An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features

Paolo Maria Rossini, Massimo Buscema, Fabrizio Vernieri, Giulia Massini, Federica Scrascia, Marco Breda, Enzo Grossi

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

This paper proposes a new, complex algorithm for the blind classification of the original electroencephalogram (EEG) tracing of each subject, without any preliminary pre-processing. The medical need in this field is to reach an early differential diagnosis between subjects affected by mild cognitive impairment (MCI), early Alzheimer's disease (AD) and the healthy elderly (CTR) using only the recording and the analysis of few minutes of their EEG.
Original languageEnglish
Pages (from-to)59-74
Number of pages16
JournalArtificial Intelligence in Medicine
Volume64
DOIs
Publication statusPublished - 2015

Keywords

  • Alzheimer's disease
  • Electroencephalogram
  • Implicit function as squashing time
  • Mild cognitive impairment
  • Multi scale ranked organizing maps
  • Training with input selection and testing

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