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

  • Dipak Ghosh
  • Shukla Samanta
  • Sayantan Chakraborty
Chapter

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.

Notes

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.

References

  1. 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 28:315–322PubMedCrossRefGoogle Scholar
  2. 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 22:1250002PubMedCrossRefGoogle Scholar
  3. Alberdi A, Aztiria A, Basarab A (2016) On the early diagnosis of Alzheimer’s disease from multimodal signals: a survey. Artif Intell Med 71:1–29PubMedCrossRefGoogle Scholar
  4. Alvarez-Ramirez J, Rodriguez E, Echeverría JC (2005) Detrending fluctuation analysis based on moving average filtering. Phys A 354:199–219CrossRefGoogle Scholar
  5. Alzheimer’s Association (2017) Alzheimer’s disease facts and figures. Alzheimers Dement 13:325–373CrossRefGoogle Scholar
  6. Andreu C, de Echave J, Buela-Casal G (1998) Actividad electroencefalográfica según la teoría del caos. Psicothema 10:319–331Google Scholar
  7. 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 64:061907CrossRefGoogle Scholar
  8. 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 21:373–381PubMedPubMedCentralCrossRefGoogle Scholar
  9. 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 30:1431–1443PubMedCrossRefGoogle Scholar
  10. Baier G, Goodfellow M, Taylor PN, Wang Y, Garry DJ (2012) The importance of modeling epileptic seizure dynamics as spatiotemporal patterns. Front Physiol 3:281PubMedPubMedCentralCrossRefGoogle Scholar
  11. Baker M, Akrofi K, Schiffer R, Boyle MWO (2008) EEG patterns in mild cognitive impairment (MCI) patients. Open Neuroimaging J 2:52–55CrossRefGoogle Scholar
  12. Barnes GN, Paolicchi JM (2008) Neuropsychiatric comorbidities in childhood absence epilepsy. Nat Clin Pract Neurol 4:650–651PubMedCrossRefGoogle Scholar
  13. Bartsch R, Hennig T, Heinen A, Heinrichs S, Maass P (2005) Statistical analysis of fluctuations in the ECG morphology. Phys A 354:415–431CrossRefGoogle Scholar
  14. Bashan A, Bartsch R, Kantelhardt JW, Havlin S (2008) Comparison of detrending methods for fluctuation analysis. Phys A 387:5080–5090CrossRefGoogle Scholar
  15. Berenyi A, Belluscio M, Mao D, Buzsaki G (2012) Closed-loop control of epilepsy by transcranial electrical stimulation. Science 337:735–737PubMedPubMedCentralCrossRefGoogle Scholar
  16. 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 84:810–817PubMedPubMedCentralCrossRefGoogle Scholar
  17. Blumenfeld H (2012) Impaired consciousness in epilepsy. Lancet Neurol 11:814–826PubMedPubMedCentralCrossRefGoogle Scholar
  18. Bob P, Susta M, Glaslova K, Boutros NN (2010) Dissociative symptoms and interregional EEG cross-correlations in paranoid schizophrenia. Psychiatry Res 177:37–40PubMedCrossRefGoogle Scholar
  19. Breakspear M (2005) A unifying explanation of primary generalized seizures through non-linear brain modeling and bifurcation analysis. Cereb Cortex 16:1296–1313PubMedCrossRefGoogle Scholar
  20. Bromfield EB, Cavazos JE, Sirven JI (2006) An introduction to epilepsy. American Epilepsy Society, West Hartford, p 2006Google Scholar
  21. Carron R, Chaillet A, Filipchuk A, Pasillas-Lépine W, Hammond C (2013) Closing the loop of deep brain stimulation. Front Syst Neurosci 7:112PubMedPubMedCentralCrossRefGoogle Scholar
  22. Contreras TI (2007) Análisis Fractal de un sistema complejo: Epilepsia. Instituto Politécnico Nacional, MexicoGoogle Scholar
  23. 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 21:663–688PubMedPubMedCentralCrossRefGoogle Scholar
  24. 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 68:75–80PubMedCrossRefGoogle Scholar
  25. 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 116:506PubMedCrossRefGoogle Scholar
  26. Das A, Das P, Roy AB (2002) Applicability of Lyapunov exponent in EEG data analysis. Complex Int 9:1Google Scholar
  27. 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 21:2152–2202PubMedCrossRefGoogle Scholar
  28. 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 21:2203–2268PubMedCrossRefGoogle Scholar
