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

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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.

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References

  • Abasolo D, Hornero R, Espino P, Poza J, Sánchez CI, de la Rosa R (2005) Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clin Neurophysiol 116:1826–1834

    Article  PubMed  Google Scholar 

  • Abasolo D, Hornero R, G´omez C, Garc´ıa M, L´opez M (2006) Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med Eng Phys 28:315–322

    Article  PubMed  Google Scholar 

  • Abasolo D, Escudero J, Hornero R, Gómez C, Espino P (2008) Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46:1019–1028

    Article  CAS  PubMed  Google Scholar 

  • Abe JM, da Silva LHF, Renato A (2007) Paraconsistent artificial neural networks and Alzheimer disease A preliminary study. Dement Neuropsychol 3:241–247

    Article  Google Scholar 

  • Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123:69–87

    Article  PubMed  Google Scholar 

  • Adler G, Brassen S, Jajcevic A (2003) EEG coherence in Alzheimer’s dementia. J Neural Transm 110:1051–1058

    Article  CAS  PubMed  Google Scholar 

  • Aghajani H, Zahedi E, Jalili M, Keikhosravi A, Vahdat BV (2013) Diagnosis of early Alzheimer’s disease based on EEG source localization and a standardized realistic head model. IEEE J Biomed Health Inform 17(6):1039–1045

    Article  PubMed  Google Scholar 

  • Ahmadlou M, Hojjat A, Anahita A (2010) New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J Neural Transm 117:1099–1109

    Article  PubMed  Google Scholar 

  • Anghinah R, Afonso P, Kanda M, Lopes HF, Fernando L, Basile H et al (2011) Alzheimer’s disease qEEG Spectral analysis versus coherence. Which is the best measurement? Arq Neuropsiquiatr 69(6):871–874

    Article  PubMed  Google Scholar 

  • Azami H, Daniel A, Samantha S, Javier E (2017) Univariate and multivariate generalized multiscale entropy to characterise EEG signals in Alzheimer’s disease. Entropy 19(31):1–17

    Google Scholar 

  • Babiloni C, Raffaele F, Giuliano B, Andrea C, Gloria DF, Matilde E (2006) Fronto-parietal coupling of brain rhythms in mild cognitive impairment: a multicentric EEG study. Brain Res Bull 69:63–73

    Article  PubMed  Google Scholar 

  • Babiloni C, Raffaele F, Giuliano B, Fabrizio V, Giovanni BF, Bartolo L et al (2009) Directionality of EEG synchronization in Alzheimer’s disease subjects. Neurobiol Aging 30:93–102

    Article  PubMed  Google Scholar 

  • Balli T, Palaniappan R (2010) Classification of biological signals using linear and nonlinear features. Physiol Meas 31:1–18

    Article  Google Scholar 

  • Bayley PJ, Jeffrey JG, Ramona OH, La Jolla LRS (2005) The neuroanatomy of remote memory. Neuron 46:799–810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Besthorn C, Sattel H, Geiger-Kabisch C, Zerfass R, Förstl H (1995) Parameters of EEG dimensional complexity in Alzheimer’s disease. Electroencephalogr Clin Neurophysiol 95(2):84–89

    Article  CAS  PubMed  Google Scholar 

  • Bhattacharya BS, Cakir Y, Serap-Sengor N, Maguire L, Coyle D (2013) Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer’s Disease. Neurocomputing 115:11–22

    Article  Google Scholar 

  • Binetti G, Magni E, Padovani A, Cappa SF, Bianchetti A, Trabucchi M (1996) Executive dysfunction in early Alzheimer’s disease. J Neurol Neurosurg Psychiatry 60:91–93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E, Wink AM, van der Flier WM, Tolboom N, Adriaanse SM, Damoiseaux JS, Scheltens P, vanBerckel BNM, Barkhof F (2012) Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 33:2018–2028

    Article  PubMed  Google Scholar 

  • Brenner RP, Reynolds CF, Ulrich RF (1988) Diagnostic efficacy of computerized spectral versus visual EEG analysis in elderly normal, demented and depressed subjects. Electroencephalogr Clin Neuroph 69:110–117

