Classification of Alzheimer's disease patients with hippocampal shape, wrapper based feature selection and support vector machine

2012 ◽  
Author(s):  
Jonathan Young ◽  
Gerard Ridgway ◽  
Kelvin Leung ◽  
Sebastien Ourselin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Support Vector

scholarly journals Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

2017 ◽  
Vol 17 (0) ◽  
pp. 112-124
Author(s):  
Asuka Hatabu ◽  
Masafumi Harada ◽  
Yoshitake Takahashi ◽  
Shunsuke Watanabe ◽  
Kenya Sakamoto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Support Vector

Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI

2008 ◽  
Vol 51 (2) ◽  
pp. 73-83 ◽  
Author(s):  
Benoît Magnin ◽  
Lilia Mesrob ◽  
Serge Kinkingnéhun ◽  
Mélanie Pélégrini-Issac ◽  
Olivier Colliot ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Support Vector ◽  
Whole Brain ◽  
Anatomical Mri

Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine

2020 ◽  
Vol 40 (4) ◽  
pp. 545-554
Author(s):  
Bang-Hung Yang ◽  
Jyh-Cheng Chen ◽  
Wen-Hsiang Chou ◽  
Wen-Sheng Huang ◽  
Jong-Ling Fuh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Pet Imaging ◽  
Imaging Biomarkers ◽  
Support Vector ◽  
18F Fdg

scholarly journals Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model

2020 ◽  
Author(s):  
S. Rajintha. A. S. Gunawardena ◽  
Fei He ◽  
Ptolemaios Sarrigiannis ◽  
Daniel J. Blackburn
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gaussian Process ◽  
Manifold Learning ◽  
Latent Variable ◽  
Time Window ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Variable Model

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.

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