Advertisement

Introduction

  • Dipak Ghosh
  • Shukla Samanta
  • Sayantan Chakraborty
Chapter

Abstract

Disease of the central nervous system has been described in the literature as a group of neurological disorders for which the function of the brain or spinal cord is affected. This chapter outlines the general description of the diseases like epilepsy, Parkinson’s, Huntington’s, Alzheimer’s, and motor neuron diseases. Also a discussion on the diagnostic tools and the methodologies adapted is reviewed in detail.

References

  1. Abbound S, Berenfeld O, Sadeh D (1991) Simulation of high- resolution QRS complex using ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials. Circ Res 68:1751–1760CrossRefGoogle Scholar
  2. Acharya UR, Chua CK, Lim TC, Dorithy, Suri JS (2009) Automatic identification of epileptic EEG signals using nonlinear parameters. J Mech Med Biol 9:539–553CrossRefGoogle Scholar
  3. Acharya UR, Filippo Molinari S, Sree V, Chattopadhyay S, Ng KH et al (2012a) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7:401–408CrossRefGoogle Scholar
  4. Acharya UR, Sree SV, Alvin APC, Yanti R, Suri JS (2012b) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22:1250002CrossRefGoogle Scholar
  5. Addison PS (2002) The illustrated wavelet transform handbook. Institute of Physics Publishing, LondonCrossRefGoogle Scholar
  6. Afsar O, Tirnakli U, Kurths J (2016) Entropy-based complexity measures for gait data of patients with Parkinson’s disease. Chaos 26:023115PubMedCrossRefPubMedCentralGoogle Scholar
  7. Aike G, Huiming L (1994) Complexity of the brain and neural dynamics. Sci Technol Rev:4Google Scholar
  8. Alados CL, Huffman MA (2000) Fractal long-range correlations in behavioural sequences of wild chimpanzees: a non-invasive analytical tool for the evaluation of health. Ethnology 106:105–116Google Scholar
  9. Al-Angari HM, Sahakian AV (2007) Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 54:1900–1904PubMedCrossRefPubMedCentralGoogle Scholar
  10. Alessio E, Carbone A, Castelli G, Frappietro V (2002) Second-order moving average and scaling of stochastic time. Eur Phys J B 27:197–200Google Scholar
  11. Alotaiby TN, Abd El-Samie FE, Alshebeili SA, Aljibreen KH, Alkhanen E (2015) Seizure detection with common spatial pattern and support vector machines. In: 2015 International conference on Information and Communication Technology Research (ICTRC), pp 152–155Google Scholar
  12. Al-Qazzaz NK, Abdulazez IF, Ridha SA (2014a) Simulation recording of an ECG, PCG, and PPG for feature extractions. Al-Khwarizmi Eng J 10:81–91Google Scholar
  13. Al-Qazzaz N, Ali S, Ahmad S, Chellappan A, Islam K et al (2014b) Role of EEG as biomarker in the early detection and classification of dementia. Sci World J 2014:Article ID 906038CrossRefGoogle Scholar
  14. Amaral LAN, Ivanov PC, Aoyagi N, Hidaka I, Tomono S et al (2001) Behavioral-independent features of complex heartbeat dynamics. Phys Rev Lett 86:6026–6029CrossRefGoogle Scholar
  15. Anderson C, Stolz E, Shamsunder S (1998) Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Trans Biomed Eng 45:277–286PubMedCrossRefPubMedCentralGoogle Scholar
  16. Andrade OA, Nasuto S, Kyberd P, Sweeney-Reed CM, van Kanijn FR (2006) EMG signal filtering based on empirical mode decomposition. Biomed Signal Process Control 1:44–55CrossRefGoogle Scholar
  17. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P et al (2001) Indications of nonlinear 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
  18. Anh V, Yu ZG, Wanliss JA (2007) Analysis of global geomagnetic variability. Nonlinear Process Geophys 14:701–708CrossRefGoogle Scholar
  19. Arjunan SP, Kumar DK (2007) Fractal theory based non-linear analysis of SEMG. In: 3rd International conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, Australia, Dec. 3–6, pp 545–548Google Scholar
  20. Arjunan SP, Kumar DK (2010) Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors. J Neuroeng Rehabil 7:53PubMedPubMedCentralCrossRefGoogle Scholar
  21. Arneodo A, Bacry E, Graves PV, Muzy JF (1995) Characterizing long-range correlations in DNA sequences from wavelet analysis. Phys Rev Lett 74:3293–3296PubMedCrossRefPubMedCentralGoogle Scholar
  22. Arneodo A, Decoster N, Roux SG (2000) A wavelet-based method for multifractal image analysis. I. Methodology and test applications on isotropic and anisotropic random rough surfaces. Eur Phys J B 15:567–600CrossRefGoogle Scholar
  23. Arneodo A, Audit B, Decoster N, Muzy JF, Vaillant C (2002) Wavelet based multifractal formalism: applications to DNA sequences, satellite images of the cloud structure, and stock market data. In: Bunde A, Kropp J, Schellnhuber H-J (eds) The science of disaster: climate disruptions, market crashes and heart attacks. Springer, Berlin, pp 27–102Google Scholar
  24. Artemiadis P, Kyriakopoulos K (2011) A switching regime model for the EMG-based control of a robot arm. IEEE Trans Syst Man Cybern B Cybern 41:53–63PubMedCrossRefPubMedCentralGoogle Scholar
  25. Aubert AE, Beckers F, Seps B (2002) Non-linear dynamics of heart rate variability in athletes: effect of training. Comput Cardiol 29:441–444CrossRefGoogle Scholar
  26. Augustine A, Prakash RD, Xavier R, Parassery MC (2016) Review of signal processing techniques for detection of power quality events. Am J Eng Appl Sci 9:364–370CrossRefGoogle Scholar
  27. Aung YM, Al-Jumaily A (2013) Estimation of upper limb joint angle using surface EMG signal. Int J Adv Robot Syst 10:369CrossRefGoogle Scholar
  28. Ayers S (1997) The application of chaos theory to psychology. Theory Psychol 7:373–398CrossRefGoogle Scholar
  29. Babloyantz A (1989) Estimation of correlation dimensions from single and multichannel recordings – a critical view. Brain Dyn 2:122–130CrossRefGoogle Scholar
  30. Babloyantz A, Salazar JM, Nicolis C (1985) Evidence of chaotic dynamics of brain activity during the sleep cycle. Phys Lett 111A:152–156CrossRefGoogle Scholar
  31. Bacry E, Delour J, Muzy JF (2001) Multifractal random walk. Phys Rev E 64:026103CrossRefGoogle Scholar
  32. Bahar S, Kantelhardt JW, Neiman A, Rego HHA, Russell DF et al (2001) Long-range temporal anticorrelations in paddlefish electroreceptors. Europhys Lett 56:454CrossRefGoogle Scholar
  33. Barabasi AL, Vicsek T (1991) Multifractality of self-affine fractals. Phys Rev A 44:2730PubMedCrossRefPubMedCentralGoogle Scholar
  34. Barsky RB, Miron JA (1989) The seasonal cycle and the business cycle. J Polit Econ 97(3):503–534CrossRefGoogle Scholar
  35. Bartsch R, Plotnik M, Kantelhardt JW, Havlin S, Giladi N, Hausdorff JM (2007) Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease. Physica A: Statistical Mechanics and its Applications 383(2):455–465PubMedCrossRefPubMedCentralGoogle Scholar
  36. Baspinar U, Varol HS, Senyurek VY (2013) Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals. Biocybernetics and Biomedical Engineering 33(1):33–45CrossRefGoogle Scholar
  37. Bassingthwaighte J, Van Beek J, King R (1990) Fractal branchings: the basis of myocardial flow heterogeneities? Ann N Y Acad Sci 591:392–401PubMedPubMedCentralCrossRefGoogle Scholar
  38. Bassingthwaighte JB, Liebovitch LS, West BJ (1994) Fractal physiology. Oxford University Press, New York, p 364CrossRefGoogle Scholar
  39. Baumert M, Czippelova B, Ganesan A, Schmidt M, Zaunseder S, Javorka M (2014) Entropy analysis of RR and QT interval variability during orthostatic and mental stress in healthy subjects. Entropy 16:6384–6393CrossRefGoogle Scholar
  40. Behbahani S, Jafarnia Dabanloo N, Motie Nasrabadi AA, Teixeira C, Dourado A (2013) Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses. Anatol J Cardiol 13:797–803Google Scholar
  41. Behnia M, Kelly J (1991) Role of electromyography in amyotrophic lateral sclerosis. Muscle Nerve 14:1236–1241PubMedCrossRefPubMedCentralGoogle Scholar
  42. Belbasis A, Fuss FK (2018) Muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against EMG. Front Physiol 9:408PubMedPubMedCentralCrossRefGoogle Scholar
