ArticleFrontiers in neuroscience2016
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.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.Disease markers · 2018Pooled it
- Predicting future risk of developing cognitive impairment using ambulatory sleep EEG: Integrating univariate analysis and multivariate information theory approach.Journal of Alzheimer's disease : JAD · 2025Article
- Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.Scientific reports · 2024Article
- Effect of sonification types in upper-limb movement: a quantitative and qualitative study in hemiparetic and healthy participants.Journal of neuroengineering and rehabilitation · 2023Article
- EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.Frontiers in human neuroscience · 2023Article
- [The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.Human brain mapping · 2022Article
- EEG analysis and classification based on cardinal spline empirical mode decomposition and synchrony features.Medical & biological engineering & computing · 2022Article
- Frontotemporal EEG as potential biomarker for early MCI: a case-control study.BMC psychiatry · 2022Article
- Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease.Frontiers in computational neuroscience · 2022Article
- On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.Journal of healthcare engineering · 2022Article
- Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2021Review
- Robust EEG Based Biomarkers to Detect Alzheimer's Disease.Brain sciences · 2021Article
- Routine magnetoencephalography in memory clinic patients: A machine learning approach.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Differentiating Alzheimer's Disease from Dementia with Lewy Bodies Using a Deep Learning Technique Based on Structural Brain Connectivity.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019Article
- EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions.Brain sciences · 2019Article
- Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging-electroencephalography study.Brain and behavior · 2019Article
- Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype.Brain : a journal of neurology · 2018Article
- Predicting Age From Brain EEG Signals-A Machine Learning Approach.Frontiers in aging neuroscience · 2018Article
- Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis.Frontiers in aging neuroscience · 2018Article
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27 authors.
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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.
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