Early Detection of the Alzheimer Disease Combining Feature Selection and Kernel Machines

  • J. Ramírez
  • J. M. Górriz
  • M. López
  • D. Salas-Gonzalez
  • I. Álvarez
  • F. Segovia
  • C. G. Puntonet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5507)

Abstract

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.

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References

  1. 1.
    Ishii, K., Kono, A.K., Sasaki, H., Miyamoto, N., Fukuda, T., Sakamoto, S., Mori, E.: Fully automatic diagnostic system for early- and late-onset mild Alzheimer’s disease using FDG PET and 3D-SSP. European Journal of Nuclear Medicine and Molecular Imaging 33(5), 575–583 (2006)CrossRefGoogle Scholar
  2. 2.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  3. 3.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Inc., New York (1998)MATHGoogle Scholar
  4. 4.
    Enqing, D., Guizhong, L., Yatong, Z., Xiaodi, Z.: Applying support vector machines to voice activity detection. In: 6th International Conference on Signal Processing, vol. 2, pp. 1124–1127 (2002)Google Scholar
  5. 5.
    Ramírez, J., Yélamos, P., Górriz, J.M., Segura, J.C.: SVM-based speech endpoint detection using contextual speech features. Electronics Letters 42(7), 877–879 (2006)CrossRefGoogle Scholar
  6. 6.
    Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2006)CrossRefGoogle Scholar
  7. 7.
    Kim, K.I., Jung, K., Park, S.H., Kim, H.J.: Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1542–1550 (2002)CrossRefGoogle Scholar
  8. 8.
    Fung, G., Stoeckel, J.: SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowledge and Information Systems 11(2), 243–258 (2007)CrossRefGoogle Scholar
  9. 9.
    Salas-González, D., Górriz, J.M., Ramírez, J., Lassl, A., Puntonet, C.G.: Improved gauss-newton optimization methods in affine registration of SPECT brain images. IET Electronics Letters 44(22), 1291–1292 (2008)CrossRefGoogle Scholar
  10. 10.
    Friston, K.J., Ashburner, J., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, London (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • M. López
    • 1
  • D. Salas-Gonzalez
    • 1
  • I. Álvarez
    • 1
  • F. Segovia
    • 1
  • C. G. Puntonet
    • 2
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaSpain
  2. 2.Dept. of Architecture and Computer TechnologyUniversity of GranadaSpain

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