Network analysis through the use of joint‐distribution entropy on EEG recordings of MCI patients during a visual short‐term memory binding task. Issue 2 (29th March 2019)

• Record Type: Journal Article
• Authors: Josefsson, Alexandra; Ibáñez, Agustín; Parra, Mario; Escudero, Javier
• Is Part Of: Healthcare technology letters. Volume 6:Issue 2(2019)
• Page Start: 27
• Page End: 31
• Publication Date: 2019-03-29
• Subjects: electroencephalography -- medical signal processing -- diseases -- neurophysiology -- brain -- entropy -- cognition; Alzheimer's disease -- mild cognitive impairment -- electroencephalogram recordings -- brain activity -- high temporal resolution -- brain functional connectivity -- functional network -- beta‐filtered EEG recordings -- short‐term memory binding task -- MCI patients -- EEG functional connectivity changes -- network differences -- network analysis -- joint‐distribution entropy -- early diagnosis; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2018.5060
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : The early diagnosis of Alzheimer's disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non‐linear measure of coupling of beta‐filtered EEG recordings during a short‐term memory binding task. It shows that the values of the small‐world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.