    Article  CAS  Google Scholar 

  • Breslau J, Starr A, Sicotte N, Higa J, Buchsbaum MS (1989) Topographic EEG changes with normal aging and SDAT. Electroencephalogr Clin Neurophysiol 72(4):281–289

    Article  CAS  PubMed  Google Scholar 

  • Bron EE, Smits M, Papma JM, Steketee RME, Meijboom R, de Groot M, van Swieten JC, Niessen WJ, Klein S (2017) Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI. Eur Radiol 27(8):3372–3382

    Article  PubMed  Google Scholar 

  • Brunovsky M, Matousek M, Edman A, Cervena K, Krajca V (2003) Objective assessment of the degree of dementia by means of EEG. Neuropsychobiology 48:19–26

    Article  PubMed  Google Scholar 

  • Buchan RJ, Nagata K, Yokoyama E, Langman P, Yuya H, Hirata Y, Hatazawa J, Kanno I (1997) Regional correlations between the EEG and oxygen metabolism in dementia of Alzheimer’s type. Electroencephalogr Clin Neurophysiol 103:409–417

    Article  CAS  PubMed  Google Scholar 

  • Carlino E, Sigaudo M, Pollo A, Benedetti A, Mongini T, Castagna F, Vighetti S, Rocca P (2012) Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia. J Psychiatry Neurosci 37(4):259–266

    Article  PubMed  PubMed Central  Google Scholar 

  • Chan D, Fox NC, Scahill RI, Crum WR, Whitwell JL, Leschziner G, Rossor AM, Stevens JM, Cipolotti L, Rossor MN (2001) Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann Neurol l49(4):433–442

    Article  Google Scholar 

  • Chen P, Ratcliff G, Belle SH, Cauley JA, DeKosky ST, Ganguli M (2001) Patterns of cognitive decline in presymptomatic Alzheimer disease: a prospective community study. Arch Gen Psychiatry 58:853–858

    Article  CAS  PubMed  Google Scholar 

  • Cichocki A, Shishkin SL, Musha T, Leonowicz Z, Asad T, Kurachi T (2004) EEG filtering based on blind source separation (BSS) for early detection of Alzheimer’s disease. Clin Neurophysiol 116(3):729–737

    Article  Google Scholar 

  • Coben LA, Chi D, Snyder AZ, Storandt M (1990) Replication of a study of frequency analysis of the resting awake EEG in mild probabke Alzheimer’s disease. Electroencephalogr Clin Neurophysiol 75(3):148–154

    Article  CAS  PubMed  Google Scholar 

  • Condat L (2013) A direct algorithm for 1D total variation denoising. IEEE Signal Process Lett 20(11):1054–1057

    Article  Google Scholar 

  • Coronel C, Garn H, Waser M, Deistler M, Benke T, Dal-Bianco P, Ransmayr G, Seiler S, Grossegger D, Schmidt R (2017) Quantitative EEG markers of entropy and auto mutual information in relation to MMSE scores of probable Alzheimer’s disease patients. Entropy 19(130):1–14

    Google Scholar 

  • Cover TM, Thomas JA (1991) Elements of information theory. J Econ Dyn Control 20(5):819–824

    Google Scholar 

  • Czigler B, Csikós D, Hidasi Z, Gaál ZA, Csibri É, Kiss É, Salacz P, Molnár M (2008) Quantitative EEG in early Alzheimer’s disease patients—power spectrum and complexity features. Int J Psychophysiol 68:75–80

    Article  PubMed  Google Scholar 

  • Daliri MR (2012) Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images. J Med Syst 36:995–1000

    Article  PubMed  Google Scholar 

  • Dauwels J, Vialatte F, Musha T, Cichocki A (2010) A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. NeuroImage 49:668–693

