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Multifractal Study of EEG Signal of Subjects with Epilepsy and Alzheimer’s

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Multifractals and Chronic Diseases of the Central Nervous System

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Abstract

Epilepsy has been identified as a common disorder of central nervous system affecting a huge size of population. This chapter presents a new approach for studying EEG patterns of the human brain in different physiological and pathological states in epileptic patients and normal people with the help of multifractal detrended fluctuation analysis. The chapter also includes a brief discussion about Alzheimer’s diseases and its diagnosis techniques. Further multifractal cross-correlation study was also applied on EEG data taken from patients in both stages – during seizure and in seizure-free interval. The chapter ends with a discussion of how this method can be used as a possible biomarker of epilepsy.

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References

  • Abásolo D, Hornero R, Gómez C, García M, López 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 

  • Acharya UR, Sree SV, Alvin APC, Yanti R, Suri JS (2012) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22:1250002

    Article  PubMed  Google Scholar 

  • Alberdi A, Aztiria A, Basarab A (2016) On the early diagnosis of Alzheimer’s disease from multimodal signals: a survey. Artif Intell Med 71:1–29

    Article  PubMed  Google Scholar 

  • Alvarez-Ramirez J, Rodriguez E, Echeverría JC (2005) Detrending fluctuation analysis based on moving average filtering. Phys A 354:199–219

    Article  Google Scholar 

  • Alzheimer’s Association (2017) Alzheimer’s disease facts and figures. Alzheimers Dement 13:325–373

    Article  Google Scholar 

  • Andreu C, de Echave J, Buela-Casal G (1998) Actividad electroencefalográfica según la teoría del caos. Psicothema 10:319–331

    Google Scholar 

  • Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P et al (2001) Indications of non-linear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64:061907

    Article  CAS  Google Scholar 

  • Austin J, Perkins SM, Johnson C, Fastenau P, Byars A et al (2011) Behaviour problems in children at time of first recognized seizure and changes over the following 3years. Epilepsy Behav 21:373–381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Babiloni C, Pievani M, Vecchio F, Geroldi C, Eusebi F et al (2009) White-matter lesions along the cholinergic tracts are related to cortical sources of EEG rhythms in amnesic mild cognitive impairment. Hum Brain Mapp 30:1431–1443

    Article  PubMed  Google Scholar 

  • Baier G, Goodfellow M, Taylor PN, Wang Y, Garry DJ (2012) The importance of modeling epileptic seizure dynamics as spatiotemporal patterns. Front Physiol 3:281

    Article  PubMed  PubMed Central  Google Scholar 

  • Baker M, Akrofi K, Schiffer R, Boyle MWO (2008) EEG patterns in mild cognitive impairment (MCI) patients. Open Neuroimaging J 2:52–55

    Article  Google Scholar 

  • Barnes GN, Paolicchi JM (2008) Neuropsychiatric comorbidities in childhood absence epilepsy. Nat Clin Pract Neurol 4:650–651

    Article  PubMed  Google Scholar 

  • Bartsch R, Hennig T, Heinen A, Heinrichs S, Maass P (2005) Statistical analysis of fluctuations in the ECG morphology. Phys A 354:415–431

    Article  Google Scholar 

  • Bashan A, Bartsch R, Kantelhardt JW, Havlin S (2008) Comparison of detrending methods for fluctuation analysis. Phys A 387:5080–5090

    Article  Google Scholar 

  • Berenyi A, Belluscio M, Mao D, Buzsaki G (2012) Closed-loop control of epilepsy by transcranial electrical stimulation. Science 337:735–737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bergey GK, Morrell MJ, Mizrahi EM, Goldman A, King-Stephens D et al (2015) Long-term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology 84:810–817

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Blumenfeld H (2012) Impaired consciousness in epilepsy. Lancet Neurol 11:814–826

    Article  PubMed  PubMed Central  Google Scholar 

  • Bob P, Susta M, Glaslova K, Boutros NN (2010) Dissociative symptoms and interregional EEG cross-correlations in paranoid schizophrenia. Psychiatry Res 177:37–40