  43. Bennasar M, Hicks Y, Clinch S, Jones P, Rosser A (2016) Huntington’s disease assessment using tri axis accelerometers. Comput Sci 96:1193–1201Google Scholar
  44. Benson CC, Partha S, Lajish VL, Kumar R (2017) Fractal analysis of MRI data for the improved characterization of brain tumors. Adv Comput Sci Technol 10:1305–1315Google Scholar
  45. Berger H (1929) Über das Elektrenkephalogramm des Menschen (On the EEG in humans). Archiv fur Psychiatrie Nervenkrankheiten 87:527–570CrossRefGoogle Scholar
  46. Berntson GG, Bigger JT Jr, Eckberg DL, Grossman P, Kaufmann PG et al (1997) Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34:623–648PubMedCrossRefPubMedCentralGoogle Scholar
  47. Bhaduri A, Ghosh D (2016) Quantitative assessment of heart rate dynamics during meditation: an ECG based study with multi Fractality and visibility graph. Front Physiol 7:44PubMedPubMedCentralCrossRefGoogle Scholar
  48. Bhavaraju NC, Frei MG, Osorio I (2006) Analog seizure detection and performance evaluation. IEEE Trans Biomed Eng 53:238–245PubMedCrossRefPubMedCentralGoogle Scholar
  49. Bianchetti A, Trabucch M (2001) Clinical aspects of Alzheimer’s disease. Aging Clin Exp Res 13:221–130CrossRefGoogle Scholar
  50. Bilodeau M, Cincera M, Arsenault AB, Gravel D (1997) Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. J Electromyogr Kinesiol 7:87–96PubMedCrossRefPubMedCentralGoogle Scholar
  51. Bishop SM, Yarham SI, Navapurkar VU, Menon DK, Ercole A (2012) Multifractal analysis of hemodynamic behavior intraoperative instability and its pharmacological manipulation. Anesthesiology 117:810–821CrossRefGoogle Scholar
  52. Blesic S, Milosevic S, Stratimirovic D, Ljubisavljevic M (1999) Detrended fluctuation analysis of time series of a firing fusimotor neuron. Physica A 268:275–282CrossRefGoogle Scholar
  53. Block A, Von Bloh W, Schellnhuber HJ (1990) Efficient box-counting determination of generalized fractal dimensions. Phys Rev A 42:1869–1874PubMedCrossRefPubMedCentralGoogle Scholar
  54. Bogunovic N, Jovic A (2010) Processing and Analysis of biomedical nonlinear signals by data mining methods. In: IWSSIP 201017th international conference on Systems, Signals and Image Processing, pp 276–279Google Scholar
  55. Bronzino JD (2000) The biomedical engineering handbook. A CRC handbook published in Cooperation with IEEE Press, pp 184–185Google Scholar
  56. Brown CT, Witschey WRT (2003) The fractal geometry of ancient Maya settlement. J Archaeol Sci 30:1619–1632CrossRefGoogle Scholar
  57. Bu N, Okamoto M, Tsuji T (2009) A hybrid motion classification approach for EMG-based human-robot interfaces using Bayesian and neural networks. IEEE Trans Robot 25:502–511CrossRefGoogle Scholar
  58. Buczkowski S, Hildgen P, Cartilier L (1998) Measurements of fractal dimension by box-counting: a critical analysis of data scatter. Physica A 252:23–34CrossRefGoogle Scholar
  59. Buldyrev SV, Goldberger AL, Havlin S, Mantegna RN, Matsa ME et al (1995) Long-range correlation properties of coding and noncoding DNA sequences: GenBank analysis. Phys Rev E 51:5084CrossRefGoogle Scholar
  60. Bunde A, Havlin S, Kantelhardt JW, Penzel T, Peter JH et al (2000) Correlated and uncorrelated regions in heart-rate fluctuations during sleep. Phys Rev Lett 85:3736–3739PubMedCrossRefPubMedCentralGoogle Scholar
  61. Bylsma FW, Peyser CE, Folstein SE, Folstein MF, Ross C et al (1994) EEG power spectra in Huntington’s disease: clinical and neuropsychological correlates. Neuropsychologia 32:137–150PubMedCrossRefPubMedCentralGoogle Scholar
  62. Cashaback JG, Cluff T, Potvin JR (2013) Muscle fatigue and contraction intensity modulates the complexity of surface electromyography. J Electromyogr Kinesiol 23:78–83PubMedCrossRefPubMedCentralGoogle Scholar
  63. Castiglioni P, Lazzeroni D, Brambilla V, Coruzzi P, Faini A (2017) Multifractal multiscale dfa of cardiovascular time series: differences in complex dynamics of systolic blood pressure, diastolic blood pressure and heart rate. in Proceedings of the 2017 39th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 3477–3480, Jeju Island, South Korea, July 2017Google Scholar
  64. Chen X, Liu A, Peng H, Ward RK (2014) A preliminary study of muscular artifact cancellation in single-channel EEG. Sensors 14:18370–18389PubMedCrossRefPubMedCentralGoogle Scholar
  65. Chorage SS, Sonone AB (2017) DWT based identification of amyotrophic lateral sclerosis using surface EMG signal. Int J Res Eng Appl Manag 3:31–35Google Scholar
  66. Chowdhury RH, Reaz MBI, Ali MABM, Bakar AAA, Chellappan K et al (2013) Surface electromyography signal processing and classification techniques. Sensors 13:12431–12466PubMedCrossRefPubMedCentralGoogle Scholar
  67. Clancy EA, Liu L, Pu L, Moyer DVZ (2012) Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models. IEEE Trans Biomed Eng 59:205–212PubMedCrossRefPubMedCentralGoogle Scholar
  68. Claus JJ, Ongerboer deVisser BW, Walstra JM, Hijdra A, Verbeeten B Jr, van Gool WA (1998) Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. Arch Neurol 55:1105–1111PubMedCrossRefPubMedCentralGoogle Scholar
  69. Claus JJ, Ongerboer de Visser BW, Bour LJ et al (2000) Determinants of quantitative spectral electroencephalography in early Alzheimer’s disease: cognitive function, regional cerebral bloodflow, and computed tomography. Dement Geriatr Cogn Disord 11:81–89PubMedCrossRefPubMedCentralGoogle Scholar
  70. Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B et al (2008) Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194–201PubMedCrossRefPubMedCentralGoogle Scholar
  71. Conte E, Ware K, Marvulli R, Ianieri G, Megna M et al (2015) Chaos, fractal and recurrence quantification analysis of surface electromyography in muscular dystrophy. World J Neurosci 5:205–257CrossRefGoogle Scholar
  72. Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89:068102PubMedCrossRefPubMedCentralGoogle Scholar
  73. D’Addio G, Romano M, Maresca L, Bifulco P, Giallauria F, et al (2014) Fractal behavior of heart rate variability during ECG stress test in cardiac patients. In: 8th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2014, pp 155–156Google Scholar
  74. Danoudis M, Iansek R (2014) Gait in Huntington’s disease and the stride length-cadence relationship. BMC Neurol 14:161PubMedPubMedCentralCrossRefGoogle Scholar
  75. Darbin O, Adams E, Martino A, Naritoku L, Dees D et al (2013) Non-linear dynamics in parkinsonism. Front Neurol 4:211PubMedPubMedCentralCrossRefGoogle Scholar
  76. DeKosky ST, Marek K (2003) Looking backward to move forward: early detection of neurodegenerative disorders. Science 302(5646):830–834PubMedCrossRefPubMedCentralGoogle Scholar
  77. Devous MD Sr (2002) Functional brain imaging in the dementias: role in early detection, differential diagnosis, and longitudinal studies. Eur J Nucl Med Mol Imaging 29:1685–1696PubMedCrossRefPubMedCentralGoogle Scholar
  78. Dick OE, Nozdrachev AD (2015) Nonlinear dynamics of involuntary shaking of the human hand under motor dysfunction. Hum Physiol 41:156CrossRefGoogle Scholar
  79. Dick OE, Nozdrachev AD (2016) Features of parkinsonian and essential tremor of the human Hand1. Hum Physiol 42:271–278CrossRefGoogle Scholar
  80. Du G, Ning X (2008) Multifractal properties of Chinese stock market in Shanghai. Physica A 387:261–269CrossRefGoogle Scholar
  81. Dutta S, Ghosh D, Samanta S (2016) Non linear approach to study the dynamics of neurodegenerative diseases by multifractal Detrended cross-correlation analysis—a quantitative assessment on gait disease. Physica A 448:181–195CrossRefGoogle Scholar
  82. Eggleston KS, Olin BD, Fisher RS (2014) Ictal tachycardia: the head-heart connection. Seizure 23:496–505PubMedCrossRefPubMedCentralGoogle Scholar
  83. Eke A, Herman P, Kocsis L, Kozak LR (2002) Fractal characterization of complexity in temporal physiological signals. Physiol Meas 23:R1–R38PubMedCrossRefPubMedCentralGoogle Scholar
  84. Elgandelwar SM, Bairagi VK (2016) Analysis of EEG signals for diagnosis of Alzheimer disease. Int J Sci Eng Res 7:529–532Google Scholar
  85. Enescu B, Ito K, Struzik ZR (2004) Wavelet-based multifractal analysis of real and stimulated time series of earthquakes. Annuals of Disaster Prevention Research Institute, Kyoto University, No. 47BGoogle Scholar
  86. Farina D, Negro F (2012) Accessing the neural drive to muscle and translation to neurorehabilitation technologies. IEEE Rev Biomed Eng 5:3–14PubMedPubMedCentralCrossRefGoogle Scholar
  87. Farina D, Merletti R, Enoka RM (2004) The extraction of neural strategies from the surface EMG. J Appl Physiol 96:1486–1495PubMedCrossRefPubMedCentralGoogle Scholar
  88. Fattah SA, Iqbal MA, Jumana MA, Sayeed Ud Doulah ABM (2012) Identifying the motor neuron disease in EMG signal using time and frequency domain features with comparison. Signal Image Process Int J 3:99–113CrossRefGoogle Scholar
  89. Fattah SA, Sayeed Ud Doulah ABM, Iqbal MA, Shahnaz C, Zhu W-P, et al (2013) Identification of motor neuron disease using wavelet domain features extracted from EMG signal. In: IEEE international symposium on circuits and systems (ISCAS 2013), 19–23 May 2013. Beijing, ChinaGoogle Scholar
  90. Faure P, Korn H (2001) Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. Comptes Rendus de l’Académie des Sciences III 324:773–793Google Scholar
  91. Faust O, Acharya UR, Min L, Sputh B (2010) Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int J Neural Syst 20:159–176PubMedCrossRefPubMedCentralGoogle Scholar
  92. Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64PubMedCrossRefPubMedCentralGoogle Scholar
  93. Feder J (1988) Fractals. Plenum Press, New YorkCrossRefGoogle Scholar
  94. Fergus P, Hussain A, David Hignett D, Al-Jumeily KA-A, Hamdan H (2016) A machine learning system for automated whole-brain seizure detection. Appl Comput Inform 12:70–89CrossRefGoogle Scholar
  95. Fisher R, van Emde Boas W, Blume W, Elger C, Genton P et al (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46:470–472PubMedCrossRefPubMedCentralGoogle Scholar
  96. Flynn AC, Jelinek HF, Smith MC (2005) Heart rate variability analysis: a useful assessment tool for diabetes associated cardiac dysfunction in rural and remote areas. Aust J Rural Health 13:77–82PubMedCrossRefPubMedCentralGoogle Scholar
  97. Forsgren L, Almay BGL, Holmgren G, Wall S (1983) Epidemiology of motor neuron disease in Northern Sweden. Acta Neurol Scand 68:20–29PubMedCrossRefPubMedCentralGoogle Scholar
  98. 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
  99. Fujiwara K, Miyajima M, Yamakawa T, Abe E, Suzuki Y et al (2016) Epileptic seizure prediction based on multivariate statistical process control of heart rate variability features. IEEE Trans Biomed Eng 63:1321–1332PubMedCrossRefPubMedCentralGoogle Scholar
  100. Fukuda O, Kim J, Nakai I, Ichikawa Y (2011) EMG control of a pneumatic 5-fingered hand using a Petri net. Artificial Life and Robotics 16(1):90–93CrossRefGoogle Scholar
  101. Gabor AJ, Leach RR, Dowla FU (1996) Automated seizure detection using a self-organizing neural network. Electroencephalogr Clin Neurophysiol 99:257–266PubMedCrossRefPubMedCentralGoogle Scholar
  102. Gao J, Sultan H, Hu J, Tung WW (2010) Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. IEEE Signal Process Lett 17:237–240CrossRefGoogle Scholar
  103. Garrett D, Peterson D, Anderson C, Thaut M (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehabil Eng 11:141–144PubMedCrossRefPubMedCentralGoogle Scholar
  104. Gato S, Jayasuriya N, Roberts P (2007) Temperature and rainfall thresholds for base use urban water demand modelling. J Hydrol 337(3–4):364–376CrossRefGoogle Scholar
  105. Ge E, Leung Y (2013) Detection of crossover time scales in multifractal detrended fluctuation analysis. J Geogr Syst 15:115–147CrossRefGoogle Scholar
  106. Ghosh D, Deb A, Dutta K, Sarkar R, Dutta I et al (2004) Multifractality and multifractal specific heat in fragmentation process in 24Mg-AgBr interaction at 4.5 A GeV. Indian J Phys 78:359–362Google Scholar
  107. Ghosh DC, Chakraborty M, Das T (2013) Fractal approach to identify quantitatively Intracardiac atrial fibrillation from ECG signals. Int J Eng Res Appl 3:129–134Google Scholar
  108. Ghosh D, Dutta S, Chakraborty S, Samanta S (2017) Chaos based quantitative electro-diagnostic marker for diagnosis of myopathy, neuropathy and motor neuron disease. J Neurol Neurosci 8(S4):226Google Scholar
  109. Gierałtowski J, Żebrowski JJ, Baranowski R (2012) Multiscale multifractal analysis of heart rate variability recordings with a large number of occurrences of arrhythmia. Phys Rev E 85(2)Google Scholar
  110. Gigola S, Ortiz F, D’Attellis CE, Silva W, Kochen S (2004) Prediction of epileptic seizures using accumulated energy in a multiresolution framework. J Neurosci Methods 138:107–111PubMedCrossRefPubMedCentralGoogle Scholar
  111. Goldberger AL (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 11:1312–1314CrossRefGoogle Scholar
  112. Goldberger AL, Rigney DR, West BJ (1990) Chaos and fractals in human physiology. Sci Am 262:42–49PubMedCrossRefPubMedCentralGoogle Scholar
  113. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov PC, Peng CK et al (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A 99:2466–2472PubMedPubMedCentralCrossRefGoogle Scholar
  114. Golińska AK (2012) Detrended fluctuation analysis (DFA) in biomedical signal processing: selected examples. Stud Logic Grammar Rhetor 29(42):107–115Google Scholar
  115. Gospodinova E (2014) Graphical methods for nonlinear analysis of ECG signals. Int J Adv Res Comput Sci Softw Eng 4:40–44Google Scholar
  116. Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophoysiol 54:530–540CrossRefGoogle Scholar
  117. Grassberger P, Procaccia I (1983) Characterization of strange attractors. Phys Rev Lett 50:346–349CrossRefGoogle Scholar
  118. Gu G-F, Zhou W-X (2010) Detrending moving average algorithm for multifractals. Phys Rev E 82:11136CrossRefGoogle Scholar
  119. Gupta V, Suryanarayanan S, Reddy NP (1997) Fractal analysis of surface EMG signals from the biceps. Int J Med Inform 45:185–192PubMedPubMedCentralCrossRefGoogle Scholar
  120. Gutiérrez Gutiérrez G, López CB, Navacerrada F, Martínez AM (2012) Use of electromyography in the diagnosis of inflammatory myopathies. ReumatologÃa ClÃnica (English Edition) 8(4):195–200CrossRefGoogle Scholar
  121. Halsey TC, Jensen MH, Kadanoff LP, Procaccia I, Shraiman BI (1986) Fractal measures and their singularities: the characterization of strange sets. Phys Rev A 33:1141–1151CrossRefGoogle Scholar
  122. Hamou A, Simmons A, Bauer M, Lewden B, Wahlund LO et al (2011) Cluster analysis of MR imaging in Alzheimer‘s disease using decision tree refinement. Int J Artif Intell 6:90–99Google Scholar
  123. Han C-X, Wang J, Yi G-S, Che Y-Q (2013) Investigation of EEG abnormalities in the early stage of Parkinson’s disease. Cogn Neurodyn 7:351–359PubMedPubMedCentralCrossRefGoogle Scholar
  124. 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
  125. Hata M, Kazui H, Tanaka T, Ishii R, Canuet L et al (2015) Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer’s disease – an eLORETA study. Clin Neurophysiol 127:1269–1278PubMedCrossRefPubMedCentralGoogle Scholar
  126. Hausdorf F (1919) Dimension und ausseres Mass. Math Ann 79:157–179CrossRefGoogle Scholar
  127. Hausdorff JM (2005) Gait variability: methods, modeling and meaning. J Neuroeng Rehabil 2:19–27PubMedPubMedCentralCrossRefGoogle Scholar
  128. Hausdorff JM (2007) Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci 26:555–589PubMedPubMedCentralCrossRefGoogle Scholar
  129. Hausdorff JM (2009) Gait dynamics in parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 19:026113PubMedPubMedCentralCrossRefGoogle Scholar
  130. Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D et al (1985) Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol 88:2045–2053CrossRefGoogle Scholar
  131. Hausdorff JM, Mitchell SL, Firtion R, Peng CK, Cudkowicz ME et al (1997) Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J Appl Physiol 82:262–269PubMedCrossRefPubMedCentralGoogle Scholar
