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 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 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 Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Parkinson’s Disease Motor Symptom Progression Slowed with Multisensory Dance Learning over 3-Years: A Preliminary Longitudinal Investigation

2021 ◽  
Vol 11 (7) ◽  
pp. 895
Author(s):  
Karolina A. Bearss ◽  
Joseph F. X. DeSouza
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daily Living ◽  
Rating Scale ◽  
Motor Impairment ◽  
Motor Dysfunction ◽  
Motor Symptom ◽  
Significant Group ◽  
Rate Of Decline ◽  
Symptom Progression

Parkinson’s disease (PD) is a neurodegenerative disease that has a fast progression of motor dysfunction within the first 5 years of diagnosis, showing an annual motor rate of decline of the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) between 5.2 and 8.9 points. We aimed to determine both motor and non-motor PD symptom progression while participating in dance classes once per week over a period of three years. Longitudinal data was assessed for a total of 32 people with PD using MDS-UPDRS scores. Daily motor rate of decline was zero (slope = 0.000146) in PD-Dancers, indicating no motor impairment, whereas the PD-Reference group showed the expected motor decline across three years (p < 0.01). Similarly, non-motor aspects of daily living, motor experiences of daily living, and motor complications showed no significant decline. A significant group (PD-Dancers and PD-Reference) by days interaction showed that PD who train once per week have less motor impairment (M = 18.75) than PD-References who do not train (M = 24.61) over time (p < 0.05). Training is effective at slowing both motor and non-motor PD symptoms over three years as shown in decreased scores of the MDS-UPDRS.

Gamma knife radiosurgery as a lesioning technique in movement disorder surgery

1997 ◽  
Vol 2 (3) ◽  
pp. E13 ◽  
Author(s):  
Ronald F. Young ◽  
Anne Shumway-Cook ◽  
Sandra S. Vermeulen ◽  
Peter Grimm ◽  
John Blasko ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Movement Disorder ◽  
Gamma Knife ◽  
Movement Disorders ◽  
Rating Scale ◽  
Gamma Knife Radiosurgery ◽  
Magnetic Resonance Images ◽  
Maximum Deviation

Fifty-five patients underwent radiosurgical placement of lesions either in the thalamus (27 patients) or globus pallidus (28 patients) for treatment of movement disorders. Patients were evaluated pre- and postoperatively by a team of observers skilled in the assessment of gait and movement disorders who were blinded to the procedure performed. They were not associated with the surgical team and concomitantly and blindly also assessed a group of 11 control patients with Parkinson's disease who did not undergo any surgical procedures. All stereotactic lesions were made with the Leksell gamma unit using the 4-mm secondary collimator helmet and a single isocenter with dose maximums from 120 to 160 Gy. Clinical follow-up evaluation indicated that 88% of patients who underwent thalamotomy became tremor free or nearly tremor free. Statistically significant improvements in performance were noted in the independent assessments of Unified Parkinson's Disease Rating Scale (UPDRS) scores in the patients undergoing thalamotomy. Eighty-five and seven-tenths percent of patients undergoing pallidotomy who had exhibited levodopa-induced dyskinesias had total or near-total relief of that symptom. Clinical assessment indicated improvement of bradykinesia and rigidity in 64.3% of patients who underwent pallidotomy. Independent blinded assessments did not reveal statistically significant improvements in Hoehn and Yahr scores or UPDRS scores. On the other hand, 64.7% of patients showed improvements in subscores of the UPDRS, including activities of daily living (58%), total contralateral score (58%), and contralateral motor scores (47%). Ipsilateral total UPDRS and ipsilateral motor scores were both improved in 59% of patients. One (1.8%) of 55 patients experienced a homonymous hemianopsia 9 months after pallidotomy due to an unexpectedly large lesion. No other complications of any kind were seen. Follow-up neuroimaging confirmed correct lesion location in all patients, with a mean maximum deviation from the planned target of 1 mm in the vertical axis. Measurements of lesions at regular interals on postoperative magnetic resonance images demonstrated considerable variability in lesion volumes. The safety and efficacy of functional lesions made with the gamma knife appear to be similar to those made with the assistance of electrophysiological guidance with open functional stereotactic procedures. Functional lesions may be made safely and accurately using gamma knife radiosurgical techniques. The efficacy is equivalent to that reported for open techniques that use radiofrequency lesioning methods with electrophysiological guidance. Complications are very infrequent with the radiosurgical method. The use of functional radiosurgical lesioning to treat movement disorders is particularly attractive in older patients and those with major systemic diseases or coagulopathies; its use in the general movement disorder population seems reasonable as well.

