Here, we tested that healthy elderly (Nold) and Alzheimer’s disease (AD) individuals can be discriminated with a moderate accuracy using resting state eyes-closed electroencephalographic (rsEEG) markers. Eyes-closed rsEEG data were collected in 100 Nold and 120 AD subjects. eLORETA freeware estimated the source current density (SCD) and functional connectivity (lagged linear connectivity, LLC) in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), and alpha 2 (10.5–13 Hz) were the frequency bands of interest. Univariate (i.e., single rsEEG marker with receiver operating characteristic, ROC, curve) and multivariate (i.e., multiple rsEEG markers with artificial neural networks, ANNs) classifiers were used. The best accuracy was of 76% with univariate classifiers and 77% with multiple classifiers. The present results suggest that both univariate and multivariate rsEEG classifiers allowed a moderate classification rate between Nold and AD individuals. Interestingly, the accuracy based on multiple rsEEG markers as inputs to ANNs was similar to that obtained with a single rsEEG marker, unveiling their information redundancy for classification purposes. In future AD studies, multiple rsEEG markers should also include other classes of independent linear (i.e. directed transfer function) and nonlinear (i.e. entropy) variables to improve the classification.

Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: comparing different approaches / Del Percio, C.; Bevilacqua, V.; Brunetti, A.; Lizio, R.; Soricelli, A.; Ferri, R.; Nobili, F.; Gesualdo, L.; Logroscino, G.; De Tommaso, M.; Triggiani, A. I.; Bluma, M.; Frisoni, G. B.; Babiloni, C.. - 21:(2019), pp. 977-981. (Intervento presentato al convegno ICNR 2018: Converging Clinical and Engineering Research on Neurorehabilitation III tenutosi a Pisa) [10.1007/978-3-030-01845-0_196].

Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: comparing different approaches

Del Percio C.
;
Lizio R.;Bluma M.;Babiloni C.
2019

Abstract

Here, we tested that healthy elderly (Nold) and Alzheimer’s disease (AD) individuals can be discriminated with a moderate accuracy using resting state eyes-closed electroencephalographic (rsEEG) markers. Eyes-closed rsEEG data were collected in 100 Nold and 120 AD subjects. eLORETA freeware estimated the source current density (SCD) and functional connectivity (lagged linear connectivity, LLC) in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), and alpha 2 (10.5–13 Hz) were the frequency bands of interest. Univariate (i.e., single rsEEG marker with receiver operating characteristic, ROC, curve) and multivariate (i.e., multiple rsEEG markers with artificial neural networks, ANNs) classifiers were used. The best accuracy was of 76% with univariate classifiers and 77% with multiple classifiers. The present results suggest that both univariate and multivariate rsEEG classifiers allowed a moderate classification rate between Nold and AD individuals. Interestingly, the accuracy based on multiple rsEEG markers as inputs to ANNs was similar to that obtained with a single rsEEG marker, unveiling their information redundancy for classification purposes. In future AD studies, multiple rsEEG markers should also include other classes of independent linear (i.e. directed transfer function) and nonlinear (i.e. entropy) variables to improve the classification.
2019
ICNR 2018: Converging Clinical and Engineering Research on Neurorehabilitation III
directed transfer function; eLORETA; Current Source Density (SCD); Synchronization Likelihood; topographic markers
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: comparing different approaches / Del Percio, C.; Bevilacqua, V.; Brunetti, A.; Lizio, R.; Soricelli, A.; Ferri, R.; Nobili, F.; Gesualdo, L.; Logroscino, G.; De Tommaso, M.; Triggiani, A. I.; Bluma, M.; Frisoni, G. B.; Babiloni, C.. - 21:(2019), pp. 977-981. (Intervento presentato al convegno ICNR 2018: Converging Clinical and Engineering Research on Neurorehabilitation III tenutosi a Pisa) [10.1007/978-3-030-01845-0_196].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1504346
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