  132. He LY, Chen SP (2011a) Nonlinear bivariate dependency between price and volume relationships in agricultural commodity futures markets: a perspective from multifractal detrended cross-correlation analysis. Physica A 390:297–308CrossRefGoogle Scholar
  133. He LY, Chen SP (2011b) Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets. Chaos Solitons Fractals 44:355–361CrossRefGoogle Scholar
  134. Helkala E, Laulumaa V, Soikkeli R, Partanen J, Soininen H et al (1991) Slow-wave activity in the spectral analysis of the electroencephalogram is associated with cortical dysfunctions in patients with Alzheimer’s disease. Behav Neurosci 105:409–415PubMedCrossRefPubMedCentralGoogle Scholar
  135. Henderson G, Ifeachor E, Hudson N, Goh C, Outram N et al (2006) Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Trans Biomed Eng 53:1557–1568PubMedCrossRefPubMedCentralGoogle Scholar
  136. Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D 31:277–283CrossRefGoogle Scholar
  137. Hirata Y, Matsuda H, Nemoto K (2005) Voxel-based morphometry to discriminate early Alzheimer’s disease from controls. Neurosci Lett 382:269–274PubMedCrossRefPubMedCentralGoogle Scholar
  138. Holschneider M (1995) Wavelets : an analysis tool. Clarendon Press/Oxford University Press, Oxford/New YorkGoogle Scholar
  139. Hoon MJLD, Van der Hagen THJJ, Schoonewelle H, van Dam H (1996) Why Yule-Walker should not be used for autoregressive modeling. Ann Nucl Energy 23:1219–1228CrossRefGoogle Scholar
  140. Horvatic D, Stanley HE, Podobnik B (2011) Detrended cross-correlation analysis for non-stationary time series with periodic trends. Europhys Lett 94:18007CrossRefGoogle Scholar
  141. Hove MJ, Suzuki K, Uchitomi H, Orimo S, Miyake Y (2012) Interactive rhythmic auditory stimulation reinstates natural 1/f timing in gait of parkinson’s patients. PLoS One 7:e32600PubMedPubMedCentralCrossRefGoogle Scholar
  142. Hu X, Wang Z-z, Ren X-m (2005) Classification of surface EMG signal with fractal dimension. J Zhejiang Univ Sci 6B(8):844–848CrossRefGoogle Scholar
  143. Hug F (2011) Can muscle coordination be precisely studied by surface electromyography. J Electromyogr Kinesiol 21:1–12PubMedCrossRefPubMedCentralGoogle Scholar
  144. Huh K-H, Baik J-S, Yi W-J, Heo M-S, Lee S-S et al (2011) Fractal analysis of mandibular trabecular bone: optimal tile sizes for the tile counting method. Imaging Sci Dent 41:71–78PubMedPubMedCentralCrossRefGoogle Scholar
  145. Humeau A, Chapeau–Blondeau F, Rousseau D, Rousseau P, Trzepizur W et al (2008) Multifractality, sample entropy, and wavelet analyses for age-related changes in the peripheral cardiovascular system: preliminary results. Med Phys 35:717–723PubMedCrossRefPubMedCentralGoogle Scholar
  146. Hurst H (1951) Long term storage capacity of reservoirs. Trans Am Soc Civil Eng 116:770–799Google Scholar
  147. Hyman S, Chisholm D, Kessler R, Patel V, Whiteford HA (2006) Mental disorders in disease control priorities in developing countries. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB (eds), Disease control priorities in developing countries, pp 605–625Google Scholar
  148. Iasemidis LD, Shiau DS, Chaovalitwongse W, Sackellares JC, Pardolas PM et al (2003) Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng 50:616–627PubMedCrossRefPubMedCentralGoogle Scholar
  149. Ihlen EAF (2012) Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol 3:Article141CrossRefGoogle Scholar
  150. Inbar GF, Paiss O, Allin J, Kranz H (1986) Monitoring surface EMG spectral changes by the zero crossing rate. Med Biol Eng Comput 24:10–18PubMedCrossRefPubMedCentralGoogle Scholar
  151. Ivanov PC, Amaral LA, Goldberger AL, Halvin S, Rosenblum MG et al (1999) Multifractality in human heartbeat dynamics. Nature 399:461–465PubMedPubMedCentralCrossRefGoogle Scholar
  152. Ivanov P, Amaral LA, Goldberger S, Halvin M, Rosenblum H et al (2001) From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 11:641–652PubMedPubMedCentralCrossRefGoogle Scholar
  153. Ivanov P, Chen Z, Hu K, Stanley HE (2004) Multiscale aspects of cardiac control. Physica A 344:685–704CrossRefGoogle Scholar
  154. Ivanova K, Ausloos M, Clothiaux EE, Ackerman TP (2000) Break-up of stratus cloud structure predicted from non-Brownian motion liquid water and brightness temperature fluctuations. Europhys Lett 52:40CrossRefGoogle Scholar
  155. Izhikevich EM (2007) Dynamical systems in neuroscience. The geometry of excitability and bursting. The MIT Press, Cambridge, MA, p 441CrossRefGoogle Scholar
  156. Jankovic J (2008) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79:368–376PubMedCrossRefPubMedCentralGoogle Scholar
  157. Jankovic J, Kapadia AS (2001) Functional decline in parkinson disease. Arch Neurol 58:1611–1615PubMedCrossRefPubMedCentralGoogle Scholar
  158. Joseph P, Acharya UR, Poo CK, Chee J, Min LC et al (2004) Effect of reflexological stimulation on heart rate variability. ITBM-RBM 25:40–45CrossRefGoogle Scholar
  159. Joshi S, Shenoy PD, Vibhudendra Simha GG, Venugopal KR, Patnaik LM (2010) Classification of neuro degenerative disorders based on major risk factors employing machine learning techniques. IACSIT Int J Eng Technol 2:350–355CrossRefGoogle Scholar
  160. Jovic A, Bogunovic N (2010) Classification of biological signals based on nonlinear features. In: Melecon 2010–2010 15th IEEE Mediterranean Electrotechnical conference, pp 1340–1345Google Scholar
  161. Jun WC, Oh G, Kim S (2006) Understanding volatility correlation behavior with a magnitude cross-correlation function. Phys Rev E 73:066128CrossRefGoogle Scholar
  162. Kahn Y, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114:898–908CrossRefGoogle Scholar
  163. Kamath C (2012) Entropy-based algorithm to detect life threatening cardiac arrhythmias using raw electrocardiogram signals. Middle East J Sci Res 12:1403–1412Google Scholar
  164. Kandel ER, Squire LR (2000) Neuroscience: breaking down scientific barriers to the study of brain and mind. Science 290:1113–1120PubMedCrossRefPubMedCentralGoogle Scholar
  165. Kantelhardt JW, Berkovits R, Havlin S, Bunde A (1999) Are the phases in the Anderson model long-range correlated? Physica A 266:461–464CrossRefGoogle Scholar
  166. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Bunde A, Havlin S et al (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316:87–114CrossRefGoogle Scholar
  167. Kantelhardt JW, Rybski D, Zschiegner SA, Braun P, Koscielny-Bunde E et al (2003) Multifractality of river runoff and precipitation: comparison of fluctuation analysis and wavelet methods. Physica A 330:240–245CrossRefGoogle Scholar
  168. Kasi PK (2009) Characterization of motor unit discharge rate in patients with Amytrophic Lateral Sclerosis (ALS). Worcester Polytechnic Institute, May 2009Google Scholar
  169. Kartz M (1988) Fractals and the analysis of waveforms. Comput Biol Med 18:145–156CrossRefGoogle Scholar
  170. Kehri V, Ingle R, Awale R, Oimbe S (2017) Techniques of EMG signal analysis and classification of neuromuscular diseases. In: Iyer B, Nalbalwar S, Pawade R (eds) ICCASP/ICMMD-2016. Advances in intelligent systems research. vol 137, pp 485–491. © 2017- The authors. Published by Atlantis PressGoogle Scholar
  171. Kim HS, Eykholt R, Salas JD (1999) Nonlinear dynamics, delay times and embedding windows. Physica D 127:48–60CrossRefGoogle Scholar
  172. Kim SH, Faloutos C, Yang HJ (2013) Coercively adjusted auto regression model for forecasting in epilepsy EEG. Hindawi Publishing Corporation, Computational and mathematical methods in medicine, 2013, Article ID 545613Google Scholar
  173. Kiran PU, Abhiram N, Taran S, Bajaj V (2018) TQWT based features for classification of ALS and healthy EMG signals. Am J Comput Sci Inf Technol 6:19Google Scholar
  174. Kirchner M, Schubert P, Liebherr M, Haas CT (2014) Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson’s disease: stitching together short gait trials. PLoS One 9:e85787PubMedPubMedCentralCrossRefGoogle Scholar
  175. Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI et al (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131:681–689PubMedPubMedCentralCrossRefGoogle Scholar