    Article  CAS  PubMed  Google Scholar 

  • Dauwels J, Srinivasan K, Reddy MR, Musha T, Vialatte FB, Latchoumane C (2011) Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int J Alzheimer’s Dis 2011:1–10

    Article  Google Scholar 

  • Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM (2008) Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514–523

    Article  PubMed  Google Scholar 

  • Davide MV, Babiloni C, Binetti G, Cassetta E, Forno GD, Ferreri F et al (2004) Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin Neurophysiol 115:299–308

    Article  Google Scholar 

  • Deng B, Cai L, Li S, Wang R, Yu H, Chen Y, Wang J (2017) Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cogn Neurodyn 11:217–231

    Article  PubMed  Google Scholar 

  • Diaz HM, Córdova FM, Cañete L, Palominos F, Cifuentes F, Sánchez C, Herrera M (2015) Order and chaos in the brain: fractal time series analysis of the EEG activity during a cognitive problem solving task. Proc Comput Sci 55:1410–1419

    Article  Google Scholar 

  • Dudas RB, Berrios GE, Hodges JR (2005) The Addenbrooke’s Cognitive Examination (ACE) in the differential diagnosis of early dementias versus affective disorder. Am J Geriatr Psychiatry 13(3):218–226

    Article  PubMed  Google Scholar 

  • Duffy FH, Albert MS, McAnulty G (1984) Brain electrical activity in patients with presenile and senile dementia of the Alzheimer type. Ann Neurol 16(4):439–448

    Article  CAS  PubMed  Google Scholar 

  • Earle JB (1988) Task difficulty and EEG alpha asymmetry: an amplitude and frequency analysis. Neuropsychobiology 20:96–112

    Article  Google Scholar 

  • Escudero J, Ab ́asolo D, Hornero R, Espino P, Opez ML (2006) Physiological measurement analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol Meas 27:1091–1106

    Article  CAS  PubMed  Google Scholar 

  • Fieller EC, Hartley HO, Pearson ES (1957) Tests for rank correlation coefficients. I. Biometrika 44(3):470–481

    Article  Google Scholar 

  • Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198

    Article  CAS  PubMed  Google Scholar 

  • Fonseca LC, Maria G, Souza TA, Rodrigues PL, De Amaral AAC (2011) Alzheimer’s disease relationship between cognitive aspects and power and coherence EEG measures. ArqNeuropsiquiatr 69(6):875–881

    Google Scholar 

  • Fonseca LC, Tedrus GMAS, Carvas PN, Machado ECFA (2013) Comparison of quantitative EEG between patients with Alzheimer’s disease and those with Parkinson’s disease dementia. Clin Neurophysiol 124:1970–1974

    Article  PubMed  Google Scholar 

  • Fonseca LC, Tedrus GMAS, Rezende ALR, Giordano HF (2015) Coherence of brain electrical activity: a quality of life indicator in Alzheimer’s disease? Arq Neuropsiquiatr 73(5):396–401

    Article  PubMed  Google Scholar 

  • Gasser US, Gasser T, Ziegler P (1994) Quantitative EEG analysis in early onset Alzheimer’s disease: correlations with severity, clinical characteristics, visual EEG and CCT. Electroencephalogr Clin Neurophysiol 90(4):267–272

    Article  PubMed  Google Scholar 

  • Geng S, Zhou W, Yuan Q, Cai D, Zeng Y (2011) EEG non-linear feature extraction using correlation dimension and Hurst exponent. Neurol Res 33(9):908–912

    Article  PubMed  Google Scholar 

  • Ghorbanian P, Devilbiss DM, Verma A, Bernstein A, Hess T, Simon AJ, Ashrafiuon H (2013) Identification of resting and active state EEG features of Alzheimer’s disease using discrete wavelet transform. Ann Biomed Eng 41(6):1243–1257

    Article  PubMed  Google Scholar 

  • Ghorbanian P, Devilbiss DM, Hess T, Bernstein A, Simon AJ, Ashrafiuon H (2015) Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform. Med Biol Eng Comput 1–14

