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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 35021, 15 pages
Research Article

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

1Semeion Research Centre of Sciences of Communication, Via Sersale, 117, Rome 00128, Italy
2Department of Human Physiology and Pharmacology, University of Rome La Sapienza, Rome 00185, Italy
3Ospedale San Giovanni Calibita “Fatebenefratelli”, Isola Tiberina, Rome 00153, Italy
4Casa di cura San Raffaele Cassino (Frosinone), San Raffaele Pisana, Rome, Italy
5IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia 25100, Italy
6Department of Clinical Neurosciences, University of Rome Campus Biomedico, Rome 00155 , Italy
7Bracco SpA Medical Department, Via E. Folli, 50, Milan 20134, Italy

Received 19 December 2006; Revised 7 June 2007; Accepted 1 August 2007

Academic Editor: Saied Sanei

Copyright © 2007 Massimo Buscema et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the procedure reported in our previous study (2007), this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing) performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (92%) (2007). Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG.