Abstract
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 (