Triggiani, AI;
Bevilacqua, V;
Brunetti, A;
Lizio, R;
Tattoli, G;
Cassano, F;
Soricelli, A;
... Babiloni, C; + view all
(2017)
Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.
Frontiers in Neuroscience
, 10
, Article 604. 10.3389/fnins.2016.00604.
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Abstract
Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.
| Type: | Article |
|---|---|
| Title: | Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnins.2016.00604 |
| Publisher version: | http://doi.org/10.3389/fnins.2016.00604 |
| Language: | English |
| Additional information: | Copyright © 2017 Triggiani, Bevilacqua, Brunetti, Lizio, Tattoli, Cassano, Soricelli, Ferri, Nobili, Gesualdo, Barulli, Tortelli, Cardinali, Giannini, Spagnolo, Armenise, Stocchi, Buenza, Scianatico, Logroscino, Lacidogna, Orzi, Buttinelli, Giubilei, Del Percio, Frisoni and Babiloni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Alzheimer’s disease (AD), electroencephalography (EEG), exact low-resolution brain electromagnetic tomography (eLORETA), linear lagged connectivity, artificial neural networks (ANNs) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10070647 |
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