  29. 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 49:668–693PubMedCrossRefGoogle Scholar
  30. Devinsky O, Vazquez B (1993) Behavioral changes associated with epilepsy. Neurol Clin 11:127–149PubMedCrossRefGoogle Scholar
  31. Dikanev T, Smirnov D, Wennberg R, Perez Velazquez LJ, Bezruchko BB (2005) EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis. Clin Neurophysiol 116:1796PubMedCrossRefGoogle Scholar
  32. Dojnow P (2007) Comptes rendus de l’Acade’mie bulgare des. Science 60:607Google Scholar
  33. Drożdż S, Kwapien J, Oswiecimka P, Rak R (2009) Quantitative features of multifractal subtleties in time series. Europhys Lett 88:60003CrossRefGoogle Scholar
  34. Dutta S (2010a) EEG pattern of normal and epileptic rats: monofractal or multifractal? Fractals 18:425–431CrossRefGoogle Scholar
  35. Dutta S (2010b) Multifractal properties of ECG patterns of patients suffering from congestive heart failure. J Stat Mech: Theory Exp:P12021CrossRefGoogle Scholar
  36. 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 396:155–163CrossRefGoogle Scholar
  37. 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 2010Google Scholar
  38. 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 32:1163–1180PubMedCrossRefGoogle Scholar
  39. 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):46Google Scholar
  40. 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 34:430–442PubMedPubMedCentralCrossRefGoogle Scholar
  41. Falconer K (2003) Fractal geometry: mathematical foundations and applications, 2nd edn. Wiley, ChichesterCrossRefGoogle Scholar
  42. Fan D, Liu S, Wang Q (2016) Stimulus-induced epileptic spike-wave discharges in thalamocortical model with disinhibition. Sci Rep 6:37703PubMedPubMedCentralCrossRefGoogle Scholar
  43. Fell J, Kaplan A, Darkhovsky B, Roschke J (2000) EEG analysis with non-linear deterministic and stochastic methods: a combined strategy. Acta Neurobiol Exp 60:87–108Google Scholar
  44. 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 24:182–189PubMedCrossRefGoogle Scholar
  45. 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 913:190–195CrossRefGoogle Scholar
  46. Freeman W, Vitiello G (2006) Non-linear brain dynamics as macroscopic manifestation of underlying many-body field dynamics. Phys Life Rev 3:93–118CrossRefGoogle Scholar
  47. Fruend’s JE (2003) Chapter 15: Design and analysis of experiments. In: Mathematical statistics with application. Pearson, BostonGoogle Scholar
  48. Fu K, Qu JF, Chai Y, Zou T (2015) Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomed Signal Process Control 18:179–185CrossRefGoogle Scholar
  49. Gasser US, Rousson V, Hentschel F, Sattel H, Gasser T (2008) Alzheimer disease versus mixed dementias: an EEG perspective. Clin Neurophysiol 119:2255–2259PubMedCrossRefGoogle Scholar
  50. Gautama T, Mandic DP, Van Hulle M (2003) Indications of non-linear structures in brain electrical activity. Phys Rev E 67:046204CrossRefGoogle Scholar
  51. Ghosh D, Dutta S, Chakraborty S (2014) Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status. Chaos, Solitons Fractals 67:1–10CrossRefGoogle Scholar
  52. Gómez C, Hornero R (2010) Entropy and complexity analyses in Alzheimer’s disease: an MEG study. Open Biomed Eng J 4:223–235PubMedPubMedCentralCrossRefGoogle Scholar
  53. 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 37:586–594PubMedCrossRefGoogle Scholar
  54. 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 31:306–313PubMedCrossRefGoogle Scholar
  55. 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 6188–6191Google Scholar
  56. Goodfellow M, Schindler K, Baier G (2011) Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model. NeuroImage 55:920–932PubMedCrossRefGoogle Scholar
  57. Guler NF, Ubeyli ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29:506–514CrossRefGoogle Scholar
  58. 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 38:10425–10436CrossRefGoogle Scholar
  59. 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 HabanaGoogle Scholar
  60. Haghighi HS, Markazi AHD (2017) A new description of epileptic seizures based on dynamic analysis of a thalamocortical model. Sci Rep 7:13615CrossRefGoogle Scholar