  176. Korn H, Faure P (2003) Is there chaos in the brain? II. Experimental evidence and related models. C R Biol 326:787–840PubMedCrossRefPubMedCentralGoogle Scholar
  177. Koscielny-Bunde E, Bunde A, Havlin S, Roman HE, Goldreich Y et al (1998) Indication of a universal persistence law governing atmospheric variability. Phys Rev Lett 81:729CrossRefGoogle Scholar
  178. Krenz G, Linehan J, Dawson C (1992) A fractal continuum model of the pulmonary arterial tree. J Appl Physiol 72:2225–2237PubMedCrossRefPubMedCentralGoogle Scholar
  179. Krishna PM, Gadre VM, Desai UB (2003) Multifractals: from modeling to control of broadband network traffic. In: Rangarajan G, Ding M (eds) Processes with long-range correlations, Lecture notes in physics, vol 621. Springer, Berlin/Heidelberg, pp 373–392CrossRefGoogle Scholar
  180. Kumar SP, Sriraam N, Benakop PG, Jinaga BC (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37:3284–3291CrossRefGoogle Scholar
  181. Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol 283:R789–R797PubMedCrossRefPubMedCentralGoogle Scholar
  182. Lambert EH (1969) Electromyography in amyotrophic lateral sclerosis. In: Norris FH, Kurland LT (eds) Motor neuron diseases: research in amyotrophic lateral sclerosis and related disorders. Grune and Stratton, New York, pp 135–153Google Scholar
  183. Lambert EH, Mulder DW (1957) Electromyographic studies in amyotrophic lateral sclerosis. Mayo Clin Proc 32:441–446Google Scholar
  184. Lamberts RJ, Thijs RD, Laffan A, Langan Y, Sander JW (2012) Sudden unexpected death in epilepsy: people with nocturnal seizures may be at highest risk. Epilepsia 53:253–257PubMedCrossRefPubMedCentralGoogle Scholar
  185. Lehnertz K (2008) Epilepsy and nonlinear dynamics. J Biol Phys 34:253–266PubMedPubMedCentralCrossRefGoogle Scholar
  186. Leigh PN, Al-Chalabi A (2000) Recent advances in amyotrophic lateral sclerosis. Curr Opin Neurol 13:397–405PubMedCrossRefPubMedCentralGoogle Scholar
  187. Li X (2002) EEG analysis with epileptic seizures using wavelet transform. Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, Shatin, Hong Kong, 28 Nov 2002Google Scholar
  188. Li X, Ouyang G (2006) Nonlinear similarity analysis for epileptic seizures prediction. Nonlinear Anal Theory Methods Appl 64:1666–1678CrossRefGoogle Scholar
  189. Li X, Yao X (2005) Application of fuzzy similarity to prediction of epileptic seizures using EEG signals. In: Proceedings of the 2nd international conference on Fuzzy Systems and Knowledge Discovery (FSKD ’05), 3613, pp 645–652CrossRefGoogle Scholar
  190. Li S, Shi F, Pu F, Li X, Jiang T et al (2007) Hippocampal shape analysis of Alzheimer disease based on machine learning methods. Am J Neuroradiol 28:1339–1345PubMedCrossRefPubMedCentralGoogle Scholar
  191. Li S, Liu G, Lin Z (2009) Comparisons of wavelet packet, lifting wavelet and stationary wavelet transform for denoising ECG. In: 2nd IEEE international conference on Computer Science and Information Technology, ICCSIT, pp 491–494Google Scholar
  192. Li Y, Wei HL, Billings SA (2011) Identification of time-varying systems using multi-wavelet basis functions. IEEE Trans Control Syst Technol 19:656–663CrossRefGoogle Scholar
  193. Li Y, Luo ML, Li K (2016) A multi-wavelet-based time-varying model identification approach for time-frequency analysis of EEG signals. Neurocomputing 193:106–114CrossRefGoogle Scholar
  194. Libenson M (2009) Practical Approach to Electroencephalography. SaundersGoogle Scholar
  195. Lim J, Sanghera MK, Darbin O, Stewart RM, Jankovic J et al (2010) Nonlinear temporal organization of neuronal discharge in the basal ganglia of Parkinson’s disease patients. Exp Neurol 224:542–544PubMedCrossRefPubMedCentralGoogle Scholar
  196. Liu Y, Gopikrishnan P, Cizeau P, Meyer M, Peng CK et al (1999) Statistical properties of the volatility of price fluctuations. Phys Rev E 60:1390CrossRefGoogle Scholar
  197. Liu D, Pang Z, Wang Z (2009) Epileptic seizure prediction by a system of particle filter associated with a neural network. EURASIP J Adv Signal Process 2009:638534CrossRefGoogle Scholar
  198. Lopes R, Betrouni N (2009) Fractal and multifractal analysis: a review. Med Image Anal 13:634–649PubMedCrossRefPubMedCentralGoogle Scholar
  199. Malamud BD, Turcotte DL (1999) Self-affine time series: measures of weak and strong persistence. J Statist Plann Inference 80:173–196CrossRefGoogle Scholar
  200. Malarvili M, Mesbah M (2009) Newborn seizure detection based on heart rate variability. IEEE Trans Biomed Eng 56:2594–2603PubMedCrossRefPubMedCentralGoogle Scholar
  201. Mallat S (2002) A wavelet tour of signal processing, 3rd edn. Amsterdam, ElsevierGoogle Scholar
  202. Mandelbrot B (1967) Hong long is the coast of Britain? Statistical self-similarity and fractional dimension. Science 156(3775):636–638PubMedCrossRefPubMedCentralGoogle Scholar
  203. Mandelbrot B (1977) Fractals: form, chance, and dimension. W. H. Freeman and Company, San Francisco, p 365Google Scholar
  204. Mandelbrot B (1985) Self-affine fractals and the fractal dimension. Phys Scr 32:257–260CrossRefGoogle Scholar
  205. Mandelbrot BB (1995) Negative dimensions and Holders, multifractals and their Holder spectra, and the role of lateral preasymptotics in science. J Fourier Anal Appl Kahane special issue 409–432Google Scholar
  206. Mantegna RN, Stanley HE (2000) An introduction to econophysics. Cambridge University Press, CambridgeGoogle Scholar
  207. Marri K, Swaminathan R (2015) Identification of onset of fatigue in biceps Brachii muscles using surface EMG and multifractal DMA Algorithm. Biomed Sci Instrum 51:107–114Google Scholar
  208. Marri K, Swaminathan R (2016) Analysis of biceps Brachii muscles in dynamic contraction using sEMG signals and multifractal DMA algorithm. Int J Signal Process Syst 4:79–85Google Scholar
  209. Marri K, Jose J, Karthick PA, Ramakrishnan S (2014) Analysis of fatigue conditions in triceps brachii muscle using sEMG signals and spectral correlation density function. In: International conference on Informatics, Electronics and Vision (ICIEV), Dhaka, May 23–24, pp 1–4Google Scholar
  210. Marsden CD (1982) The mysterious motor function of the basal ganglia: the Robert Wartenberg lecture. Neurology 32:514–539PubMedCrossRefPubMedCentralGoogle Scholar
  211. Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A (2008) Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 25:119–131PubMedCrossRefPubMedCentralGoogle Scholar
  212. Meigal AY, Rissanen SM, Tarvainen MP, Georgiadis SD, Karjalainen PA, Airaksinen O, Kankaanpää M (2012) Linear and nonlinear tremor acceleration characteristics in patients with Parkinson's disease. Physiol Meas 33(3):395–412PubMedCrossRefPubMedCentralGoogle Scholar
  213. Meigal AY, Rissanen SM, Tarvainen MP, Airaksinen O, Kankaanpaa M et al (2013) Non-linear EMG parameters for differential and early diagnostics of Parkinson’s disease. Front Neurol 4:135PubMedPubMedCentralCrossRefGoogle Scholar
  214. Merletti R, Farina D (2008) Surface EMG processing: introduction to the special issue. Biomed Signal Process Control 3:115–117CrossRefGoogle Scholar
  215. Merrikh-Bayat F (2011) Time series analysis of parkinson’s disease, huntington’s disease and amyotrophic lateral sclerosis. Procedia Comput Sci 3:210–215CrossRefGoogle Scholar
  216. Mesin L, Cescon C, Gazzoni M, Merletti R, Rainoldi A (2009) A bidimensional index for the selective assessment of myoelectric manifestations of peripheral and central muscle fatigue. J Electromyogr Kinesiol 19:851–863PubMedCrossRefPubMedCentralGoogle Scholar
  217. Millan H, Kalauzi A, Cukic M, Biondi R (2010) Nonlinear dynamics of meteorological variables: Multifractality and chaotic invariants in daily records from Pastaza, Ecuador. Theor Appl Climatol 102:75–85CrossRefGoogle Scholar
  218. Minasyan GR, Chatten JB, Chatten MJ, Harner RN (2010) Patient-specific early seizure detection from scalp EEG. J Clin Neurophysiol 27:163–178PubMedPubMedCentralCrossRefGoogle Scholar