  • Gomez C, Hornero R, Abásolo D, Fernández Escudero J (2007) Analysis of the magnetoencephalograrn background activity in Alzheimer’s disease patients with auto-mutual information. Comput Methods Prog Biomed 87(3):239–247

    Article  Google Scholar 

  • Gordon EB, Sim M (1967) The E.E.G. in presenile dementia. J Neurol Neurosurg Psychiat 30(3):285–291

  • Grunwald M, Busse F, Hensel A, Riedel-Heller S, Kruggel F, Arendt T, Wolf H, Gertz HJ (2002) Theta-power differences in patients with mild cognitive impairment under rest condition and during haptic tasks. Alzheimer Dis Assoc Disord 16(1):40–48

    Article  PubMed  Google Scholar 

  • Hara J, Shankle WR, Musha T (1999) Cortical atrophy in Alzheimer’s disease unmasks electrically silent sulci and lowers EEG dipolarity. IEEE Trans Biomed Eng 46(8):905–910

    Article  CAS  PubMed  Google Scholar 

  • Hogan MJ, Swanwick GRJ, Kaiser J, Rowan M, Lawlor B (2003) Memory-related EEG power and coherence reductions in mild Alzheimer’s disease. Int J Psychophysiol 49:147–163

    Article  PubMed  Google Scholar 

  • Hornero R, Escudero J, Fern´andez A, Poza J, G´omez C (2008) Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer’s disease. IEEE Trans Biomed Eng 55(6):1658–1665

    Article  PubMed  Google Scholar 

  • Hornero R, Abasolo D, Escudero J, GóMez C C (2009) Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Phil Trans R Soc A 367:317–336

    Article  PubMed  Google Scholar 

  • Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL (1982) A new clinical scale for the staging of dementia. Br J Psychiatry 140:566–572

    Article  CAS  PubMed  Google Scholar 

  • Hurst HE (1951) Long-term storage capacity in reservoirs. Trans Am Soc Civ Eng 55:400–410

    Google Scholar 

  • Jasper HH (1958) The ten-twenty electrode system of the International Federation. Electroencephalogr Clin Neurophysiol 10:371–375

    Google Scholar 

  • Jelles B, van Birgelen JH, Slaets JPJ, Hekster REM, Jonkman EJ, Stam CK (1999) Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls. Clin Neurophysiol 110:1159–1167

    Article  CAS  PubMed  Google Scholar 

  • Jelles B, Scheltens Ph, van der Flier WM, Jonkman EJ, Lopes da Silva FH, Stam CJ (2008) Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clin Neurophysiol 119:837–841

    Article  CAS  PubMed  Google Scholar 

  • Jeong J, Kim SY (1997) Nonlinear Analysis of chaotic dynamics underlying the electroencephalogram in patients with Alzheimer’s disease. J Korean Phys Soc 30(2):320–327

    Google Scholar 

  • Jeong J, Kim SY, Han SH (1998) Non-linear dynamical analysis of the EEG in Alzheimer’s disease with optimal embedding dimension. Electroencephalogr Clin Neurophysiol 106:220–228

    Article  CAS  PubMed  Google Scholar 

  • Jeong J, Gore JC, Peterson BS (2001) Mutual information analysis of the EEG in patients with Alzheimer’s disease. Clin Neurophysiol 112:827–835

    Article  CAS  PubMed  Google Scholar 

  • Jeong DH, Kim YD, Song IU, Chung YA, Jeong J (2016) Wavelet energy and wavelet coherence as EEG biomarkers for the diagnosis of Parkinson’s disease-related Dementia and Alzheimer’s disease. Entropy 18(8):1–17

    CAS  Google Scholar 

  • Joyce CA, Gorodnitsky IF, Kutas M (2004) Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41:1–13

    Article  Google Scholar 

  • Kikuchi M, Wada Y, Takeda T, Hiroyasu O, Hashimoto T, Koshino Y (2002) EEG harmonic responses to photic stimulation in normal aging and Alzheimer’s disease: differences in interhemispheric coherence. Clin Neurophysiol 113:1045–1051

