Analysis of Long Range Dependence in the EEG Signals of Alzheimer Patients

Overview

Journal Cogn Neurodyn

Publisher Springer

Specialty Neurology

Date 2018 Mar 23

PMID 29564027

Citations 13

Authors

T Nimmy John

Subha D Puthankattil

Ramshekhar Menon

Affiliations

Soon will be listed here.

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.

Citing Articles

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References

Dauwels J, Srinivasan K, Ramasubba Reddy M, Musha T, Vialatte F, Latchoumane C . Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?. Int J Alzheimers Dis. 2011; 2011:539621. PMC: 3090755. DOI: 10.4061/2011/539621. View

Carlino E, Sigaudo M, Pollo A, Benedetti F, Mongini T, Castagna F . Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia. J Psychiatry Neurosci. 2012; 37(4):259-66. PMC: 3380097. DOI: 10.1503/jpn.110030. View

Morris J, Storandt M, Miller J, McKeel D, Price J, Rubin E . Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001; 58(3):397-405. DOI: 10.1001/archneur.58.3.397. View

Grunwald M, Busse F, Hensel A, Riedel-Heller S, Kruggel F, Arendt T . Theta-power differences in patients with mild cognitive impairment under rest condition and during haptic tasks. Alzheimer Dis Assoc Disord. 2002; 16(1):40-8. DOI: 10.1097/00002093-200201000-00006. View

Binnewijzend M, Schoonheim M, Sanz-Arigita E, Wink A, van der Flier W, Tolboom N . Resting-state fMRI changes in Alzheimer's disease and mild cognitive impairment. Neurobiol Aging. 2011; 33(9):2018-28. DOI: 10.1016/j.neurobiolaging.2011.07.003. View

Ahmadlou M, Adeli H, Adeli A . New diagnostic EEG markers of the Alzheimer's disease using visibility graph. J Neural Transm (Vienna). 2010; 117(9):1099-109. DOI: 10.1007/s00702-010-0450-3. View

Klimesch W . EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 1999; 29(2-3):169-95. DOI: 10.1016/s0165-0173(98)00056-3. View

Adler G, Brassen S, Jajcevic A . EEG coherence in Alzheimer's dementia. J Neural Transm (Vienna). 2003; 110(9):1051-8. DOI: 10.1007/s00702-003-0024-8. View

Folstein M, Folstein S, McHugh P . "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975; 12(3):189-98. DOI: 10.1016/0022-3956(75)90026-6. View

10.

Yang A, Wang S, Lai K, Tsai C, Yang C, Hwang J . Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease. Prog Neuropsychopharmacol Biol Psychiatry. 2013; 47:52-61. DOI: 10.1016/j.pnpbp.2013.07.022. View

11.

Stam C, Montez T, Jones B, Rombouts S, van der Made Y, Pijnenburg Y . Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease. Clin Neurophysiol. 2005; 116(3):708-15. DOI: 10.1016/j.clinph.2004.09.022. View

12.

Wan B, Ming D, Qi H, Xue Z, Yin Y, Zhou Z . Linear and nonlinear quantitative EEG analysis. IEEE Eng Med Biol Mag. 2008; 27(5):58-63. DOI: 10.1109/MEMB.2008.923957. View

13.

Davatzikos C, Fan Y, Wu X, Shen D, Resnick S . Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging. 2006; 29(4):514-23. PMC: 2323584. DOI: 10.1016/j.neurobiolaging.2006.11.010. View

14.

Abe J, Lopes H, Anghinah R . Paraconsistent artificial neural networks and Alzheimer disease: a preliminary study. Dement Neuropsychol. 2017; 1(3):241-247. PMC: 5619001. DOI: 10.1590/S1980-57642008DN10300004. View

15.

Stam C, de Haan W, Daffertshofer A, Jones B, Manshanden I, Van Cappellen van Walsum A . Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain. 2008; 132(Pt 1):213-24. DOI: 10.1093/brain/awn262. View

16.

Adeli H, Zhou Z, Dadmehr N . Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods. 2003; 123(1):69-87. DOI: 10.1016/s0165-0270(02)00340-0. View

17.

Aghajani H, Zahedi E, Jalili M, Keikhosravi A, Vosoughi Vahdat B . Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model. IEEE J Biomed Health Inform. 2013; 17(6):1039-45. DOI: 10.1109/JBHI.2013.2253326. View

18.

Ghorbanian P, Devilbiss D, Verma A, Bernstein A, Hess T, Simon A . Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform. Ann Biomed Eng. 2013; 41(6):1243-57. DOI: 10.1007/s10439-013-0795-5. View

19.

Abasolo D, Hornero R, Gomez C, Garcia M, Lopez M . Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. Med Eng Phys. 2005; 28(4):315-22. DOI: 10.1016/j.medengphy.2005.07.004. View

20.

Toth B, File B, Boha R, Kardos Z, Hidasi Z, Gaal Z . EEG network connectivity changes in mild cognitive impairment - Preliminary results. Int J Psychophysiol. 2014; 92(1):1-7. DOI: 10.1016/j.ijpsycho.2014.02.001. View