Electroencephalograms (EEG) recordings are now widely used more and more as a method to assess the susceptibility to Alzheimer's disease. In this study, we aimed at classifying control subjects from subjects with mild cognitive impairment (MCI) and from Alzheimer's disease (AD). For each subject, we computed the relative Fourier power of five frequency bands. Then for each frequency band, we estimated the mean power of five brain regions: frontal, left temporal, central, right temporal and posterior. There were an equivalent number of electrodes in each of the five regions. This grouping is very useful in normalizing the regional repartition of the information. We can form a three-way tensor, which is the Fourier power by frequency band and by brain region for each subject. From this tensor, we extracted characteristic filters for the classification of subjects using linear and nonlinear classifiers.
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References
Ferri, C.P. et al. Global prevalence of dementia: a Delphi consensus study. The Lancet 366, 2112–2117 (2006).
Alexander, G.E. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies. American Journal of Psychiatry 159, 738–745 (2002).
Deweer, B. et al. Memory disorders in probable Alzheimer's disease: the role of hippocampal atrophy as shown with MRI. British Medical Journal 58, 590 (1995).
Tanzi, R.E. & Bertram, L. New frontiers in Alzheimer's disease genetics. Neuron 32, 181–184 (2001).
Andreasen, N. et al. Evaluation of CSF-tau and CSF-Aβ42 as Diagnostic Markers for Alzheimer Disease in Clinical Practice, Archives of Neurology, 58, pp. 373–379 (2001).
Cichocki, A. et al. EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease. Clinical Neurophysiology 116, 729–737 (2005).
Buscema, M., Rossini, P., Babiloni, C. & Grossi, E. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy. Artificial Intelligence in Medicine 40, 127–141 (2007).
Huang, C. et al. Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study. Clinical Neurophysiology 111, 1961–1967 (2000).
Musha, T. et al. A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease. Clinical Neurophysiology 113, 1052–1058 (2002).
Pritchard, W.S. et al. EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures. Electroencephalography and Clinical Neurophysiology 91, 118–30 (1994).
Woon, W.L., Cichocki, A., Vialatte, F. & Musha, T. Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings. Physiological Measurement 28, 335–347 (2007).
Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R. & Yener, B. Multiway analysis of epilepsy tensors. Bioinformatics 23, i10–i18 (2007).
Acar, E., Bing, C.A., Bing, H. & Yener, B. in Proceedings of the 24th IASTED International Conference on Biomedical Engineering 317–322 (2006).
Lee, H., Kim, Y.D., Cichocki, A. & Choi, S. Nonnegative tensor factorization for continuous EEG classification. International Journal of Neural Systems 17, 305 (2007).
Andersson, C.A. & Bro, R. The N-way toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems 52, 1–4 (2000).
Bro, R. & Kiers, H.A.L. A new efficient method for determining the number of components in PARAFAC models. Contract 1999, 10377 (1984).
Coben, L.A., Danziger, W.L. & Berg, L. Frequency analysis of the resting awake EEG in mild senile dementia of Alzheimer type. Electroencephalography and Clinical Neurophysiology 55, 372–380 (1983).
Sloan, E.P., Fenton, G.W., Kennedy, N.S.J. & MacLennan, J.M. Electroencephalography and single photon emission computed tomography in dementia: a comparative study. Psychological Medicine 25, 631 (1995).
Atkinson, A.C. & Riani, M. Exploratory tools for clustering multivariate data. Computational Statistics and Data Analysis 52, 272–285 (2007).
Bro, R. PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38, 149–171 (1997).
Al Kiers, H., Ten Berge, J. & Bro, R. PARAFAC2: PART I. a direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics 13, 275–294 (1999).
Kim, Y.D., Cichocki, A. & Choi, S. in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2008) (IEEE, Las Vegas, Nevada, 2008).
Cichocki, A., Zdunek, R. & Amari, S. Nonnegative matrix and tensor factorization. Signal Processing Magazine, IEEE 25, 142–145 (2008).
Cichocki, A., Zdunek, R., Plemmons, R. & Amari, S. in ICANNGA-2007 (ed. Science, L.N.i.C.) 271–280 (Springer, Warsaw, Poland, 2007).
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Latchoumane, CF.V., Vialatte, FB., Jeong, J., Cichocki, A. (2009). EEG Classification of Mild and Severe Alzheimer's Disease Using Parallel Factor Analysis Method. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_60
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