    Article  PubMed  Google Scholar 

  • Kim HT, Kim BY, Park EH, Kim JW, Hwang EW, Han SK, Cho S (2005) Computerized recognition of Alzheimer disease-EEG using genetic algorithms and neural network. Future Gener Comput Syst 21:1124–1130

    Article  Google Scholar 

  • Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169–195

    Article  CAS  PubMed  Google Scholar 

  • Kloppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer D, Fox NC, Jack CR Jr, Ashburner J, Frackowiak RSJ (2008) Automatic classification of MR scans inAlzheimer’s disease. Brain 131:681–689

    Article  PubMed  PubMed Central  Google Scholar 

  • Koenig T, Prichep L, Dierks T, Hubl D, Wahlund LO, John ER, Jelic V (2005) Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 26:165–171

    Article  CAS  PubMed  Google Scholar 

  • Labate D, Foresta FL, Morabito G, Palamara I, Morabito FC (2013) Entropic measures of EEG complexity in Alzheimer’s disease through a multivariate multiscale approach. IEEE Sens J 13(9):3284–3292

    Article  Google Scholar 

  • Latchoumane Vincent CF, Ifeachor E, Hudson N, Wimalaratna S, Jeong J (2008) Dynamical nonstationarity analysis of resting EEGs in Alzheimer’s disease. Lect Notes Comput Sci 4985:921–929

    Article  Google Scholar 

  • Lefleche G, Albert MS (1995) Executive function deficits in mild Alzheimer’s disease. Neuropsychology 9:313–320

    Article  Google Scholar 

  • Letemendia F, Pampiglione G (1958) Clinical and electroencephalographic observations in Alzheimer’s disease. J Neurol Neurosurg Psychiat 21:167–172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leuchter FA, Spar JE, Walter DO, Weiner H (1987) Electroencephalographic spectra and coherence in the diagnosis of Alzheimer’s-Type and multi-infarct dementia: a Pilot study. Arch Gen Psychiatry 44(11):993–998

    Article  CAS  PubMed  Google Scholar 

  • Liu X, Zhang C, Ji Z, Ma Y, Shang X, Zhang Q, Zheng W, Li X, Gao J, Wang R, Wang J, Yu H (2016) Multiple characteristics analysis of Alzheimer’s electroencephalogram by power spectral density and Lempel-Ziv complexity. Cogn Neurodyn 10:121–133

    Article  PubMed  Google Scholar 

  • Locatelli T, Cursi M, Liberati D, Franceschi M, Comi G (1998) EEG coherence in Alzheimers disease. Electroencephalogr clin Neurophysiol 106:229–237

    Article  CAS  PubMed  Google Scholar 

  • Loechesa MM, Garcia-Traperoa J, Gilb P, Rubia FJ (1991) Topography of mobility and complexity parameters of the EEG in Alzheimer’s disease. Biol Psychiat 30(11):1111–1121

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  • Mandelbrot B, Wallis JR (1969) Robustness of the rescaled range R/S in the measurement of noncyclic long-run statistical dependence. Water Resour Res 5:967–988

    Article  Google Scholar 

  • Maxim V, Sendur L, Fadili J, Suckling J, Gould R, Howard R, Bullmore E (2005) Fractional Gaussian noise, functional MRI and Alzheimer’s disease. Neuroimage 25:141–158

    Article  PubMed  Google Scholar 

  • McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34:939–944

    Article  CAS  PubMed  Google Scholar 

  • Melissant C, Ypma A, Frietman EEE, Stam CJ (2005) A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements. Artif Intell Med 33:209–222

    Article  PubMed  Google Scholar 

  • Min BC, Jin SH, Kang IH, Lee DH, Kang JK, Lee ST, Sakamoto K (2003) Analysis of mutual information content for EEG responses to odor stimulation for subjects classified by occupation. Chem Senses 28:741–749

