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New diagnostic EEG markers of the Alzheimer’s disease using visibility graph

  • Dementias - Original Article
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Abstract

A new chaos–wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer’s disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.

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Acknowledgments

The authors would like to thank Dr. Dennis Duke of Florida State University and Dr. Kerry Coburn of Mercer University for providing EEG data for this research project.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

    Mehran Ahmadlou

  2. Department of Biomedical Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  3. Department of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  4. Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  5. Department of Electrical and Computer Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  6. Department of Neurological Surgery, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  7. Department of Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 4310, USA

    Hojjat Adeli

  8. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, 15213, USA

    Anahita Adeli

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  1. Mehran Ahmadlou
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  2. Hojjat Adeli
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  3. Anahita Adeli
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Correspondence to Hojjat Adeli.

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Ahmadlou, M., Adeli, H. & Adeli, A. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J Neural Transm 117, 1099–1109 (2010). https://doi.org/10.1007/s00702-010-0450-3

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