title: An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  This study analyzed the EEGs of 272 subjects, recorded at Rome's Neurology Unit of the Policlinico Campus Bio-Medico. The EEG recordings were performed using 19 electrodes, in a 0.3-70Hz bandpass, positioned according to the International 10-20 System. Many powerful learning machines and algorithms have been proposed during the last 20 years to effectively resolve this complex problem, resulting in different and interesting outcomes. Among these algorithms, a new artificial adaptive system, named implicit function as squashing time (I-FAST), is able to diagnose, with high accuracy, a few minutes of the subject's EEG track; whether it manifests an AD, MCI or CTR condition. An updating of this system, carried out by adding a new algorithm, named multi scale ranked organizing maps (MS-ROM), to the I-FAST system, is presented, in order to classify with greater accuracy the unprocessed EEG's of AD, MCI and control subjects.
  
  
  RESULTS
  The proposed system has been measured on three independent pattern recognition tasks from unprocessed EEG tracks of a sample of AD subjects, MCI subjects and CTR: (a) AD compared with CTR; (b) AD compared with MCI; (c) CTR compared with MCI. While the values of accuracy of the previous system in distinguishing between AD and MCI were around 92%, the new proposed system reaches values between 94% and 98%. Similarly, the overall accuracy with best artificial neural networks (ANNs) is 98.25% for the distinguishing between CTR and MCI.
  
  
  CONCLUSIONS
  This new version of I-FAST makes different steps forward: (a) avoidance of pre-processing phase and filtering procedure of EEG data, being the algorithm able to directly process an unprocessed EEG; (b) noise elimination, through the use of a training variant with input selection and testing system, based on naïve Bayes classifier; (c) a more robust classification phase, showing the stability of results on nine well known learning machine algorithms; (d) extraction of spatial invariants of an EEG signal using, in addition to the unsupervised ANN, the principal component analysis and the multi scale entropy, together with the MS-ROM; a more accurate performance in this specific task.
authors.affiliation: Semeion Research Centre of Sciences of Communication, Via Sersale 117, Rome 00128, Italy; Department of Mathematical and Statistical Sciences, University of Colorado at Denver, P.O. Box 173364, Denver, CO, USA. Electronic address: m.buscema@semeion.it.
authors.forename: Massimo
authors.initials: M
authors.lastname: Buscema
authors.affiliation: Institute of Neurology, Campus Bio-Medico University, Via Álvaro del Portillo 200, 00128 Rome, Italy.
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Vernieri
authors.affiliation: Semeion Research Centre of Sciences of Communication, Via Sersale 117, Rome 00128, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Massini
authors.affiliation: Institute of Neurology, Campus Bio-Medico University, Via Álvaro del Portillo 200, 00128 Rome, Italy.
authors.forename: Federica
authors.initials: F
authors.lastname: Scrascia
authors.affiliation: Semeion Research Centre of Sciences of Communication, Via Sersale 117, Rome 00128, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Breda
authors.affiliation: Institute of Neurology, Catholic University of The Sacred Heart, Largo Agostino Gemelli 8, 00168 Rome, Italy.
authors.forename: Paolo Maria
authors.initials: PM
authors.lastname: Rossini
authors.affiliation: Semeion Research Centre of Sciences of Communication, Via Sersale 117, Rome 00128, Italy.
authors.forename: Enzo
authors.initials: E
authors.lastname: Grossi
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis*; D001499:Bayes Theorem; D060825:Cognitive Dysfunction / Q000175:diagnosis; D003937:Diagnosis, Differential; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Alzheimer's disease; Electroencephalogram; Implicit function as squashing time; Mild cognitive impairment; Multi scale ranked organizing maps; Training with input selection and testing