    Article  PubMed  Google Scholar 

  • Breakspear M (2005) A unifying explanation of primary generalized seizures through non-linear brain modeling and bifurcation analysis. Cereb Cortex 16:1296–1313

    Article  PubMed  Google Scholar 

  • Bromfield EB, Cavazos JE, Sirven JI (2006) An introduction to epilepsy. American Epilepsy Society, West Hartford, p 2006

    Google Scholar 

  • Carron R, Chaillet A, Filipchuk A, Pasillas-Lépine W, Hammond C (2013) Closing the loop of deep brain stimulation. Front Syst Neurosci 7:112

    Article  PubMed  PubMed Central  Google Scholar 

  • Contreras TI (2007) Análisis Fractal de un sistema complejo: Epilepsia. Instituto Politécnico Nacional, Mexico

    Google Scholar 

  • Curia G, Lucchi C, Vinet J, Gualtieri F, Marinelli C et al (2014) Pathophysiogenesis of mesial temporal lobe epilepsy: is prevention of damage antiepileptogenic? Curr Med Chem 21:663–688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Czigler B, CsikoÂs D, Hidasi Z, Anna Gaal Z, Csibri E et al (2008) Quantitative EEG in early Alzheimer’s disease patients – power spectrum and complexity features. Int J Psychophysiol 68:75–80

    Article  PubMed  Google Scholar 

  • D’Alessandro M, Vachtsevanos G, Esteller R, Echauz J, Cranstoun S et al (2005) A multi-feature and multi-channel univariate selection process for seizure prediction. Clin Neurophysiol 116:506

    Article  PubMed  Google Scholar 

  • Das A, Das P, Roy AB (2002) Applicability of Lyapunov exponent in EEG data analysis. Complex Int 9:1

    Google Scholar 

  • Dauwels J, Vialatte FB, Weber T, Cichocki A (2009a) Quantifying statistical interdependence by message passing on graphs, Part I: One-dimensional point processes. Neural Comput 21:2152–2202

    Article  CAS  PubMed  Google Scholar 

  • Dauwels J, Vialatte FB, Weber T, Musha T, Cichocki A (2009b) Quantifying statistical interdependence by message passing on graphs, Part II: Multi-dimensional point processes. Neural Comput 21:2203–2268

    Article  CAS  PubMed  Google Scholar 

  • Dauwels J, Vialatte FB, 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 

  • Devinsky O, Vazquez B (1993) Behavioral changes associated with epilepsy. Neurol Clin 11:127–149

    Article  CAS  PubMed  Google Scholar 

  • Dikanev T, Smirnov D, Wennberg R, Perez Velazquez LJ, Bezruchko BB (2005) EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis. Clin Neurophysiol 116:1796

    Article  CAS  PubMed  Google Scholar 

  • Dojnow P (2007) Comptes rendus de l’Acade’mie bulgare des. Science 60:607

    Google Scholar 

  • Drożdż S, Kwapien J, Oswiecimka P, Rak R (2009) Quantitative features of multifractal subtleties in time series. Europhys Lett 88:60003

    Article  CAS  Google Scholar 

  • Dutta S (2010a) EEG pattern of normal and epileptic rats: monofractal or multifractal? Fractals 18:425–431

    Article  Google Scholar 

  • Dutta S (2010b) Multifractal properties of ECG patterns of patients suffering from congestive heart failure. J Stat Mech: Theory Exp:P12021

    Article  Google Scholar 

  • Dutta S, Ghosh D, Samanta S, Dey S (2014) Multifractal parameters as an indication of different physiological and pathological states of the human brain. Phys A 396:155–163

    Article  Google Scholar 

  • Easwaramoorthy D, Uthayakumar R (2010) Analysis of EEG signals using advanced generalized fractal dimensions. In: Second international conference on computing, communication and networking technologies, 29–31 July 2010

    Google Scholar 

  • Escudero J, Sanei S, Jarchi D, AbaÂsolo D, Hornero R (2011) Regional coherence evaluation in mild cognitive impairment and Alzheimer’s disease based on adaptively extracted magnetoencephalogram rhythms. Physiol Meas 32:1163–1180