  219. Minguez C, Winblad B (2010) Biomarkers for Alzheimer’s disease and other forms of dementia: clinical needs, limitations and future aspects. Exp Gerontol 45:5–14CrossRefGoogle Scholar
  220. Mobasser F, Eklund JM, Hashtrudi-Zaad K (2007) Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search. IEEE Trans Biomed Eng 54:683–693PubMedCrossRefPubMedCentralGoogle Scholar
  221. Molina-Picó A, Cuesta-Frau D, Aboy M, Crespo C, Miró-Martínez P et al (2011) Comparative study of approximate entropy and sample entropy robustness to spikes. Artif Intell Med 53:97–106PubMedCrossRefPubMedCentralGoogle Scholar
  222. Monsifrot J, Carpentier EL, Aoustin Y (2004) Sequential decoding of intramuscular EMG signals via estimation of a Markov model. IEEE Trans Neural Syst Rehabil Eng 22:1030–1038CrossRefGoogle Scholar
  223. Morales CJ, Kolaczyk ED (2002) Wavelet-based multifractal analysis of human balance. Ann Biomed Eng 30:588–597PubMedCrossRefPubMedCentralGoogle Scholar
  224. Mormann F, Kreuz T, Andrzejak RG, Peter D, Lehnertz K et al (2003) Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res 53:173–185CrossRefGoogle Scholar
  225. Mormann F, Thomas K, Christoph R, Andrzejak RG, Kraskov A et al (2005) On the predictability of epileptic seizures. Clin Neurophysiol 116:569–587PubMedCrossRefPubMedCentralGoogle Scholar
  226. Movahed MS, Jafari GR, Ghasemi F, Rahvar S, Tabar MRR (2006) Multifractal detrended fluctuation analysis of sunspot time series. J Stat Mech Theory Exp 2006(2):1–17CrossRefGoogle Scholar
  227. Muzy JF, Bacry E, Arneodo A (1991) Wavelets and multifractal formalism for singular signals: application to turbulence data. Phys Rev Lett 67:3515–3518PubMedCrossRefPubMedCentralGoogle Scholar
  228. Muzy JF, Bacry E, Arneodo A (1994) The multifractal formalism revisited with wavelets. Int J Bifurcation Chaos 4:245–302CrossRefGoogle Scholar
  229. Namazi H, Kulish VV (2015) Fractional diffusion based modelling and prediction of human brain response to external stimuli. Comput Math Methods Med 2015:148534PubMedPubMedCentralGoogle Scholar
  230. Namazi H, Kulish VV (2016) Fractal based analysis of the influence of odorants on heart activity. Sci Rep 6:38555PubMedPubMedCentralCrossRefGoogle Scholar
  231. Namazi H, Kulish VV, Wong A (2015) Mathematical modelling and prediction of the effect of chemotherapy on cancer cells. Sci Rep 5:13583PubMedPubMedCentralCrossRefGoogle Scholar
  232. Namazi H, Kulish VV, Akrami A (2016a) The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal. Sci Rep 6:26639PubMedPubMedCentralCrossRefGoogle Scholar
  233. Namazi H, Kulish VV, Hussaini J, Delaviz A, Delaviz F et al (2016b) A signal processing based analysis and prediction of seizure onset in patients with epilepsy. Oncotarget 7:342–350PubMedPubMedCentralGoogle Scholar
  234. Nasehi S, Pourghassem H (2013) Patient-specific epileptic seizure onset detection algorithm based on spectral features and IPSONN classifier. In: International conference on Communication Systems and Network Technologies, pp 186–190, 2013Google Scholar
  235. Nathan DG, Fontanarosa PB, Wilson JD (2001) Opportunities for medical research in the 21st century. JAMA 285:533–534PubMedCrossRefPubMedCentralGoogle Scholar
  236. Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36:2027–2036CrossRefGoogle Scholar
  237. Ossadnik SM, Buldyrev SV, Goldberger AL, Havlin S, Mantegna RN et al (1994) Correlation approach to identify coding regions in DNA sequences. Biophys J 67:64–70PubMedPubMedCentralCrossRefGoogle Scholar
  238. Oswiecimka P, Kwapien J, Drozdz S, Rak R (2005) Investigating multifractality of stock market fluctuations using wavelet and detrending fluctuation methods. Acta Phys Pol B 36:2447–2457Google Scholar
  239. Parker TS, Chua LO (1989) Practical numerical algorithms for chaotic systems. Springer, New York, pp 193–194CrossRefGoogle Scholar
  240. Parkinson I, Fazzalari N (1994) Cancellous bone structure analysis using image analysis. Australas Phys Eng Sci Med 17:64–70PubMedPubMedCentralGoogle Scholar
  241. Patrzalek E (2006) Fractals: Useful Beauty General Introduction to Fractal Geometry. In: General Introduction to Fractal Geometry, pp 1–7, Stan Ackermans Institute, IPO Centre for User- System Interaction, Eindhoven University of TechnologyGoogle Scholar
  242. Pavei J, Walz R, Marques JLB (2014) Study of biomarkers for prediction of epileptic seizures using ECG. In: Proceedings CBEB 2014 XXIV Brazilian conference on Biomedical Engineering—CBEB 2014 (Uberlândia), pp 1677–1680Google Scholar
  243. Pavei J, Heinzen RG, Novakova B, Walz R, Serra AJ et al (2017) Early seizure detection based on cardiac autonomic regulation dynamics. Front Physiol 8:765PubMedPubMedCentralCrossRefGoogle Scholar
  244. Peitgen HO, Jurgens H, Saupe D (1992) Chaos and Fractals, Springer, New York (Appendix B)CrossRefGoogle Scholar
  245. Peng C-K, Buldyrev SV, Havlin S, Simons M, Stanley HE et al (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49:1685–1689CrossRefGoogle Scholar
  246. Peng C-K, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82–87CrossRefGoogle Scholar
  247. Peng CK, Mietus JE, Liu Y, Lee C, Hausdorff JM et al (2002) Quantifying fractal dynamics of human respiration: age and gender effects. Ann Biomed Eng 30:683–692PubMedCrossRefPubMedCentralGoogle Scholar
  248. Penney JB, Young AB (1993) Huntington’s disease. In: Jankovic J, Tolosa E (eds) Parkinson’s disease and movement disorders. Williams & Wilkins, Baltimore, pp 205–216Google Scholar
  249. Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE et al (2009) Mild cognitive impairment: ten years later. Arch Neurol 66:1447PubMedPubMedCentralCrossRefGoogle Scholar
  250. Petrosian A, Prokhorov DV, Lajara-Nanson W, Schiffer RB (2001) Recurrent neural network-based approach for early recognition of Alzheimer’s disease in EEG. Clin Neurophysiol 112:1378–1387PubMedCrossRefPubMedCentralGoogle Scholar
  251. Pezard L, Jech R, Ruzicka E (2001) Investigation of non-linear properties of multi- channel EEG in the early stages of Parkinson’s disease. Clin Neurophysiol 112:38–45PubMedCrossRefPubMedCentralGoogle Scholar
  252. Phinyomark A, Limsakul C, Phukpattaranont P (2009) A comparative study of wavelet denoising for multifunction myoelectric control. In: International conference on Computer and Automation Engineering, ICCAE, pp 21–25Google Scholar
  253. Pikkujamsa SM, Makikallio TM, Sourannder LB, Raiha IJ, Puukka P et al (1999) Cardiac interbeat interval dynamics from childhood to senescence. Comparison of conventional and new measures based on fractals and chaos theory. Circulation 100:393–399PubMedCrossRefPubMedCentralGoogle Scholar
  254. Pikkujamsa SM, Makikallio TH, Airaksinen KEJ, Huikuri HV (2001) Determinants and interindividual variation of R-R interval dynamics in healthy middle aged subjects. Am J Phys Heart Circ Phys 280:H1400–H1406Google Scholar
  255. Pincus SM (1991) Approximate entropy as a measure of system complexity. In: Proc Natl Acad Sci USA, vol 88, pp 2297–2301Google Scholar
  256. Pincus SM (1995) Approximate entropy ApEn as a complexity measure. Chaos 5:110–117PubMedCrossRefPubMedCentralGoogle Scholar
  257. Podobnik B, Stanley HE (2008) Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett 100:084102CrossRefGoogle Scholar
  258. Podobnik B, Grosse I, Horvati D, Ilic S, Ivanov PC et al (2009a) Quantifying cross-correlations using local and global detrending approaches. Eur Phys J B 71:243–250CrossRefGoogle Scholar
  259. Podobnik B, Horvatic D, Petersen AM, Stanley HE (2009b) Cross-correlations between volume change and price change. Proc Natl Acad Sci USA 106:22079–22084PubMedCrossRefPubMedCentralGoogle Scholar
  260. Podobnik B, Jiang Z-Q, Zhou W-X, Stanley HE (2011) Statistical tests for power-law cross-correlated processes. Phys Rev E 84:066118CrossRefGoogle Scholar
  261. 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
  262. Ponnusamy A, Marques JL, Reuber M (2011) Heart rate variability measures as biomarkers in patients with psychogenic nonepileptic seizures: potential and limitations. Epilepsy Behav 22:685–691PubMedCrossRefPubMedCentralGoogle Scholar