  61. Harris B, Gath I, Rondouin G, Feuerstein C (1994) On time delay estimation of epileptic EEG. IEEE Trans Biomed Eng 41:820–829PubMedCrossRefGoogle Scholar
  62. 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 137:247–259CrossRefGoogle Scholar
  63. 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 2007Google Scholar
  64. 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 367:317–336CrossRefGoogle Scholar
  65. 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:e0193607PubMedPubMedCentralCrossRefGoogle Scholar
  66. 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 24:070502CrossRefGoogle Scholar
  67. Ivanov PC, Amaral LAN, Goldberger AL, Havlin S, Rosenblum MG et al (1999) Multifractality in human heartbeat dynamics. Nature 399:461–465PubMedPubMedCentralCrossRefGoogle Scholar
  68. Ivanov PC, Amaral LAN, Goldberger AL, Havlin S, Rosenblum MG et al (2001) From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 11:641–652PubMedPubMedCentralCrossRefGoogle Scholar
  69. 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 79:041920CrossRefGoogle Scholar
  70. 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 127–130Google Scholar
  71. 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 110:1159–1167CrossRefGoogle Scholar
  72. Jeong J, Gore JC, Peterson BS (2001) Mutual information analysis of the EEG in patients with Alzheimer’s disease. Clin Neurophysiol 112:827–835PubMedCrossRefGoogle Scholar
  73. Jeongn J (2002) Non-linear dynamics of EEG in Alzheimer’s disease. Drug Dev Res 56:57–66CrossRefGoogle Scholar
  74. Jerger KK, Netoff TI, Francis JT, Sauer T, Pecora L, Weinstein SL et al (2001) Early seizure detection. J Clin Neurophysiol 18:259–268PubMedCrossRefGoogle Scholar
  75. Jing ZL, Lu DZ, Guang HY (2003) Fractal dimension in human cerebellum measured by magnetic resonance imaging. Biophys J 85:4041–4046CrossRefGoogle Scholar
  76. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C (2014) On the nature of seizure dynamics. Brain 137:2210–2230PubMedPubMedCentralCrossRefGoogle Scholar
  77. Jun W, Da-Qing Z (2012) Detrended cross-correlation analysis of electroencephalogram. Chin Phys B 21:028703CrossRefGoogle Scholar
  78. Kamath C (2015) Analysis of EEG signals in epileptic patients and control subjects using non-linear deterministic chaotic and fractal quantifiers. Science Postprint 1:e00042Google Scholar
  79. Kannathal N, Acharya R, Alias F, Tiboleng T, Puthusserypady K (2004) Non-linear analysis of EEG signals at different mental states. Biomed Eng Online 3:7CrossRefGoogle Scholar
  80. Kannathal N, Rajendra Acharya U, Lim CM, Sadasivan PK (2005) Characterization of EEG—a comparative study. Comput Methods Prog Biomed 80:17–23CrossRefGoogle Scholar
  81. 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 2011Google Scholar
  82. Kim JW, Roberts JA, Robinson PA (2009) Dynamics of epileptic seizures: evolution, spreading, and suppression. J Theor Biol 257:527–532PubMedCrossRefGoogle Scholar
  83. 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 17:61–69PubMedCrossRefGoogle Scholar
  84. Kulish V, Sourin A, Sourina O (2006) Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput Biol Med 36:291–302PubMedCrossRefGoogle Scholar
  85. 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 116:10–25CrossRefGoogle Scholar
  86. 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 172:131–136PubMedCrossRefGoogle Scholar
  87. Li Y, Wei HL, Billings SA, Liao XF (2012) Time-varying linear and non-linear parametric model for granger causality analysis. Phys Rev E 85:041906CrossRefGoogle Scholar
  88. Lin P-J, Neumann PJ (2013) The economics of mild cognitive impairment. Alzheimers Dement 9:58–62PubMedCrossRefGoogle Scholar
  89. Lin CY, Chen WL, Ker MD (2013) Implantable stimulator for epileptic seizure suppression with loading impedance adaptability. IEEE Trans Biomed Circuits Syst 7:196–203PubMedCrossRefGoogle Scholar
  90. Litt B, Echauz J (2002) Comparison of three non-linear seizure prediction methods by means of the seizure prediction characteristic. Lancet Neurol 1:22PubMedCrossRefGoogle Scholar