    Article  PubMed  Google Scholar 

  • Esteller R, Echauz J, Pless B, Tcheng T, Litt B (2002) Real-time simulation of a seizure detection system suitable for an implantable device. Epilepsia 43(suppl 7):46

    Google Scholar 

  • Ewers M, Sperling RA, Klunk WE, Weiner MW, Hampel H (2011) Neuroimaging markers for the prediction and early diagnosis of Alzheimer’s disease dementia. Trends Neurosci 34:430–442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Falconer K (2003) Fractal geometry: mathematical foundations and applications, 2nd edn. Wiley, Chichester

    Book  Google Scholar 

  • Fan D, Liu S, Wang Q (2016) Stimulus-induced epileptic spike-wave discharges in thalamocortical model with disinhibition. Sci Rep 6:37703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fell J, Kaplan A, Darkhovsky B, Roschke J (2000) EEG analysis with non-linear deterministic and stochastic methods: a combined strategy. Acta Neurobiol Exp 60:87–108

    CAS  Google Scholar 

  • Fernández A, Hornero R, Gómez C, Turrero A, Gil-Gregorio P, Matías-Santos J, Ortiz T (2010) Complexity analysis of spontaneous brain activity in Alzheimer disease and mild cognitive impairment: an MEG study. Alzheimer Dis Assoc Disord 24:182–189

    Article  PubMed  Google Scholar 

  • Figliola A, Serrano E, Rostas JAP, Hunter M, Rosso OA (2007) Study of EEG brain maturation signals with multifractal detrended fluctuation analysis. AIP Conf Proc 913:190–195

    Article  Google Scholar 

  • Freeman W, Vitiello G (2006) Non-linear brain dynamics as macroscopic manifestation of underlying many-body field dynamics. Phys Life Rev 3:93–118

    Article  Google Scholar 

  • Fruend’s JE (2003) Chapter 15: Design and analysis of experiments. In: Mathematical statistics with application. Pearson, Boston

    Google Scholar 

  • Fu K, Qu JF, Chai Y, Zou T (2015) Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomed Signal Process Control 18:179–185

    Article  Google Scholar 

  • Gasser US, Rousson V, Hentschel F, Sattel H, Gasser T (2008) Alzheimer disease versus mixed dementias: an EEG perspective. Clin Neurophysiol 119:2255–2259

    Article  PubMed  Google Scholar 

  • Gautama T, Mandic DP, Van Hulle M (2003) Indications of non-linear structures in brain electrical activity. Phys Rev E 67:046204

    Article  CAS  Google Scholar 

  • Ghosh D, Dutta S, Chakraborty S (2014) Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status. Chaos, Solitons Fractals 67:1–10

    Article  Google Scholar 

  • Gómez C, Hornero R (2010) Entropy and complexity analyses in Alzheimer’s disease: an MEG study. Open Biomed Eng J 4:223–235

    Article  PubMed  PubMed Central  Google Scholar 

  • Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J (2009a) Analysis of MEG background activity in Alzheimer’s disease using non-linear methods and ANFIS. Ann Biomed Eng 37:586–594

    Article  PubMed  Google Scholar 

  • Gómez C, Mediavilla A, Hornero R, Abásolo D, Fernández A (2009b) Use of the Higuchi’s fractal dimension for the analysis of MEG recordings from Alzheimer’s disease patients. Med Eng Phys 31:306–313

    Article  PubMed  Google Scholar 

  • Gómez C, Martinez-Zarzuela M, Poza J, Diaz-Pernas FJ, Fernandez A, Hornero R (2012) Synchrony analysis of spontaneous MEG activity in Alzheimer’s disease patients. In: 2012 annual international conference of the IEEE Engineering in Medicine and Biology Society 2012, pp 6188–6191

    Google Scholar 

  • Goodfellow M, Schindler K, Baier G (2011) Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model. NeuroImage 55:920–932

    Article  PubMed  Google Scholar 

  • Guler NF, Ubeyli ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29:506–514

