Analysis of long range dependence in the EEG signals of Alzheimer patients

Abstract

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

Event related potential analysis techniques for autism spectrum disorders: A review

“…The previous EEG based studies revealed the potential of non-linear techniques such as correlation dimension, fractal dimension, entropies, higher order spectra and detrended fluctuation analysis in ASD (Bhat et al, 2014). Similar studies were reported in depression (Acharya et al, 2015) and Alzheimer cases also (John et al, 2018). Hence these studies could also be employed to unravel the information embedded in ERP based investigations in ASD.…”

Regional Hurst Exponent Reflects Impulsivity-Related Alterations in Fronto-Hippocampal Pathways Within the Waiting Impulsivity Network

“…For example, H in the prefrontal cortex in healthy volunteers correlated with impulsivity ( Gentili et al, 2020 ), personality traits (e.g., extraversion Lei et al, 2013 ; Gentili et al, 2017 ), cognitive processing (response time in a face recognition task Wink et al, 2008 ), and healthy aging ( Dong et al, 2018 ; Mukli et al, 2018 ). In addition, pathological processes were discovered, e.g., in the IFG of schizophrenic patients ( Sokunbi et al, 2014 ), the MFG of patients with mild cognitive impairment ( Long et al, 2016 , 2018 ), and Alzheimer’s Disease ( Nimmy John et al, 2018 ) as well as the IFG of autistic individuals ( Lai et al, 2010 ). Thus, frontal processing has been described as following fractal rules before.…”

Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer’s and Parkinson’s disease

“…Tylová et al [56] PeEn Nimmy John et al [57] EEG HE Nobukawa et al [58] EEG HFD Amezquita et al [59] EEG HFD, HE Echegoyen et al [60] MEG PeEn Zorick et al [9] EEG MF-DFA Seker et al [61] EEG PeEn Ando et al [8] EEG MF-DFA MSE a dimension = 1) and less than an area (characterized by dimension = 2) and can be described by a dimension value between 1 and 2 (fractional dimension) [62]. One of the main properties of fractal systems, and then of fractal time series, are scale invariance and self-similarity [7].…”