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Journal Abstract Search
484 related items for PubMed ID: 19860726
1. The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy. Buscema M, Grossi E, Capriotti M, Babiloni C, Rossini P. Curr Alzheimer Res; 2010 Mar; 7(2):173-87. PubMed ID: 19860726 [Abstract] [Full Text] [Related]
2. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy. Buscema M, Rossini P, Babiloni C, Grossi E. Artif Intell Med; 2007 Jun; 40(2):127-41. PubMed ID: 17466496 [Abstract] [Full Text] [Related]
3. Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Rossini PM, Buscema M, Capriotti M, Grossi E, Rodriguez G, Del Percio C, Babiloni C. Clin Neurophysiol; 2008 Jul; 119(7):1534-45. PubMed ID: 18485814 [Abstract] [Full Text] [Related]
4. The implicit function as squashing time model: a novel parallel nonlinear EEG analysis technique distinguishing mild cognitive impairment and Alzheimer's disease subjects with high degree of accuracy. Buscema M, Capriotti M, Bergami F, Babiloni C, Rossini P, Grossi E. Comput Intell Neurosci; 2007 Jul; 2007():35021. PubMed ID: 18309366 [Abstract] [Full Text] [Related]
5. An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features. Buscema M, Vernieri F, Massini G, Scrascia F, Breda M, Rossini PM, Grossi E. Artif Intell Med; 2015 May; 64(1):59-74. PubMed ID: 25997573 [Abstract] [Full Text] [Related]