    Article  Google Scholar 

  • Guo L, Rivero D, Dorado J, Munteanu CR, Pazos A (2011) Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst Appl 38:10425–10436

    Article  Google Scholar 

  • Gutiérrez J (2001) Detección del foco epiléptico y su ruta de propagación, Memorias II Congreso Latinoamericano de Ingeniería Biomédica. Instituto Nacional de Neurología y Neurocirugía, La Habana

    Google Scholar 

  • Haghighi HS, Markazi AHD (2017) A new description of epileptic seizures based on dynamic analysis of a thalamocortical model. Sci Rep 7:13615

    Article  CAS  Google Scholar 

  • Harris B, Gath I, Rondouin G, Feuerstein C (1994) On time delay estimation of epileptic EEG. IEEE Trans Biomed Eng 41:820–829

    Article  CAS  PubMed  Google Scholar 

  • Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-q factor wavelet transform and bootstrap aggregating. Comput Methods Prog Biomed 137:247–259

    Article  Google Scholar 

  • He A, Yang X, Yang Xi, Ning X (2007) Multifractal analysis of epilepsy in electroencephalogram. In: IEEE/ICME international conference on Complex Medical Engineering, 23–27 May 2007

    Google Scholar 

  • Hornero R, Abasolo D, Escudero J, Gómez C (2009) Non-linear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Philos Trans R Soc A Math Phys Eng Sci 367:317–336

    Article  Google Scholar 

  • Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel V, Mariani J, Kinugawa K (2018) Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework. PLoS One 13:e0193607

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Huang-Jing N, Lu-Ping Z, Peng Z, Xiao-Lin H, Hong-Xing L, Xin-Bao N (2015) Multifractal analysis of white matter structural changes on 3D magnetic resonance imaging between normal aging and early Alzheimer’s disease. Chin Phys B 24:070502

    Article  CAS  Google Scholar 

  • Ivanov PC, Amaral LAN, Goldberger AL, Havlin S, Rosenblum MG et al (1999) Multifractality in human heartbeat dynamics. Nature 399:461–465

    Article  CAS  PubMed  Google Scholar 

  • Ivanov PC, Amaral LAN, Goldberger AL, Havlin S, Rosenblum MG et al (2001) From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 11:641–652

    Article  PubMed  Google Scholar 

  • Ivanov p C, Ma QDY, Bartsch R, Hausdorff JM, Amaral LAN et al (2009) Levels of complexity in scale-invariant neural signals. Phys Rev E 79:041920

    Article  CAS  Google Scholar 

  • Janjarasjit S, Loparo KA (2009) Wavelet-based fractal analysis of the epileptic EEG signal. In: International symposium on intelligent signal processing and communication systems (ISPACS 2009), 7–9 December, pp 127–130

    Google Scholar 

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

    Article  CAS  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 

  • Jeongn J (2002) Non-linear dynamics of EEG in Alzheimer’s disease. Drug Dev Res 56:57–66

    Article  CAS  Google Scholar 

  • Jerger KK, Netoff TI, Francis JT, Sauer T, Pecora L, Weinstein SL et al (2001) Early seizure detection. J Clin Neurophysiol 18:259–268

    Article  CAS  PubMed  Google Scholar 

  • Jing ZL, Lu DZ, Guang HY (2003) Fractal dimension in human cerebellum measured by magnetic resonance imaging. Biophys J 85:4041–4046

    Article  Google Scholar 

  • Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C (2014) On the nature of seizure dynamics. Brain 137:2210–2230

    Article  PubMed  PubMed Central  Google Scholar 

  • Jun W, Da-Qing Z (2012) Detrended cross-correlation analysis of electroencephalogram. Chin Phys B 21:028703

    Article  Google Scholar 

  • Kamath C (2015) Analysis of EEG signals in epileptic patients and control subjects using non-linear deterministic chaotic and fractal quantifiers. Science Postprint 1:e00042

    Google Scholar 

  • Kannathal N, Acharya R, Alias F, Tiboleng T, Puthusserypady K (2004) Non-linear analysis of EEG signals at different mental states. Biomed Eng Online 3:7

