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Early Detection of Alzheimer’s Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals

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Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

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Abstract

The early detection Alzheimer’s disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using electroencephalographic (EEG) recordings: first a blind source separation algorithm is applied to extract the most significant spatio-temporal components; these components are subsequently wavelet transformed; the resulting time-frequency representation is approximated by sparse “bump modeling”; finally, reliable and discriminant features are selected by orthogonal forward regression and the random probe method. These features are fed to a simple neural network classifier. The method was applied to EEG recorded in patients with Mild Cognitive Impairment (MCI) who later developed AD, and in age-matched controls. This method leads to a substantially improved performance (93% correctly classified, with improved sensitivity and specificity) over classification results previously published on the same set of data. The method is expected to be applicable to a wide variety of EEG classification problems.

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Author information

Authors and Affiliations

  1. ESPCI (ParisTech), Laboratoire d’Electronique (CNRS UMR 7084), 10 rue Vauquelin, 75005, Paris, France

    François Vialatte & Gérard Dreyfus

  2. BSI RIKEN ABSP Lab, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan

    Andrzej Cichocki & Sergei L. Shishkin

  3. Brain Functions Laboratory Inc., KSP Building E211, Sakado, Takatsu Kawasaki-shi, Kanagawa, 213-0012, Japan

    Toshimitsu Musha

  4. Equipe Neurobiologie de la Mémoire Olfactive, Institut des Sciences Cognitives, (UMR 5015 CNRS UCB), 67 Boulevard Pinel, 69675, Bron Cedex, France

    Rémi Gervais

Authors
  1. François Vialatte
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  2. Andrzej Cichocki
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  3. Gérard Dreyfus
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  4. Toshimitsu Musha
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  5. Sergei L. Shishkin
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  6. Rémi Gervais
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Vialatte, F., Cichocki, A., Dreyfus, G., Musha, T., Shishkin, S.L., Gervais, R. (2005). Early Detection of Alzheimer’s Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_106

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  • DOI: https://doi.org/10.1007/11550822_106

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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