TY - JOUR
T1 - An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features
AU - Buscema, Massimo
AU - Vernieri, Fabrizio
AU - Massini, Giulia
AU - Scrascia, Federica
AU - Breda, Marco
AU - Rossini, Paolo Maria
AU - Grossi, Enzo
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Electroencephalogram
KW - Implicit function as squashing time
KW - Mild cognitive impairment
KW - Multi scale ranked organizing maps
KW - Training with input selection and testing
KW - Alzheimer's disease
KW - Electroencephalogram
KW - Implicit function as squashing time
KW - Mild cognitive impairment
KW - Multi scale ranked organizing maps
KW - Training with input selection and testing
UR - http://hdl.handle.net/10807/69851
U2 - 10.1016/j.artmed.2015.03.003
DO - 10.1016/j.artmed.2015.03.003
M3 - Article
SN - 0933-3657
VL - 64
SP - 59
EP - 74
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
ER -