    Article  Google Scholar 

  • Kannathal N, Rajendra Acharya U, Lim CM, Sadasivan PK (2005) Characterization of EEG—a comparative study. Comput Methods Prog Biomed 80:17–23

    Article  CAS  Google Scholar 

  • Ker MD, Chen WL, Lin CY (2011) Adaptable stimulus driver for epileptic seizure suppression. In IEEE international conference on IC design & technology, 2–4 May 2011

    Google Scholar 

  • Kim JW, Roberts JA, Robinson PA (2009) Dynamics of epileptic seizures: evolution, spreading, and suppression. J Theor Biol 257:527–532

    Article  CAS  PubMed  Google Scholar 

  • Kramer MA, Chang FL, Cohen ME, Hudson D, Szeri AJ (2007) Synchronization measures of the scalp EEG can discriminate healthy from Alzheimer’s subjects. Int J Neural Syst 17:61–69

    Article  PubMed  Google Scholar 

  • Kulish V, Sourin A, Sourina O (2006) Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput Biol Med 36:291–302

    Article  PubMed  Google Scholar 

  • Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and euclidean distance. Comput Methods Prog Biomed 116:10–25

    Article  Google Scholar 

  • Li Y, Qiu J, Yang Z, Johns EJ, Zhang T (2008) Long-range correlation of renal sympathetic nerve activity in both conscious and anesthetized rats. J Neurosci Methods 172:131–136

    Article  PubMed  Google Scholar 

  • Li Y, Wei HL, Billings SA, Liao XF (2012) Time-varying linear and non-linear parametric model for granger causality analysis. Phys Rev E 85:041906

    Article  CAS  Google Scholar 

  • Lin P-J, Neumann PJ (2013) The economics of mild cognitive impairment. Alzheimers Dement 9:58–62

    Article  PubMed  Google Scholar 

  • Lin CY, Chen WL, Ker MD (2013) Implantable stimulator for epileptic seizure suppression with loading impedance adaptability. IEEE Trans Biomed Circuits Syst 7:196–203

    Article  PubMed  Google Scholar 

  • Litt B, Echauz J (2002) Comparison of three non-linear seizure prediction methods by means of the seizure prediction characteristic. Lancet Neurol 1:22

    Article  PubMed  Google Scholar 

  • Lopes da Silva FH, Pijn JP, Boeijinga P (1989) Interdependence of EEG signals: linear vs. non-linear associations and the significance of time delays and phase shifts. Brain Topogr 2:9–18

    Article  CAS  PubMed  Google Scholar 

  • Lopes da Silva FH, Blanes W, Kalitzin SN, Parra J, Suffczynski P et al (2003a) Dynamical diseases of brain systems: different routes to epileptic seizures. IEEE Trans Biomed Eng 50:540–548

    Article  PubMed  Google Scholar 

  • Lopes da Silva FH, Blanes W, Kalitzin SN, Parra J, Suffczynski P et al (2003b) Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity. Epilepsia 44:72–83

    Article  PubMed  Google Scholar 

  • López T, Martínez-González CL, Manjarrez J, Plascencia N, Balankin AS (2009) Fractal analysis of EEG signals in the brain of epileptic rats, with and without biocompatible implanted neuroreservoirs. Appl Mech Mater 15:127–136

    Article  CAS  Google Scholar 

  • Ludescher J, Bogachev MI, Kantelhardt JW, Schumann AY, Bunde A (2011) On spurious and corrupted multifractality: the effects of additive noise, short-term memory and periodic trends. Phys A 390:2480–2490

    Article  Google Scholar 

  • Lutz A, Greischar LL, Rawlings NB, Ricard M, Davidson RJ (2004) Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proc Natl Acad Sci U S A 101:16369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ma QDY, Bartsch RP, Bernaola-Galvan P, Yoneyama M, Ivanov PC (2010) Effect of extreme data loss on long-range correlated and anticorrelated signals quantified by detrended fluctuation analysis. Phys Rev E 81:031101

