Schemata and sequential thought processes in PDP models
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
Foundations of genetic algorithms
Foundations of genetic algorithms
Backpropagation: theory, architectures, and applications
Backpropagation: theory, architectures, and applications
An introduction to genetic algorithms
An introduction to genetic algorithms
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
An optimized experimental protocol based on neuro-evolutionary algorithms
Artificial Intelligence in Medicine
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements
Artificial Intelligence in Medicine
Hi-index | 0.00 |
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.