    Article  PubMed  Google Scholar 

  • Mishra P, Singla SK (2013) Artifact Removal from biosignal using fixed point ICA algorithm for pre-processing in biometric recognition. Meas Sci Rev 13(1):7–11

    Article  Google Scholar 

  • Mizuno T, Takahashi T, Cho RY, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clin Neurophysiol 121(9):1438–1446

    Article  PubMed  PubMed Central  Google Scholar 

  • Montez T, Poil SS, Jones BF, Manshanden I, Verbunt JPA, van Dijk BW, Brussaard AB, van Ooyen A et al (2009) Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease. PNAS 106(5):1614–1619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morabito FC, Labate D, Foresta FL, Bramanti A, Morabito G, Palamara I (2012) Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer’s disease EEG. Entropy 14:1186–1202

    Article  Google Scholar 

  • Morabito FC, Labate D, Bramanti A, Foresta FL, Morabito G, Palamara I, Szu HH (2013) Enhanced compressibility of EEG signal in Alzheimer’s disease patients. IEEE Sens J 13(9):3255–3261

    Article  Google Scholar 

  • Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L (2001) mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol 58:397–405

    CAS  PubMed  Google Scholar 

  • Na SH, Jin SH, Kim SY, Ham BJ (2002) EEG in schizophrenic patients: mutual information analysis. Clin Neurophysiol 113:1954–1960

    Article  PubMed  Google Scholar 

  • Nasrolahzadeh M, Mohammadpoory Z, Haddadnia J (2016) A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals. Cogn Neurodyn 10(6):495–503

    Article  PubMed  PubMed Central  Google Scholar 

  • Nuwer M (1997) Assessment of digital EEG, quantitative EEG, and EEG brain mapping. Neurology 49:277–292

    Article  CAS  PubMed  Google Scholar 

  • Osorio I, Mark GF (2007) Hurst parameter estimation for epileptic seizure detection. Commun Inf Syst 7(2):167–176

    Google Scholar 

  • Park YM, Che HJ, Im CH, Jung HT, Bae SM, Lee SH (2008) Decreased EEG synchronization and its correlation with symptom severity in Alzheimer’s disease. Neurosci Res 62:112–117

    Article  PubMed  Google Scholar 

  • Perry RJ, Hodges JR (1999) Attention and executive deficits in Alzheimer’s disease. A critical review. Brain 122:383–404

    Article  PubMed  Google Scholar 

  • Petit D, Lorrain D, Gauthier S, Montplaisir J (1993) Regional spectral analysis of the REM sleep EEG in mild to moderate Alzheimer’s disease. Neurobiol Aging 14(2):141–145

    Article  CAS  PubMed  Google Scholar 

  • Pijnenburg YAL, vd Made Y, van Cappellen van Walsum AM, Knol DL, Scheltens Ph, Stam CJ (2004) EEG synchronization likelihood in mild cognitive impairment and Alzheimer’s disease during a working memory task. Clin Neurophysiol 115:1332–1339

    Article  CAS  PubMed  Google Scholar 

  • Podgorelec V (2012) Analyzing EEG signals with machine learning for diagnosing Alzheimer’s disease. Elektronika Ir Elektrotechnika 18(8):61–64

    Article  Google Scholar 

  • Pogarell O, Teipel SJ, Juckel G, Gootjes L, Möller T, Bürger K, Leinsinger G, Möller H-J, Hegerl U, Hampel H (2005) EEG coherence reflects regional corpus callosum area in Alzheimer’s disease. J Neurol Neurosurg Psychiatry 76:109–111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Prinz PN, Vitiell MV (1989) Dominant occipital (alpha) rhythm frequency in early stage Alzheimer’s disease and depression. Electroencephalogr Clin Neurophysiol 73(5):427–432

    Article  CAS  PubMed  Google Scholar 

  • Pu J, Xu H, Wang Y, Cui H, Hu Y (2016) Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study. Cogn Neurodyn 10(5):367–373