    Article  CAS  Google Scholar 

  • Maiwald T, Winterhalder M, Aschenbrenner-Scheibe R, Voss HU, Schulze-Bonhage A, Timmer J (2004) Comparison of three non-linear seizure prediction methods by means of the seizure prediction characteristic. Phys D 194:357

    Article  Google Scholar 

  • Mann K, Backer P, Roschke J (1993) Dynamical properties of the sleep EEG in different frequency bands. Int J Neurosci 73:161–169

    Article  CAS  PubMed  Google Scholar 

  • Mars NJ, Lopes da Silva FH (1983) Propagation of seizure activity in kindled dogs. Electroencephalogr Clin Neurophysiol 56:194–209

    Article  CAS  PubMed  Google Scholar 

  • Marten F, Rodrigues S, Suffczynski P, Richardson MP, Terry JR (2009) Derivation and analysis of an ordinary differential equation mean-field model for studying clinically recorded epilepsy dynamics. Phys Rev E 79:21911

    Article  CAS  Google Scholar 

  • Meghdadi AH, Kinsner W, Fazel-Rezai R (2008) Characterization of healthy and epileptic brain EEG signals by monofractal and multifractal analysis. In: Canadian conference on Electrical and Computer Engineering, June 2008, pp 001407–001411

    Google Scholar 

  • Milanowski P, Suffczynski P (2016) Seizures start without common signatures of critical transition. Int J Neural Syst 26:1650053

    Article  PubMed  Google Scholar 

  • Morales-Matamoros O, Contreras-Troya TI, Mota Hernández CI, Trueba-Ríos B (2009) Fractal analysis of epilepsy. In: Proceedings of the 53rd annual meeting of the international society for the systems sciences, 2009

    Google Scholar 

  • Mormann F, Kreuz T, Andrzejak RG, David P, Lehnertz K, Elger CE (2003) Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res 53:173

    Article  PubMed  Google Scholar 

  • Mormann F, Andrzejak RG, Elger CE, Lehnertz K (2007) Seizure prediction: the long and winding road. Brain 130:314–333

    Article  PubMed  Google Scholar 

  • Murphy JV, Patil A (2003) Stimulation of the nervous system for the management of seizures. CNS Drugs 17:101–115

    Article  CAS  PubMed  Google Scholar 

  • Nagao M, Murase K, Kikuchi T, Ikeda M, Nebu A et al (2001) Fractal analysis of cerebral blood flow distribution in Alzheimer’s disease. J Nucl Med 42:1446–1450

    CAS  PubMed  Google Scholar 

  • Navarro V, Martinerie J, Quyen MLV, Clemenceau S, Adam C et al (2002) Seizure anticipation in human neocortical partial epilepsy. Brain 125:640

    Article  PubMed  Google Scholar 

  • Ni H, Zhou L, Ning X, Wang L (2016) Exploring multifractal-based features for mild Alzheimer’s disease classification. Magn Reson Med 76:259–269

    Article  PubMed  Google Scholar 

  • Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26:55–60

    Article  PubMed  Google Scholar 

  • Nikulin V, Brismar T (2005) Long-range temporal correlations in electroencephalographic oscillations: relation to topography, frequency band, age and gender. Neuroscience 130:549–558

    Article  CAS  PubMed  Google Scholar 

  • Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36:2027–2036

    Article  Google Scholar 

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

    Google Scholar 

  • Ouyang GX, Li XL, Li Y, Guan XP (2007) Application of wavelet-based similarity analysis to epileptic seizures prediction. Comput Biol Med 37:430–437

    Article  PubMed  Google Scholar 

  • Parish L, Worrell GA, Cranstoun SD, Stead SM, Pennell P et al (2004) Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus. Neuroscience 125:1069–1076

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Peiris MTR, Jones RD, Davidson PR, Bones PJ, Myall DJ (2005) Fractal dimension of the EEG for detection of behavioural microsleeps. In: Proceedings of IEEE Engineering in medicine and biology, 27th annual conference Shanghai, China, 1–4 September

    Google Scholar 

  • Polat K, Güne S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187:1017–1026

