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A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG

Abstract : This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.
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Submitted on : Tuesday, November 30, 2021 - 12:23:43 PM
Last modification on : Sunday, June 26, 2022 - 3:22:35 AM
Long-term archiving on: : Tuesday, March 1, 2022 - 6:54:49 PM

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Majd Abazid, Nesma Houmani, Jérôme Boudy, Bernadette Dorizzi, Jean Mariani, et al.. A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG. Entropy, MDPI, 2021, 23 (11), pp.1553:1-1553:22. ⟨10.3390/e23111553⟩. ⟨hal-03456928⟩

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