    Article  PubMed  PubMed Central  Google Scholar 

  • Pucci E, Cacchio G, Angeloni R, Belardinelli N, Nolfe G, Signorino M, Angeleri F (1998) EEG spectral analysis in Alzheimer’s disease and different degenerative dementias. Arch Gerontol Geriatr 26:283–297

    Article  CAS  PubMed  Google Scholar 

  • Pucci E, Belardinelli N, CacchioÁ G, Signorino M, Angeleri F (1999) EEG power spectrum differences in early and late onset forms of Alzheimer’s disease. Clin Neurophysiol 110:621–631

    Article  CAS  PubMed  Google Scholar 

  • Rae GA, Blume W, Lau C, Hachinski VC, Fisman M, Merskey H (1987) The electroencephalogram in alzheimer-type dementia: a sequential study correlating the electroencephalogram with psychometric and quantitative pathologic data. Arch Neurol 44(1):50–54

    Article  Google Scholar 

  • Raghavan N, Glover JR, Sheer DE (1986) A microprocessor-based system for diagnosis of cognitive dysfunction. IEEE Trans Biomed Eng 33(10):942–948

    Article  CAS  PubMed  Google Scholar 

  • Rosenberg SJ, Ryan JJ, Prifitera A (1984) Rey auditory-verbal learning test performance of patients with and without memory impairment. J Clin Psychol 40(3):785–787

    Article  CAS  PubMed  Google Scholar 

  • Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60:259–268

    Article  Google Scholar 

  • Sankari Z, Adeli H, Adeli A (2011) Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease. Clin Neurophysiol 122:897–906

    Article  PubMed  Google Scholar 

  • Selesnick IW, Graber HL, Pfeil DS, Barbour RL (2014) Simultaneous low-pass filtering and total variation denoising. IEEE Trans Signal Process 62(5):1109–1124

    Article  Google Scholar 

  • Simonsen I, Hansen A, Nes OM (1998) Determination of the Hurst exponent by use of wavelet transforms. Phys Rev E 58(3):2779–2787

    Article  CAS  Google Scholar 

  • Smits FM, Porcaro C, Cottone C, Cancelli A, Rossini PM, Tecchio F (2016) electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PLoS ONE 11(2):1–16

    Article  CAS  Google Scholar 

  • Snaedal J, Johannesson GH, GudmundssonThE Gudmundsson S, Pajdak TH, Johnsen K (2010) The use of EEG in Alzheimer’s disease, with and without scopolamine—a pilot study. Clin Neurophysiol 121:836–841

    Article  CAS  PubMed  Google Scholar 

  • Snaedal J, Johannesson GH, Gudmundsson TE, Blin NP, Emilsdottir AL, Bjorn E, Johnsen K (2012) diagnostic accuracy of statistical pattern recognition of electroencephalogram registration in evaluation of cognitive impairment and dementia. Dement Geriatr Cogn Disord 34:51–60

    Article  PubMed  Google Scholar 

  • Sperling RA, Bates JF, Chua EF, Cocchiarella AJ, Rentz DM, Rosen BR, Schacter DL, Albert MS (2003) fMRI studies of associative encoding in young and elderly controls and mild Alzheimer’s disease. Neurol Neurosurg Psychiatry 74:44–50

    Article  CAS  Google Scholar 

  • Stam CJ, Montez T, Jones BF, Rombouts SARB, van der Made Y, Pijnenburg YAL, Scheltens Ph (2005) Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clin Neurophysiol 116:708–715

    Article  CAS  PubMed  Google Scholar 

  • Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM et al (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132:213–224

    Article  CAS  PubMed  Google Scholar 

  • Stan C, Cristescu CM, Cristescu CP (2014) Computation of hurst exponent of time series using delayed (Log-) returns. Application to estimating the financial volatility. U.P.B. Sci Bull Ser A 76(3):235–244

    Google Scholar 

  • Stigsby B, Jóhannesson G, Ingvar DH (1981) Regional EEG analysis and regional cerebral blood flow in Alzheimer’s and Pick’s diseases. Electroencephalogr Clin Neurophysiol 51(5):537–547

