Buscema M - Search Results

1

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. Buscema M, et al. Comput Intell Neurosci. 2007;2007:35021. doi: 10.1155/2007/35021. Comput Intell Neurosci. 2007. PMID: 18309366 Free PMC article.

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. Buscema M, et al. Artif Intell Med. 2007 Jun;40(2):127-41. doi: 10.1016/j.artmed.2007.02.006. Epub 2007 Apr 26. Artif Intell Med. 2007. PMID: 17466496

3

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. Buscema M, et al. Curr Alzheimer Res. 2010 Mar;7(2):173-87. doi: 10.2174/156720510790691137. Curr Alzheimer Res. 2010. PMID: 19860726

4

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. Rossini PM, et al. Among authors: buscema m. Clin Neurophysiol. 2008 Jul;119(7):1534-45. doi: 10.1016/j.clinph.2008.03.026. Epub 2008 May 15. Clin Neurophysiol. 2008. PMID: 18485814

5

The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease.

Buscema M, Grossi E. Buscema M, et al. Int J Data Min Bioinform. 2008;2(4):362-404. doi: 10.1504/ijdmb.2008.022159. Int J Data Min Bioinform. 2008. PMID: 19216342

6

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. Buscema M, et al. Artif Intell Med. 2015 May;64(1):59-74. doi: 10.1016/j.artmed.2015.03.003. Epub 2015 May 12. Artif Intell Med. 2015. PMID: 25997573

7

Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.

Grossi E, Olivieri C, Buscema M. Grossi E, et al. Among authors: buscema m. Comput Methods Programs Biomed. 2017 Apr;142:73-79. doi: 10.1016/j.cmpb.2017.02.002. Epub 2017 Feb 20. Comput Methods Programs Biomed. 2017. PMID: 28325448

8

The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.

Grossi E, Buscema M, Della Torre F, Swatzyna RJ. Grossi E, et al. Among authors: buscema m. Clin EEG Neurosci. 2019 Sep;50(5):319-331. doi: 10.1177/1550059419861007. Epub 2019 Jul 11. Clin EEG Neurosci. 2019. PMID: 31296052

9

Detection of an Autism EEG Signature From Only Two EEG Channels Through Features Extraction and Advanced Machine Learning Analysis.

Grossi E, Valbusa G, Buscema M. Grossi E, et al. Among authors: buscema m. Clin EEG Neurosci. 2021 Sep;52(5):330-337. doi: 10.1177/1550059420982424. Epub 2020 Dec 21. Clin EEG Neurosci. 2021. PMID: 33349054

10

Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.

Buscema M, Grossi E, Snowdon D, Antuono P, Intraligi M, Maurelli G, Savarè R. Buscema M, et al. Neuroinformatics. 2004;2(4):399-416. doi: 10.1385/NI:2:4:399. Neuroinformatics. 2004. PMID: 15800371 Free PMC article.

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