brain stimulation (DBS) planning were eligible for inclusion in this study. The entire STN and its three functional subdivisions, as well as the adjacent white matter (WM), were segmented and measured. The QSM value difference between the entire STN and adjacent WM (STN-WM), between the limbic and associative regions of the STN (L-A), and between the associative and motor regions of the STN (A-M) were obtained as measures of gradient and were input into an unsupervised k-means clustering algorithm to automatically categorize the overall boundary distinctness between the STN and adjacent WM and between STN subdivisions (gradient blur [GB] and gradient sharp [GS] groups). Statistical tests were performed to compare clinical and image measurements for discrimination between GB and GS groups.RESULTSOf the 39 study patients, 19 were categorized into the GB group and 20 into the GS group, based on quantitative cluster analysis. The GB group had a significantly higher presurgical off-medication Unified Parkinson’s Disease Rating Scale Part III score (51.289 ± 20.741) than the GS group (38.5 ± 16.028; p = 0.037). The GB group had significantly higher QSM values for the STN and its three subdivisions and adjacent WM than those for the GS group (p < 0.01). The GB group also demonstrated a significantly higher STN-WM gradient in the right STN (p = 0.01). The GB group demonstrated a significantly lower L-A gradient in both the left and the right STN (p < 0.02).CONCLUSIONSAdvancing PD with more severe motor impairment leads to more iron deposition in the STN and adjacent WM, as shown in the QSM signal. Loss of the STN inner QSM signal gradient should be considered as an image marker for more severe motor impairment in PD patients.

scholarly journals Treadmill training and kinesiotherapy versus conventional physiotherapy in Parkinson’s disease: a pragmatic study

2019 ◽  
Vol 32 ◽  
Author(s):  
Thiago da Silva Rocha Paz ◽  
Fernando Guimarães ◽  
Vera Lúcia Santos de Britto ◽  
Clynton Lourenço Correa
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Clinical Status ◽  
Functional Independence ◽  
Treadmill Training ◽  
Clinical Aspects ◽  
Group 12 ◽  
Conventional Physiotherapy

Abstract Introduction: Physiotherapy has been identified in the literature as an important treatment for individuals with Parkinson’s disease (PD) to improve functional capacity. Little is discussed about the physiotherapy practice environment for this population. Objective: To assess pragmatically the effects of two physiotherapy protocols: Conventional Physiotherapy (CP) and Treadmill Training and Kinesiotherapy (TTK) in PD patients. Method: Twenty-four PD patients classified from 1 to 3 on the Hoehn and Yahr scale were randomly distributed into two groups. In CP group (12 patients), exercises aimed to improve range of motion, bradykinesia, postural adjustments and gait. In TTK group (12 patients), exercises aimed to improve physical fitness, mobility and functional independence. The treatments were performed for 50 minutes, twice a week for 14 weeks. The following evaluations were performed before and after the interventions: Unified Parkinson’s Disease Rating Scale (UPDRS); gait speed (GS); up stairs (US) and down stairs (DS) tests; timed get-up-and-go test (TUG) and 6-Minute Walk Distance Test (6-MWDT). Sociodemographic and clinical data were presented as descriptive analysis. Variables with normal and non-normal distributions were analyzed by specific statistical tests. Results: Intragroup analysis showed significant results for the TTK group (TUG, US, DS, GS, UPDRS total and UPDRS II) and for the CP group only UPDRS total. Intergroup analysis was favorable for the TTK group (TUG, US, DS, 6-MWDT). Conclusion: CP group improved the patients’ general clinical status, while treadmill and kinesiotherapy improved the physical-functional and clinical aspects.

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).

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.

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.  

scholarly journals Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease

TecnoLógicas ◽  
2020 ◽  
Vol 23 (47) ◽  
pp. 93-108 ◽  
Author(s):  
Felipe O. López-Pabón ◽  
Tomas Arias-Vergara ◽  
Juan R. Orozco-Arroyave
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Statistical Tests ◽  
Support Vector ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Hilbert Huang Transform ◽  
Cepstral Analysis ◽  
Healthy Control ◽  
The Stability

Most patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.

scholarly journals m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals

2021 ◽  
Vol 15 ◽  
pp. 34-41
Author(s):  
Luca Parisi ◽  
Amir Zaernia ◽  
Renfei Ma ◽  
Mansour Youseffi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Early Diagnosis ◽  
Real Life ◽  
Kernel Functions ◽  
Support Vector ◽  
Margin Width ◽  
Speech Data ◽  
Benchmark Datasets

Recent advances in the state-of-the-art open-source kernel functions for support vector machines (SVMs) have widened the choices of benchmark kernels for Machine Learning (ML)-based classification. However, it is still challenging to achieve margin maximisation in SVM, and further evidence is required to ensure such novel kernel functions can have translational applications with tangible impact. Noteworthily, m-arcsinh, freely available in scikit-learn, was preliminarily proven as a benchmark kernel function on 15 datasets in its seminal paper. Quantifying the benefit from leveraging this kernel in a specific application is essential to provide further evidence of its accuracy and reliability on real-life supervised ML-aided tasks. Thus, the predictive capability of SVM, including that with Lagrange multipliers for the first time coupled with m-arcsinh (m-ark-SVM with soft margin; m-arK-SVM with hard margin), is hereby assessed in aiding early detection of Parkinson’s Disease (PD) from speech data. This is important to leverage the m-arcsinh kernel ‘trick’ to maximise the margin width and, therefore, the linear separability of input speech features via automated pattern recognition. In this study, we demonstrate the accuracy and reliability of m-ark-SVM to aid early diagnosis of PD, evaluated against other gold standard kernel functions. Two benchmark datasets from the University of California-Irvine (UCI) database, pre-processed solely via min-max normalisation, were used to discriminate between speech patterns of 72 healthy subjects and 211 patients with PD. Overtraining was avoided via cross validation and the models were developed and tested in Python 3.7. The supervised model (m-ark-SVM) could detect early Parkinson’s Disease with 87.18% and 86.9% classification accuracy from the two datasets respectively (F1- scores: 85 and 86.2% correspondingly). Furthermore, the model achieved high precision (89.2% and 86.8%) and specificity (87% and 86.8%). Thus, this study validates the application of m-arcsinh to aid real-life supervised ML-based classification, in particular early diagnosis of Parkinson’s Disease from speech data.

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