Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

2017 ◽  
Vol 42 (2) ◽  
Author(s):  
Qi Wei Oung ◽  
Hariharan Muthusamy ◽  
Shafriza Nisha Basah ◽  
Hoileong Lee ◽  
Vikneswaran Vijean
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Disease Severity ◽  
Empirical Wavelet Transform

Classification of Parkinson’s Disease Severity Using Heart Rate Variability Analysis

2012 ◽  
pp. 425-442
Author(s):  
Che-Wei Lin ◽  
Jeen-Shing Wang ◽  
Pau-Choo Chung ◽  
Chung-Yao Hsu ◽  
Li-Ming Liou ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Heart Rate ◽  
Parkinson's Disease ◽  
Heart Rate Variability ◽  
Disease Severity ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis

scholarly journals Disrupted morphological grey matter networks in early-stage Parkinson’s disease

2021 ◽  
Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Nannan Li ◽  
Wenbin Li ◽  
Graham J. Kemp ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Severity ◽  
Grey Matter ◽  
Early Stage ◽  
Clustering Coefficient ◽  
Subject Classification ◽  
Single Subject ◽  
Topological Organization

AbstractWhile previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

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

Treadmill exercise tests in persons with Parkinson’s disease: responses and disease severity

2015 ◽  
Vol 28 (5) ◽  
pp. 1009-1014 ◽  
Author(s):  
Mon S. Bryant ◽  
George R. Jackson ◽  
Jyhgong Gabriel Hou ◽  
Elizabeth J. Protas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Severity ◽  
Treadmill Exercise ◽  
Exercise Tests

scholarly journals Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Ryan J.H. West ◽  
Christopher J.H. Elliott ◽  
Alex R. Wade
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease
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