IFAST: Implicit function as squashing time for eeg analysis - application

Paolo Maria Rossini, Massimo Buscema, Enzo Grossi

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

It has been shown that a new procedure (Implicit Function as Squashing Time, IFAST) based on Artificial Neural Networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects with classification accuracy of individual subjects higher than 92%. In this chapter the method has been applied to distinguish individual normal elderly (Nold) vs. Mild Cognitive Impairment (MCI) subjects, an important issue for the screening of large populations at high risk of AD. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 171 Nold and in 115 amnesic MCI subjects. The data inputs for the classification by IFAST were the weights of the connections within a non linear auto-associative ANN trained to generate the instant voltage distributions of 60-s artifact free EEG data. The most relevant features were selected and coincidently the dataset was split into two halves for the final binary classification (training and testing) performed by a supervised ANN. The classification of the individual Nold and MCI subjects reached 95.87% of sensitivity and 91.06% of specificity (93.46% of accuracy). These results indicate that IFAST can reliably distinguish eyes-closed resting EEG in individual Nold and MCI subjects, and may be used for large-scale periodic screening of large populations at risk of AD and personalized care.

Original languageEnglish
Title of host publicationArtificial Adaptive Systems in Medicine: New Theories and Models for New Applications in the Real World
PublisherBentham Science Publishers Ltd.
Pages104-113
Number of pages10
ISBN (Print)9781608053926
DOIs
Publication statusPublished - 2009

Keywords

  • Alzheimer's disease (AD)
  • Artificial neural networks (ANNs)
  • Electroencephalography (EEG)
  • Mild cognitive impairment (MCI)

ASJC Scopus subject areas

  • Medicine(all)

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    Rossini, P. M., Buscema, M., & Grossi, E. (2009). IFAST: Implicit function as squashing time for eeg analysis - application. In Artificial Adaptive Systems in Medicine: New Theories and Models for New Applications in the Real World (pp. 104-113). Bentham Science Publishers Ltd.. https://doi.org/10.2174/978160805042010901010104