    Google Scholar 

  • Poza J, Gómez C, García M, Corralejo R, Fernández A et al (2014) Analysis of neural dynamics in mild cognitive impairment and Alzheimer’s disease using wavelet turbulence. J Neural Eng 11:26010

    Article  Google Scholar 

  • Quyen LVM, Martinerie J, Navarro V, Boon P, Have MD et al (2001) Anticipation of epileptic seizures from standard EEG recordings. Lancet 357:183–188

    Article  Google Scholar 

  • Rizvi SA, Zenteno JFT, Crawford SL, Wu A (2013) Outpatient ambulatory EEG as an option for epilepsy surgery evaluation instead of inpatient EEG telemetry. Epilepsy Behav Case Rep 1:39–41

    Article  PubMed  PubMed Central  Google Scholar 

  • Röschke J, Fell J, Beckmann P (1995) Non-linear analysis of sleep EEG in depression: calculation of the largest Lyapunov exponent. Eur Arch Psychiatry Clin Neurosci 245:27–35

    Article  PubMed  Google Scholar 

  • Ruiz-Gómez SJ, Gomez C, Poza J, Gutiérrez-Tobal GC, Tola-Arribas MA et al (2018) Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy 20:35

    Article  PubMed Central  Google Scholar 

  • Sackellares JC, Iasemidis LD, Shiau DS, Gilmore RL, Roper SN (2002) Epilepsy—when chaos fails. In: Lehnertz K, Arnhold J, Grassberger P, Elger CE (eds) Chaos in the brain? World Scientific, Singapore, pp 112–133

    Google Scholar 

  • Salam MT, Perez Velazquez JL, Genov R (2016) Seizure suppression efficacy of closed-loop versus open-loop deep brain stimulation in a rodent model of epilepsy. IEEE Trans Neural Syst Rehabil Eng 24:710–719

    Article  PubMed  Google Scholar 

  • Sankari Z, Adeli H, Adeli A (2012) Wavelet coherence model for diagnosis of Alzheimer’s disease. Clin EEG Neurosci 43:268–278

    Article  PubMed  Google Scholar 

  • Schelter B, Winterhalder M, Maiwald T, Brandt A, Schad A et al (2006) Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos 16:013108

    Article  PubMed  Google Scholar 

  • Serletis D, Bardakjian BL, Valiante TA, Carlen PL (2012) Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics. J Neural Eng 9:056008

    Article  PubMed  Google Scholar 

  • Stam CJ (2005) Non-linear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301

    Article  CAS  PubMed  Google Scholar 

  • Stam CJ, van Woerkom TCAM, Pritchard WS (1996) EEG measures to characterize EEG changes during mental activity. Electroencephalogr Clin Neurophysiol 99:214–224

    Article  CAS  PubMed  Google Scholar 

  • Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084–1093

    Article  Google Scholar 

  • Suffczynski P, Kalitzin S, Lopes Da Silva FH (2004) Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network. Neuroscience 126:467–484

    Article  CAS  PubMed  Google Scholar 

  • Susmakova K (2004) Human sleep and sleep EEG. Meas Sci Rev 4:59–74

    Google Scholar 

  • Taylor PN, Baier G (2011) A spatially extended model for macroscopic spike-wave discharges. J Comput Neurosci 31:679–684

    Article  PubMed  Google Scholar 

  • Taylor PN, Wang Y, Goodfellow M, Dauwels J, Moeller F et al (2014) A computational study of stimulus driven epileptic seizure abatement. PLoS One 9:e114316

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Thakor NV, Tong S (2004) Advances in quantitative electroencephalogram analysis methods. Annu Rev Biomed Eng 6:453–495

    Article  CAS  PubMed  Google Scholar 

  • Timasheva Serge F, Panischev Oleg Y, Polyakov Yuriy S, Demin Sergey A, Kaplan Alexander Y (2012) Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia. Phys A 391:1179–1194

    Article  Google Scholar 

  • Torres NV (1991) Caos en Sistemas Biológicos. Biochemistry and Molecular Biology Department, Santa Cruz de Tenerife

    Google Scholar 

  • Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510

    Article  PubMed Central  Google Scholar 

  • Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans Inf Technol Biomed 13:703–710

