The transferal of evidences derived from clinical research to single patient level: Automatic distinction of normal elderly vs. mild cognitive impairment subjects by resting EEG data processed by IFAST, a novel intelligent system

Paolo M. Rossini, Massimo Buscema, Enzo Grossi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%. Here we tested the hypothesis that this is the case also for the classification of individual normal elderly (Nold) vs. 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 publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
DOIs
Publication statusPublished - 2008
Event2008 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008 - New York City, NY, United States
Duration: May 19 2008May 22 2008

Other

Other2008 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2008
CountryUnited States
CityNew York City, NY
Period5/19/085/22/08

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

Fingerprint Dive into the research topics of 'The transferal of evidences derived from clinical research to single patient level: Automatic distinction of normal elderly vs. mild cognitive impairment subjects by resting EEG data processed by IFAST, a novel intelligent system'. Together they form a unique fingerprint.

Cite this