Classification accuracy of multivariate analysis applied to 99mTc-ECD SPECT data in Alzheimer s disease patients and asymptomatic controls

Merhof, Dorit; Markiewicz, Pawel J.; Declerck, Jérôme; Platsch, Günther; Matthews, Julian C.; Herholz, Karl

Classification accuracy of multivariate analysis applied to 99mTc-ECD SPECT data in Alzheimer s disease patients and asymptomatic controls

Dateien

merhof_classification_accuracy_2009.pdfGröße: 630.57 KBDownloads: 221

Datum

2009

Autor:innen

Merhof, Dorit

Markiewicz, Pawel J.

Declerck, Jérôme

Platsch, Günther

Matthews, Julian C.

Herholz, Karl

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). IEEE, 2009, pp. 3721-3725. ISBN 978-1-4244-3961-4. Available under: doi: 10.1109/NSSMIC.2009.5401871

Zusammenfassung

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%.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Single photon emission computed tomography (SPECT), Alzheimer s disease (AD), Multivariate Analysis, Principal Component Analysis (PCA)

Konferenz

2009 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009), 24. Okt. 2009 - 1. Nov. 2009, Orlando, FL

Zitieren

ISO 690

MERHOF, Dorit, Pawel J. MARKIEWICZ, Jérôme DECLERCK, Günther PLATSCH, Julian C. MATTHEWS, Karl HERHOLZ, 2009. Classification accuracy of multivariate analysis applied to 99mTc-ECD SPECT data in Alzheimer s disease patients and asymptomatic controls. 2009 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009). Orlando, FL, 24. Okt. 2009 - 1. Nov. 2009. In: 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). IEEE, 2009, pp. 3721-3725. ISBN 978-1-4244-3961-4. Available under: doi: 10.1109/NSSMIC.2009.5401871

BibTex

@inproceedings{Merhof2009-10Class-5731,
  year={2009},
  doi={10.1109/NSSMIC.2009.5401871},
  title={Classification accuracy of multivariate analysis applied to 99mTc-ECD SPECT data in Alzheimer s disease patients and asymptomatic controls},
  isbn={978-1-4244-3961-4},
  publisher={IEEE},
  booktitle={2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC)},
  pages={3721--3725},
  author={Merhof, Dorit and Markiewicz, Pawel J. and Declerck, Jérôme and Platsch, Günther and Matthews, Julian C. and Herholz, Karl}
}

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