  91. 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 2:9–18PubMedCrossRefGoogle Scholar
  92. 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 50:540–548PubMedCrossRefGoogle Scholar
  93. 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 44:72–83PubMedCrossRefGoogle Scholar
  94. 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 15:127–136CrossRefGoogle Scholar
  95. 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 390:2480–2490CrossRefGoogle Scholar
  96. 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 101:16369PubMedPubMedCentralCrossRefGoogle Scholar
  97. 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 81:031101CrossRefGoogle Scholar
  98. 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 194:357CrossRefGoogle Scholar
  99. Mann K, Backer P, Roschke J (1993) Dynamical properties of the sleep EEG in different frequency bands. Int J Neurosci 73:161–169PubMedCrossRefGoogle Scholar
  100. Mars NJ, Lopes da Silva FH (1983) Propagation of seizure activity in kindled dogs. Electroencephalogr Clin Neurophysiol 56:194–209CrossRefGoogle Scholar
  101. 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 79:21911CrossRefGoogle Scholar
  102. 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 001407–001411Google Scholar
  103. Milanowski P, Suffczynski P (2016) Seizures start without common signatures of critical transition. Int J Neural Syst 26:1650053PubMedCrossRefGoogle Scholar
  104. 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, 2009Google Scholar
  105. Mormann F, Kreuz T, Andrzejak RG, David P, Lehnertz K, Elger CE (2003) Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res 53:173PubMedCrossRefGoogle Scholar
  106. Mormann F, Andrzejak RG, Elger CE, Lehnertz K (2007) Seizure prediction: the long and winding road. Brain 130:314–333PubMedCrossRefGoogle Scholar
  107. Murphy JV, Patil A (2003) Stimulation of the nervous system for the management of seizures. CNS Drugs 17:101–115PubMedCrossRefGoogle Scholar
  108. 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 42:1446–1450PubMedGoogle Scholar
  109. Navarro V, Martinerie J, Quyen MLV, Clemenceau S, Adam C et al (2002) Seizure anticipation in human neocortical partial epilepsy. Brain 125:640PubMedCrossRefGoogle Scholar
  110. Ni H, Zhou L, Ning X, Wang L (2016) Exploring multifractal-based features for mild Alzheimer’s disease classification. Magn Reson Med 76:259–269PubMedCrossRefGoogle Scholar
  111. Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26:55–60PubMedCrossRefGoogle Scholar
  112. Nikulin V, Brismar T (2005) Long-range temporal correlations in electroencephalographic oscillations: relation to topography, frequency band, age and gender. Neuroscience 130:549–558PubMedCrossRefGoogle Scholar
  113. Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36:2027–2036CrossRefGoogle Scholar
  114. Osorio I, Frei MG (2007) Hurst parameter estimation for epileptic seizure detection. Commun Inf Syst 7:167–176Google Scholar
  115. Ouyang GX, Li XL, Li Y, Guan XP (2007) Application of wavelet-based similarity analysis to epileptic seizures prediction. Comput Biol Med 37:430–437PubMedCrossRefGoogle Scholar
  116. 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 125:1069–1076PubMedCrossRefGoogle Scholar
  117. 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 62:112–117PubMedCrossRefGoogle Scholar
  118. 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 SeptemberGoogle Scholar
  119. 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 187:1017–1026Google Scholar
  120. 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 11:26010CrossRefGoogle Scholar
  121. Quyen LVM, Martinerie J, Navarro V, Boon P, Have MD et al (2001) Anticipation of epileptic seizures from standard EEG recordings. Lancet 357:183–188CrossRefGoogle Scholar
  122. 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 1:39–41PubMedPubMedCentralCrossRefGoogle Scholar
  123. 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 245:27–35PubMedCrossRefGoogle Scholar
  124. 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 20:35CrossRefGoogle Scholar
  125. 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 112–133Google Scholar
