Classification of Neurodegenerative Diseases Using Gaussian Process Classification with Automatic Feature Determination

2010 ◽  
Author(s):  
Carlton Chu ◽  
Peter Bandettini ◽  
John Ashburner ◽  
Andre Marquand ◽  
Stefan Kloeppel
Keyword(s):  
Neurodegenerative Diseases ◽  
Gaussian Process ◽  
Process Classification

Gaussian process classification of superparamagnetic relaxometry data: Phantom study

2017 ◽  
Vol 82 ◽  
pp. 47-59 ◽  
Author(s):  
Javad Sovizi ◽  
Kelsey B. Mathieu ◽  
Sara L. Thrower ◽  
Wolfgang Stefan ◽  
John D. Hazle ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Phantom Study ◽  
Process Classification

Scalable Gaussian Process Classification With Additive Noise for Non-Gaussian Likelihoods

2021 ◽  
pp. 1-13
Author(s):  
Haitao Liu ◽  
Yew-Soon Ong ◽  
Ziwei Yu ◽  
Jianfei Cai ◽  
Xiaobo Shen
Keyword(s):  
Gaussian Process ◽  
Additive Noise ◽  
Non Gaussian ◽  
Process Classification

Rule based classification of neurodegenerative diseases using data driven gait features

2018 ◽  
Vol 9 (4) ◽  
pp. 547-560 ◽  
Author(s):  
Kartikay Gupta ◽  
Aayushi Khajuria ◽  
Niladri Chatterjee ◽  
Pradeep Joshi ◽  
Deepak Joshi
Keyword(s):  
Neurodegenerative Diseases ◽  
Data Driven ◽  
Rule Based ◽  
Gait Features ◽  
Using Data

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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