    Article  PubMed  Google Scholar 

  • Uthayakumar R, Easwaramoorthy D (2013) Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals 21:1350011

    Article  Google Scholar 

  • Vingerhoets G (2006) Cognitive effects of seizures. Seizure 15:221–226

    Article  PubMed  Google Scholar 

  • Wang J, Niebur E, Hu J, Li X (2016) Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller. Sci Rep 6:27344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Watters PA (2000) Time-invariant EEG power laws. Int J Syst Sci 31:819–826

    Article  Google Scholar 

  • Watters PA, Martin F (2004) A method for estimating long-range power law correlations from the electroencephalogram. Biol Psychol 66:79–89

    Article  PubMed  Google Scholar 

  • Weiss B, Hegedus B, Vago Z, Roska T (2008a) Fractal spectra of intracranial electroencephalograms in different types of epilepsy. In: 19th international EURASIP conference Biosignal, pp 1–5

    Google Scholar 

  • Weiss B, Vago Z, Tetzlaff R, Roska T (2008b). Long-range dependence of longterm continuous intracranial electroencephalograms for detection and prediction of epileptic seizures. In: international symposium on non-linear theory and its applications, pp 704–707

    Google Scholar 

  • Wendling F, Hernandez A, Bellanger J, Chauvel P, Bartolomei F (2005) Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol 22:343–356

    PubMed  PubMed Central  Google Scholar 

  • Winterhalder M, Maiwald T, Voss HU, Aschenbrenner-Scheibe R, Timmer J et al (2003) The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav 4:318–325

    Article  CAS  PubMed  Google Scholar 

  • Winterhalder M, Schelter B, Maiwald T, Brandt A, Schad A et al (2006) Spatio-temporal patient-individual assessment of synchronization changes for epileptic seizure prediction. Clin Neurophysiol 117:2399–2413

    Article  PubMed  Google Scholar 

  • Woon WL, Cichocki A, Vialatte F, Musha T (2007) Techniques for early detection of Alzheimer’s disease using spontaneous EEG recordings. Physiol Meas 28:335–347

    Article  CAS  PubMed  Google Scholar 

  • Xu Y, Ma QDY, Schmitt DT, Galvan P, Ivanov PC (2011) Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals. Phys A 390:4057–4072

    Article  Google Scholar 

  • Zhang Y, Zhou W, Yuan S (2015) Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial EEG. Int J Neural Syst 25:1550020

    Article  PubMed  Google Scholar 

  • Zhao J, Dou W, Ji H, Wang J (2013) Detrended cross-correlation analysis of epilepsy electroencephalogram signals. In: Proceedings of the 2nd international conference on systems engineering and modeling (ICSEM-13), 2013

    Google Scholar 

  • Zhou WX (2008) Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E 77:066211

    Article  CAS  Google Scholar 

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Acknowledgment

The authors gratefully acknowledge Physica A and Elsevier Publishing Co. for providing the copyrights of Figs. 2.2, 2.3a, 2.3b, 2.3c, and 2.4 and Table 2.1 and Chaos, Solitons, and Fractals for Figs. 2.5a, 2.5b, 2.6a, 2.6b, 2.7a, 2.7b, and 2.8a, 2.8b and Tables 2.2 and 2.3 used in this chapter.

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Authors and Affiliations

  1. Department of Physics, Sir C V Raman Centre for Physics and Music, Jadavpur University, Kolkata, West Bengal, India

    Dipak Ghosh

  2. Department for Physics, Seacom Engineering College, Howrah, West Bengal, India

    Shukla Samanta

  3. Electrical and Electronics Engineering, ICFAI University, Agartala, Tripura, India

    Sayantan Chakraborty

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  1. Dipak Ghosh
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Ghosh, D., Samanta, S., Chakraborty, S. (2019). Multifractal Study of EEG Signal of Subjects with Epilepsy and Alzheimer’s. In: Multifractals and Chronic Diseases of the Central Nervous System. Springer, Singapore. https://doi.org/10.1007/978-981-13-3552-5_2

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