  263. Ponnusamy A, Marques JL, Reuber M (2012) Comparison of heart rate variability parameters during complex partial seizures and psychogenic nonepileptic seizures. Epilepsia 53:1314–1321PubMedCrossRefPubMedCentralGoogle Scholar
  264. Poornachandra S (2008) Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process 18:49–55CrossRefGoogle Scholar
  265. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W et al (2013) The global prevalence of dementia: a systematic review and meta analysis. Alzheimers Dement 9:63–75PubMedCrossRefPubMedCentralGoogle Scholar
  266. Quintero-Rincon A, Pereyra M, Giano CD, Batatia H, Risk M (2016) A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals. J Phys Conf Ser 705:012032CrossRefGoogle Scholar
  267. Quiroga RQ, Garcia H (2003) Single-trial event-related potentials with wavelet denoising. Clin Neurophysiol 114:376–390CrossRefGoogle Scholar
  268. Rabbi AF, Aarabi A, Fazel-Rezai R (2010) Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG. In: Proceedings of the IEEE Engineering in Medicine and Biology Society conference, pp 3301–3304Google Scholar
  269. Reaz MBI, Hussain MS, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Procedures Online 8:11–35CrossRefGoogle Scholar
  270. Rhaman M, Karim AHM, Hasan M, Sultana J (2013) Successive RR interval analysis of PVC with sinus rhythm using fractal dimension, Poincare plot and sample entropy method. Int J Image Graphics Signal Process 2:17–24CrossRefGoogle Scholar
  271. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Phys Heart Circ Phys 278:H2039–H2049Google Scholar
  272. Rodrıguez-Bermudez G, Garcıa-Laencina PJ (2015) Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl Math Inf Sci 9:2309–2321Google Scholar
  273. Rogowski Z, Gath I, Bental E (1981) On the prediction of epileptic seizures. Biol Cybern 42:9–15PubMedCrossRefPubMedCentralGoogle Scholar
  274. Ronghua T, Chizhong H, Siyu F, Suming Z, Jinxiang W et al (2001) Correlation analysis of the cognitive function and changes of BEAM and CT scan in patients with Alzheimer’s disease. J Neurol Disord Stroke 8:266–269Google Scholar
  275. Ruonala V, Meigal A, Rissanen SM, Airaksinen O, Kankaanpää M et al (2014) EMG signal morphology and kinematic parameters in essential tremor and Parkinson’s disease patients. J Electromyogr Kinesiol 24:300–306PubMedCrossRefPubMedCentralGoogle Scholar
  276. Saab ME, Gotman J (2005) A system to detect the onset of epileptic seizures in scalp EEG. Clin Neurophysiol 116:427–442PubMedCrossRefPubMedCentralGoogle Scholar
  277. Salant Y, Gath I, Henriksen O (1998) Prediction of epileptic seizures from two-channel EEG. Med Biol Eng Comput 36:549–556PubMedCrossRefPubMedCentralGoogle Scholar
  278. Samiee K, Kiranyaz S, Gabbouj M, Saramäki T (2015) Long-term epileptic EEG classification via 2D mapping and textural features. Expert Syst Appl 42:7175–7185CrossRefGoogle Scholar
  279. Sanei S, Chambers JA (2007) EEG signal processing. Wiley, New YorkCrossRefGoogle Scholar
  280. Sarkar M, Leong TY (2003) Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artif Intell Med 27:201–222PubMedPubMedCentralCrossRefGoogle Scholar
  281. Schaafsma JD, Giladi N, Balash Y, Bartels AL, Gurevich T et al (2003) Gait dynamics in parkinson’s disease: relationship to parkinsonian features, falls and response to levodopa. J Neurol Sci 212:47–53PubMedCrossRefPubMedCentralGoogle Scholar
  282. Schellenberg R, Schwarz A (1993) EEG- and EP-mapping--possible indicators for disturbed information processing in schizophrenia? Prog Neuro-Psychopharmacol Biol Psychiatry 17:595–607CrossRefGoogle Scholar
  283. Schiff SJ, Jerger K, Duong DH, Chang T, Spano ML, Ditto WL (1994) Controlling chaos in the brain. Nature 370(6491):615–620PubMedCrossRefPubMedCentralGoogle Scholar
  284. Sezgin N (2012) Analysis of EMG signals in aggressive and normal activities by using higher-order spectra. Sci World J 2012:478952CrossRefGoogle Scholar
  285. Shen CP, Chen CC, Hsieh SL, Chen WH, Chen JM et al (2013) High-performance seizure detection system using a wavelet-approximate entropy-fSVM cascade with clinical validation. Clin EEG Neurosci 44:247–256PubMedCrossRefPubMedCentralGoogle Scholar
  286. Shen D, Cul L, Cul B, Fang J, Li D et al (2015) A systematic review and meta-analysis of the functional MRI investigation of motor neuron disease. Front Neurol 6:246PubMedPubMedCentralCrossRefGoogle Scholar
  287. Sheng H, Chen YQ (2011) Multifractional property analysis of human sleep EEG signals. In: Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering conference, August 28–31, 2011, Washington, DC, USAGoogle Scholar
  288. Shoeb A, Guttag J (2010) Application of machine learning to epileptic seizure detection. In: Proceedings of the 27th international conference on Machine Learning, Haifa, Israel, 2010Google Scholar
  289. Sian J, Gerlach M, Youdim MBH, Riederer P (1999) Parkinson’s disease: a major hypokinetic basal ganglia disorder. J Neural Transm 106:443–476PubMedCrossRefPubMedCentralGoogle Scholar
  290. Silchenko A, Hu CK (2001) Multifractal characterization of stochastic resonance. Phys Rev E 63:041105CrossRefGoogle Scholar
  291. Simjanoska M, Gjoreski M, Bogdanova A, Koteska B, Gams M, et al (2018) ECG-derived blood pressure classification using complexity analysis-based machine learning. In: Proceedings of the 11th international joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) – 5, HEALTHINF, pp 282–292Google Scholar
  292. Singh M, Singh M, Paramjeet (2013) Neuro-degenerative disease diagnosis using human gait: a review. IJITKMI 7:16–20Google Scholar
  293. Siuly S, Li Y (2014) A novel statistical framework for multiclass EEG signal classification. Eng Appl Artif Intell 34:154–167CrossRefGoogle Scholar
  294. Siuly S, Zhang Y (2016) Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci Eng 1:54–64CrossRefGoogle Scholar
  295. Siuly S, Li Y, Wen P (2011) EEG signal classification based on simple random sampling technique with least square support vector machines. Int J Biomed Eng Technol 7:390–409CrossRefGoogle Scholar
  296. Solinski M, Gierałtowski J, Zebrowski J (2016) Modeling heart rate variability including the effect of sleep stages. Chaos 26:023101PubMedCrossRefPubMedCentralGoogle Scholar
  297. Song Y (2011) A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection. J Biomed Sci Eng 4:788–796CrossRefGoogle Scholar
  298. Soo Y, Sugi M, Nishino M, Yokoi H, Arai T, et al (2009) Quantitative estimation of muscle fatigue using surface electromyography during static muscle contraction. In: 31st IEEE Engineering in Medicine and Biology Society conference, Minneapolis, MN, Sept 3–6, 1, 2975–2978Google Scholar
  299. Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301CrossRefGoogle Scholar
  300. Stam CJ, Jelles B, Achtereekte HA, Rombouts SA, Slaets JP et al (1995) Investigation of EEG non-linearity in dementia and Parkinson’s disease. Electroencephalogr Clin Neurophysiol 95:309–317PubMedCrossRefPubMedCentralGoogle Scholar
  301. Stanley HE, Meakin P (1988) Multifractal phenomena in physics and chemistry. Nature 335:405–409CrossRefGoogle Scholar
  302. Stanley HE, Amaral LAN, Goldberger AL, Havlin S, Ivanov PC et al (1999) Statistical physics and physiology: Monofractal and multifractal approaches. Physica A 270:309–324PubMedCrossRefPubMedCentralGoogle Scholar
  303. Stollberger C, Finsterer J, Lutz W, Stoberl C, Kroiss A et al (2000) Multivariate analysis based prediction rule for pulmonary embolism. Thromb Res 97:267–273PubMedCrossRefPubMedCentralGoogle Scholar
  304. Sugavaneswaran L, Umapathy K, Krishnan S (2012) Ambiguity domain-based identification of altered gait pattern in ALS disorder. J Neural Eng 9(4):046004PubMedCrossRefPubMedCentralGoogle Scholar
  305. Suryanarayanan S, Reddy NP, Gupta V (1995) Artificial neural networks for estimation of joint angle from EMG signals. In: Proceedings of 17th international conference of the engineering in Medicine and Biology Society, 1Google Scholar