  126. 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 24:710–719PubMedCrossRefGoogle Scholar
  127. Sankari Z, Adeli H, Adeli A (2012) Wavelet coherence model for diagnosis of Alzheimer’s disease. Clin EEG Neurosci 43:268–278PubMedCrossRefGoogle Scholar
  128. 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 16:013108PubMedCrossRefGoogle Scholar
  129. 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 9:056008PubMedCrossRefGoogle Scholar
  130. Stam CJ (2005) Non-linear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301PubMedCrossRefGoogle Scholar
  131. Stam CJ, van Woerkom TCAM, Pritchard WS (1996) EEG measures to characterize EEG changes during mental activity. Electroencephalogr Clin Neurophysiol 99:214–224PubMedCrossRefGoogle Scholar
  132. Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084–1093CrossRefGoogle Scholar
  133. Suffczynski P, Kalitzin S, Lopes Da Silva FH (2004) Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network. Neuroscience 126:467–484PubMedCrossRefGoogle Scholar
  134. Susmakova K (2004) Human sleep and sleep EEG. Meas Sci Rev 4:59–74Google Scholar
  135. Taylor PN, Baier G (2011) A spatially extended model for macroscopic spike-wave discharges. J Comput Neurosci 31:679–684PubMedCrossRefGoogle Scholar
  136. 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:e114316PubMedPubMedCentralCrossRefGoogle Scholar
  137. Thakor NV, Tong S (2004) Advances in quantitative electroencephalogram analysis methods. Annu Rev Biomed Eng 6:453–495PubMedCrossRefGoogle Scholar
  138. 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 391:1179–1194CrossRefGoogle Scholar
  139. Torres NV (1991) Caos en Sistemas Biológicos. Biochemistry and Molecular Biology Department, Santa Cruz de TenerifeGoogle Scholar
  140. Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510PubMedCentralCrossRefPubMedGoogle Scholar
  141. Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans Inf Technol Biomed 13:703–710PubMedCrossRefGoogle Scholar
  142. Uthayakumar R, Easwaramoorthy D (2013) Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals 21:1350011CrossRefGoogle Scholar
  143. Vingerhoets G (2006) Cognitive effects of seizures. Seizure 15:221–226PubMedCrossRefGoogle Scholar
  144. 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 6:27344PubMedPubMedCentralCrossRefGoogle Scholar
  145. Watters PA (2000) Time-invariant EEG power laws. Int J Syst Sci 31:819–826CrossRefGoogle Scholar
  146. Watters PA, Martin F (2004) A method for estimating long-range power law correlations from the electroencephalogram. Biol Psychol 66:79–89PubMedCrossRefGoogle Scholar
  147. 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 1–5Google Scholar
  148. 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 704–707Google Scholar
  149. 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 22:343–356PubMedPubMedCentralGoogle Scholar
  150. 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 4:318–325PubMedCrossRefGoogle Scholar
  151. 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 117:2399–2413PubMedCrossRefGoogle Scholar
  152. Woon WL, Cichocki A, Vialatte F, Musha T (2007) Techniques for early detection of Alzheimer’s disease using spontaneous EEG recordings. Physiol Meas 28:335–347PubMedCrossRefGoogle Scholar
  153. 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 390:4057–4072CrossRefGoogle Scholar
  154. Zhang Y, Zhou W, Yuan S (2015) Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial EEG. Int J Neural Syst 25:1550020PubMedCrossRefGoogle Scholar
  155. 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), 2013Google Scholar
  156. Zhou WX (2008) Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E 77:066211CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dipak Ghosh
    • 1
  • Shukla Samanta
    • 2
  • Sayantan Chakraborty
    • 3
  1. 1.Department of PhysicsSir C V Raman Centre for Physics and Music, Jadavpur UniversityKolkataIndia
  2. 2.Department for PhysicsSeacom Engineering CollegeHowrahIndia
  3. 3.Electrical and Electronics EngineeringICFAI UniversityAgartalaIndia

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