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

Antonio Ivano Triggiani, Vitoantonio Bevilacqua, Antonio Brunetti, Roberta Lizio, Giacomo Tattoli, Fabio Cassano, Andrea Soricelli, Raffaele Ferri, Flavio Nobili, Loreto Gesualdo, Maria Rosaria Barulli, Rosanna Tortelli, Valentina Cardinali, Antonio Giannini, Pantaleo Spagnolo, Silvia Armenise, Fabrizio Stocchi, Grazia Buenza, Gaetano Scianatico, Giancarlo LogroscinoGiordano Lacidogna, Francesco Orzi, Carla Buttinelli, Franco Giubilei, Claudio Del Percio, Giovanni B. Frisoni, Claudio Babiloni

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number604
JournalFrontiers in Neuroscience
Volume10
Issue numberJAN
DOIs
Publication statusPublished - 2017

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Keywords

  • Alzheimer's disease (AD)
  • Artificial neural networks (ANNs)
  • Electroencephalography (EEG)
  • Exact low-resolution brain electromagnetic tomography (eLORETA)
  • Linear lagged connectivity

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Triggiani, A. I., Bevilacqua, V., Brunetti, A., Lizio, R., Tattoli, G., Cassano, F., Soricelli, A., Ferri, R., Nobili, F., Gesualdo, L., Barulli, M. R., Tortelli, R., Cardinali, V., Giannini, A., Spagnolo, P., Armenise, S., Stocchi, F., Buenza, G., Scianatico, G., ... Babiloni, C. (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(JAN), [604]. https://doi.org/10.3389/fnins.2016.00604