Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory
| Autor: | Alicia Carreres, Jesús Poza, Roberto Hornero, Carlos Gómez, Miguel A. Tola-Arribas, Mónica Cano, Alejandro Bachiller, Celia Juan-Cruz, Javier Gomez-Pilar |
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| Rok vydání: | 2017 |
| Předmět: |
Male
medicine.medical_specialty Entropy 0206 medical engineering Pilot Projects 02 engineering and technology Electroencephalography Audiology Machine learning computer.software_genre Models Biological 03 medical and health sciences 0302 clinical medicine Alzheimer Disease Resampling medicine Computer Graphics Dementia Entropy (information theory) Cluster Analysis Humans Clustering coefficient Aged medicine.diagnostic_test Artificial neural network business.industry Brain Graph theory medicine.disease 020601 biomedical engineering Sample entropy Case-Control Studies Female Artificial intelligence business Psychology computer 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p < 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD. |
| Databáze: | OpenAIRE |
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