Use of machine learning method on automatic classification of motor subtype of Parkinson’s disease based on multilevel indices of rs-fMRI

2021 ◽  
Author(s):  
HuiZe Pang ◽  
ZiYang Yu ◽  
HongMei Yu ◽  
JiBin Cao ◽  
YingMei Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Automatic Classification ◽  
Machine Learning Method ◽  
Learning Method

scholarly journals Bilateral Globus Pallidus Internus Deep Brain Stimulation for Parkinson’s Disease: Therapeutic Effects and Motor Outcomes Prediction

2020 ◽  
Author(s):  
Dingding Shen ◽  
Linhao Cao ◽  
Yun Ling ◽  
Dianyou Li ◽  
Kang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Prediction Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
One Year ◽  
Deep Brain

Abstract Background: Deep brain stimulation (DBS) has emerged as a highly effective surgical treatment for advanced Parkinson’s disease (PD). Good response in levodopa challenge test has suggested as criterion to identify optimal candidates for surgery. However, the response to levodopa and DBS is not always congruent, and predictive value of the levodopa test remains controversial. This study was set out to identify predictors of response to DBS and develop a novel prediction model evaluating DBS candidacy. Methods: Herein, we retrospectively analyzed 62 consecutive PD patients who underwent bilateral globus pallidus interna (GPi) DBS from 2016 to 2019.  The changes in UPDRS-III (Unified Parkinson’s Disease Rating Scale part III) total and subscores after surgery at one-year follow-up were evaluated and potential predictor variables were also collected. In the training cohort of 29 patients, we developed a novel machine learning method with 5-fold cross validations implementing on these variables to predict GPi DBS treatment outcomes in a multivariate linear analysis. Furthermore, the machine learning model was externally validated with another cohort of 33 GPi DBS PD patients.Results: GPi DBS significantly improved postoperative motor function of PD patients. The overall UPDRS-III scores improved by 30.4%, with highest improvement in tremor (75.0%), followed by limb bradykinesia (27.5%), rigidity (27.3%) and axial bradykinesia (22.4%). Most intriguingly, improvement in tremor can be predicted with high accuracy using this prediction model (adjusted R2= 0.82 for absolute improvement, and adjusted R2 = 0.76 for relative improvement), in which off medication tremor subscore was identified as the most powerful preoperative predictor. In the external validation cohort, the machine learning method showed good predictive performance.Conclusions: We confirmed the effects of bilateral GPi-DBS with a one-year follow-up. The good performance of the present prediction model demonstrated the utility of machine-learning based motor response prediction after GPi DBS, based on clinical preoperative variables.

High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method

2017 ◽  
Vol 38 (11) ◽  
pp. 1980-1999 ◽  
Author(s):  
Hyoseon Jeon ◽  
Woongwoo Lee ◽  
Hyeyoung Park ◽  
Hong Ji Lee ◽  
Sang Kyong Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
High Accuracy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Parkinsonian Tremor

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Methodology Proposal of ADHD Classification of Children Based on Cross Recurrence Plots

2021 ◽  
Author(s):  
Marco Aceves-Fernandez
Keyword(s):  
Machine Learning ◽  
Spectral Distribution ◽  
High Dimensional ◽  
Control Group ◽  
Machine Learning Method ◽  
Learning Method ◽  
Recurrence Plots ◽  
Eeg Signals ◽  
Power Spectral

Abstract Dealing with electroencephalogram signals (EEG) are often not easy. The lack of predicability and complexity of such non-stationary, noisy and high dimensional signals is challenging. Cross Recurrence Plots (CRP) have been used extensively to deal with detecting subtle changes in signals even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent the proposed methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. Lastly, the results were compared with a baseline machine learning method to prove experimentally that this methodology is consistent and the results repeatable.

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

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