NDLI: Classification accuracy of multivariate analysis applied to $^{99m}Tc-ECD$ SPECT data in Alzheimer's disease patients and asymptomatic controls

Content Provider	IEEE Xplore Digital Library
Author	Merhof, D. Markiewicz, P.J. Declerck, J. Platsch, G. Matthews, J.C. Herholz, K.
Copyright Year	2009
Description	Author affiliation: Research School of Translational Medicine at the University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK (Markiewicz, P.J.; Herholz, K.) \|\| School of Cancer, Enabling Sciences and Technology, Manchester Academic Health Science Centre, Wolfson Molecular Imaging Centre, The University of Manchester, Manchester, UK (Matthews, J.C.) \|\| Siemens Molecular Imaging EU, Erlangen, Germany (Platsch, G.) \|\| Siemens Molecular Imaging, Oxford, UK (Merhof, D.; Declerck, J.)
Abstract	With increasing life expectancy in developed countries, there is a corresponding increase in the frequency of diseases typically associated with old age, in particular dementia. In recent research, multivariate analysis of Positron Emission Tomography (PET) datasets has shown potential for classification between Alzheimer's disease (AD) patients and asymptomatic controls. In this work, the feasibility of multivariate analysis using Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) of Single Photon Emission Computed Tomography (SPECT) data is investigated. In order to obtain robust and reliable results, bootstrap resampling is applied and the robustness and classification accuracy of PCA/FDA are investigated. The robustness of the analysis is assessed by estimating the distribution of the angle between PCA/FDA discriminative vectors generated by bootstrap resampling, and the classification predictive accuracy is assessed using the 632 bootstrap estimator. The results indicate that PCA/FDA on SPECT data enables a robust differentiation between AD patients and asymptomatic controls based on three principal components, with a classification accuracy of 89%.
Starting Page	3721
Ending Page	3725
File Size	341765
Page Count	5
File Format	PDF
ISBN	9781424439614
ISSN	10957863
DOI	10.1109/NSSMIC.2009.5401871
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Positron emission tomography Alzheimer's disease Single photon emission computed tomography Molecular imaging Dementia Robustness Performance analysis Robust control Nuclear and plasma sciences Classification Accuracy Single photon emission computed tomography (SPECT) Alzheimer's disease (AD) Multivariate Analysis Principal Component Analysis (PCA)
Content Type	Text
Resource Type	Article

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