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 Diffusion Kurtosis Imaging of Substantia Nigra Is a Sensitive Method for Early Diagnosis and Disease Evaluation in Parkinson’s Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Guohua Zhang ◽  
Yuhu Zhang ◽  
Chengguo Zhang ◽  
Yukai Wang ◽  
Guixian Ma ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Diffusion Kurtosis Imaging ◽  
Healthy Controls ◽  
Potential Biomarker ◽  
Diffusion Kurtosis ◽  
Sensitive Method

Background.To diagnose Parkinson disease (PD) in an early stage and accurately evaluate severity, it is important to develop a sensitive method for detecting structural changes in the substantia nigra (SN).Method.Seventy-two untreated patients with early PD and 72 healthy controls underwent diffusion tensor and diffusion kurtosis imaging. Regions of interest were drawn in the rostral, middle, and caudal SN by two blinded and independent raters. Mean kurtosis (MK) and fractional anisotropy in the SN were compared between the groups. Receiver operating characteristic (ROC) and Spearman correlation analyses were used to compare the diagnostic accuracy and correlate imaging findings with Hoehn-Yahr (H-Y) staging and part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).Result.MK in the SN was increased significantly in PD patients compared with healthy controls. The area under the ROC curve was 0.976 for MK in the SN (sensitivity, 0.944; specificity, 0.917). MK in the SN had a positive correlation with H-Y staging and UPDRS-III scores.Conclusion.Diffusion kurtosis imaging is a sensitive method for PD diagnosis and severity evaluation. MK in the SN is a potential biomarker for imaging studies of early PD that can be widely used in clinic.

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.

scholarly journals Automated Assessment of the Substantia Nigra Pars Compacta in Parkinson’s Disease: A Diffusion Tensor Imaging Study

2021 ◽  
Vol 11 (11) ◽  
pp. 1235
Author(s):  
Niels Bergsland ◽  
Laura Pelizzari ◽  
Maria Marcella Laganá ◽  
Sonia Di Tella ◽  
Federica Rossetto ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Diffusion Tensor Imaging ◽  
Substantia Nigra ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Imaging Study ◽  
Substantia Nigra Pars Compacta ◽  
Disease Rating ◽  
Magnetic Resonance Imaging Mri

The substantia nigra (SN) pars compacta (SNpc) and pars reticulata (SNpr) are differentially affected in Parkinson’s disease (PD). Separating the SNpc and SNpr is challenging with standard magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) allows for the characterization of SN microstructure in a non-invasive manner. In this study, 29 PD patients and 28 healthy controls (HCs) were imaged with 1.5T MRI for DTI. Images were nonlinearly registered to standard space and SNpc and SNpr DTI parameters were measured. ANCOVA and receiver operator characteristic (ROC) analyses were performed. Clinical associations were assessed with Spearman correlations. Multiple corrections were controlled for false discovery rate. PD patients presented with significantly increased SNpc axial diffusivity (AD) (1.207 ± 0.068 versus 1.156 ± 0.045, p = 0.024), with ROC analysis yielding an under the curve of 0.736. Trends with Unified Parkinson’s Disease Rating Scale (UPDRS) III scores were identified for SNpc MD (rs = 0.449), AD (rs = 0.388), and radial diffusivity (rs = 0.391) (all p < 0.1). A trend between baseline SNpr MD and H&Y change (rs = 0.563, p = 0.081) over 2.9 years of follow-up was identified (n = 14). In conclusion, SN microstructure shows robust, clinically meaningful associations in PD.

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