  306. Tafhim M, Kshirsagar P (2014) A Review on EMG Signal Classification for neurological disorder using neural network. In: International conference on Advances in Engineering & Technology – 2014 (ICAET-2014), pp 21–23Google Scholar
  307. Talebinejad M, Chan ADC, Miri A, Dansereau RM (2009) Fractal analysis of surface electromyography signals: a novel power spectrum- based method. J Electromyogr Kinesiol 19:840–850CrossRefGoogle Scholar
  308. Talkner P, Weber RO (2000) Power spectrum and detrended fluctuation analysis: application to daily temperatures. Phys Rev E 62:150CrossRefGoogle Scholar
  309. Telesca L, Lapenna V (2006) Measuring multifractality in seismic sequences. Tectonophysics 423:115–123CrossRefGoogle Scholar
  310. Telesca L, Lapenna V, Macchiato M (2005) Multifractal fluctuations in earthquake-related geoelectrical signals. New J Phys 7:214CrossRefGoogle Scholar
  311. Thankor NV, Tong S (2009) Quantitative EEG analysis methods and clinical applications (Artech House, 2009)Google Scholar
  312. Thongpanja S, Phinyomark A, Quaine F, Laurillau Y, Wongkittisuksa B, et al (2013) Effects of window size and contraction types on the stationarity of biceps brachii muscle EMG signals. In: IEEE 7th International Convention on Rehabilitation Engineering and Assistive Technology, 2013, 44:1–44:4Google Scholar
  313. Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans Inf Technol Biomed 13:703–710CrossRefGoogle Scholar
  314. Ullah K, Jung-Hoon K (2009) A mathematical model for mapping EMG signal to joint torque for the human elbow joint using nonlinear regression. In: 4th International Conference on Autonomous Robots and Agents, ICARA 2009Google Scholar
  315. Vaillancourt DE, Newell KM (2000) The dynamics of resting and postural tremor in Parkinson's disease. Clin Neurophysiol 111(11):2046–2056PubMedCrossRefPubMedCentralGoogle Scholar
  316. Vanage AM, Khade RH, Shinde DB (2012) Classifying five different arrhythmias by analyzing the ECG signals. IJCEM Int J Comput Eng Manag 15:75–80Google Scholar
  317. Vandewalle N, Ausloos M (1998) Crossing of two mobile averages: a method for measuring the roughness exponent. Phys Rev E 58:6832–6834CrossRefGoogle Scholar
  318. Vandewalle N, Ausloos M, Boveroux P (1999a) The moving averages demystified. Physica A 269:170–176CrossRefGoogle Scholar
  319. Vandewalle N, Ausloos M, Houssa M, Mertens PW, Heyns MM (1999b) Non-Gaussian behavior and anticorrelations in ultrathin gate oxides after soft breakdown. Appl Phys Lett 74:1579–1581CrossRefGoogle Scholar
  320. Varon C, Caicedo A, Jansen K, Lagae L, Huffel SV (2014) Detection of epileptic seizures from single lead ECG by means of phase rectified signal averaging. In: 36th Annual international conference of the IEEE Engineering in Medicine and Biology Society, Chicago, pp 3789–3790Google Scholar
  321. Vaseghi VS (1996) Advanced Signal Processing and Digital Noise Reduction. John Wiley, New YorkCrossRefGoogle Scholar
  322. Vassoler RT, Zebende GF (2012) DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity. Physica A 391:2438–2443CrossRefGoogle Scholar
  323. Venugopal G, Ramakrishnan S (2014) Analysis of progressive changes associated with muscle fatigue in dynamic contraction of biceps brachii muscle using surface EMG signals and bispectrum features. Biomed Eng Lett 4:269–276CrossRefGoogle Scholar
  324. Venugopal G, Navaneethakrishna M, Ramakrishnan S (2014) Extraction and analysis of multiple time window features associated with muscle fatigue conditions using SEMG signals. Expert Syst Appl 41:2652–2659CrossRefGoogle Scholar
  325. Vinik AI, Erbas T, Casellini CM (2013) Diabetic cardiac autonomic neuropathy, inflammation and cardiovascular disease. J Diab Invest 4:4–18CrossRefGoogle Scholar
  326. Vogel J, Castellini C, van der Smagt PP (2011) EMG-based teleoperation and manipulation with the DLR LWR-III. In: Proceedings IEEE/RSJ international conference on Intelligent Robots and Systems, 2011, pp 672–678Google Scholar
  327. von Campenhausen S, Bornschein B, Wick R, Botzel K, Sampaio C et al (2005) Prevalence and incidence of Parkinson’s disease in Europe. Eur Neuropsychopharmacol 15:473–490CrossRefGoogle Scholar
  328. Wang G, Huang H, Xie H, Wang Z, Hu X (2007) Multifractal analysis of ventricular fibrillation and ventricular tachycardia. Med Eng Phys 29:375–379PubMedCrossRefPubMedCentralGoogle Scholar
  329. Wang Y, Wei Y, Wu C (2010) Cross-correlations between Chinese A-share and B-share markets. Physica A 389:5468–5478CrossRefGoogle Scholar
  330. Warner JH, Sampalo C (2016) Modeling variability in the progression of Huntington’s disease a novel modeling approach applied to structural imaging markers from TRACK-HD. CPT Pharmacometrics Syst Pharmacol 5:437–445PubMedPubMedCentralCrossRefGoogle Scholar
  331. Webber CL Jr, Zbilut JP (1984) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76:965–973CrossRefGoogle Scholar
  332. Weibel ER (1991) Fractal geometry: a design principle for living organisms. Am J Physiol 261:361–369Google Scholar
  333. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ et al (2012) The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement 8:S1–S68PubMedCrossRefPubMedCentralGoogle Scholar
  334. Wessel N, Ziehmann C, Kurths J, Meyerfeldt U, Schirdewan A et al (2000) Short-term forecasting of life-threatening cardiac arrhythmias based on symbolic dynamics and finite- time growth rules. Phys Rev E 61:733–739CrossRefGoogle Scholar
  335. Wink AM, Bullmore E, Barnes A, Bernard F, Suckling J (2008) Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI. Hum Brain Mapp 29:791–801PubMedCrossRefPubMedCentralGoogle Scholar
  336. Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M et al (2011) The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol 21:655–679PubMedCrossRefPubMedCentralGoogle Scholar
  337. Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96:29–38PubMedCrossRefPubMedCentralGoogle Scholar
  338. Yuan Y, Zhuang X, Liu Z (2012) Price-volume multifractal analysis and its application in Chinese stock markets. Physica A 391:3484–3495CrossRefGoogle Scholar
  339. Yunfeng Wu, Sin CN (2010) A PDF-based classification of gait cadence patterns in patients with amyotrophic lateral sclerosis. In: Annual international conference of the IEEE EMBS Buenos Aires, Argentina, pp 1304–1307, 2010Google Scholar
  340. Zandi SA, Dumont GA, Javidan M, Tafreshi R (2009) An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG. Conf Proc IEEE Eng Med Biol Soc 2009:2228–2231Google Scholar
  341. Zebende GF (2011) DCCA cross-correlation coefficient: quantifying level of cross-correlation. Physica A 390:614–618CrossRefGoogle Scholar
  342. Zhang ZG, Liu HT, Chan SC, Luk KDK, Hu Y (2010) Time- dependent power spectral density estimation of surface electromyography during isometric muscle contraction: methods and comparisons. J Electromyogr Kinesiol 20:89–101PubMedCrossRefPubMedCentralGoogle Scholar
  343. Zheng Y, Gao JB, Sanchez JC, Principe JC, Okun MS (2005) Multiplicative multifractal modeling and discrimination of human neuronal activity. Phys Lett A 344:253–264CrossRefGoogle Scholar
  344. Zhou WX (2008) Multifractal detrended cross-correlation analysis for two nonstationary time series. Phys Rev E 77:066211CrossRefGoogle Scholar
  345. Zhou P, Li X, Nezhad FJ, Rymer WZ, Barkhaus PE (2012) Duration of observation required in detecting fasciculation potentials in amyotrophic lateral sclerosis using high-density surface EMG. J Neuroeng Rehabil 9:78PubMedPubMedCentralCrossRefGoogle Scholar
  346. Zhuo SM, Gan JQ, Sepulveda F (2008) Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface. Inf Sci 178:1629–1640CrossRefGoogle Scholar
  347. Zueva MV (2015) Fractality of sensations and the brain health: the theory linking neurodegenerative disorder with distortion of spatial and temporal scale-invariance and fractal complexity of the visible world. Front Aging Neurosci 7:135PubMedPubMedCentralCrossRefGoogle 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

Personalised recommendations