    Article  CAS  PubMed  Google Scholar 

  • Stoub TR, Bulgakova M, Leurgans S, Bennett DA, Fleischman D, Turner D, deToledo-Morrell L (2005) MRI predictors of risk of incident Alzheimer disease A longitudinal study. Neurology 64(9):1520–1524

    Article  CAS  PubMed  Google Scholar 

  • Tóth B, File B, Boha R, Kardos Z, Hidasi Z, Gaál ZA, Csibri É, Salacz P, Stam CJ, Molnár M (2014) EEG network connectivity changes in mild cognitive impairment—Preliminary results. Int J Psychophysiol 92:1–7

    Article  PubMed  Google Scholar 

  • Tsai PH, Lin C, Tsao J, Lin PF, Wang PC, Huang NE, Lo MT (2012) Empirical mode decomposition based detrended sample entropy in electroencephalography for Alzheimer’s disease. J Neurosci Methods 210:230–237

    Article  PubMed  Google Scholar 

  • Vladana (2015) Automated nonlinear analysis of newborn electroencephalographic signals. Doctoral Thesis 1:100

  • Vorobyov S, Cichocki A (2002) Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biol Cybern 86:293–303

    Article  PubMed  Google Scholar 

  • Wada Y, Yuko N, Jiang ZY, Koshino Y, Yamaguchi N, Hashimoto T (1997) Electroencephalographic abnormalities in patients with presenile dementia of the Alzheimer type: quantitative analysis at rest and during photic stimulation. Biol Psychiatry 41:217–225

    Article  CAS  PubMed  Google Scholar 

  • Wan B, Ming D, Qi H, Xue Z, Yin Y, Zhou Z, Cheng L (2008) Linear and nonlinear quantitative EEG analysis. IEEE Eng Med Biol Mag 27:58–63

    Article  PubMed  Google Scholar 

  • Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T (2007) Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 28:967–978

    Article  PubMed  Google Scholar 

  • Wang R, Wang J, Yu H, Wei X, Yang C, Deng B (2015) Power spectral density and coherence analysis of Alzheimer’s EEG. Cogn Neurodyn 9:291–304

    Article  PubMed  Google Scholar 

  • Waser M, Deistler M, Garn H, Benke T, Dal-Bianco P, Ransmayr G (2013) EEG in the diagnostics of Alzheimer’s disease. Stat Papers 54:1095–1107

    Article  Google Scholar 

  • Waser M, Garn H, Schmidt R, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H (2016) Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers. J Neural Transm 123:297–316

    Article  PubMed  Google Scholar 

  • Woyshville MJ, Calabrese JR (1994) Quantification of occipital EEG changes in Alzheimer’s disease utilizing a new metric: the fractal dimension. Biol Psychiat 35(6):381–387

    Article  CAS  PubMed  Google Scholar 

  • Yang AC, Wang SJ, Lai KL, Tsai CF, Yang CH, Hwang JP, Lo MT, Huang NE, Peng CK, Fuh JL (2013) Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry 47:52–61

    Article  PubMed  Google Scholar 

  • Yi GS, Wang J, Deng B, Wei XL (2017) Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease. Cogn Neurodyn 11(2):147–160

    Article  PubMed  Google Scholar 

  • Yuvaraj R, Murugappan M (2016) Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients. Cogn Neurodyn 10(3):225–234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors wish to acknowledge Dr. P.S. Mathuranath for his valuable advice. This research is carried out with the funding received from Science and Engineering Research Board (DST-SERB- No. SR/FTP/ETA-102/2010), Department of Science and Technology, Government of India.

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Correspondence to Subha D. Puthankattil.

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Nimmy John, T., D. Puthankattil, S. & Menon, R. Analysis of long range dependence in the EEG signals of Alzheimer patients. Cogn Neurodyn 12, 183–199 (2018). https://doi.org/10.1007/s11571-017-9467-8

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  • DOI: https://doi.org/10.1007/s11571-017-9467-8

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