Medical Engineering & Physics
Volume 31, Issue 3 , Pages 306-313 , April 2009

Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients

  • Carlos Gómez

      Affiliations

    • Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
    • Corresponding Author InformationCorresponding author at: E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain. Tel.: +34 983 423000x3703; fax: +34 983 423667.
    • ,
  • Ángela Mediavilla

      Affiliations

    • Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
    • ,
  • Roberto Hornero

      Affiliations

    • Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
    • ,
  • Daniel Abásolo

      Affiliations

    • Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
    • ,
  • Alberto Fernández

      Affiliations

    • Centro de Magnetoencefalografía Dr. Pérez-Modrego, Complutense University of Madrid, Madrid, Spain

Received 29 April 2008 ,Revised 24 June 2008 ,Accepted 25 June 2008.

References 

  1. Bird TD. Alzheimer's disease and other primary dementias. In:  Braunwald E,  Fauci AS,  Kasper DL,  Hauser SL,  Longo DL,  Jameson JL editor. Harrison's principles of internal medicine. New York: The McGraw-Hill Companies Inc.; 2001;p. 2391–2399
  2. Jorm AF. Cross-national comparisons of the occurrence of Alzheimer's and vascular dementias. Eur Arch Psychiatry Clin Neurosci. 1991;240:218–222
  3. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clin Neurophysiol. 2004;115:1490–1505
  4. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys. 1993;65:413–497
  5. Hari R. Magnetoencephalography in clinical neurophysiological assessment of human cortical functions. In:  Niedermeyer E,  Lopes da Silva F editor. Electroencephalography: Basic principles, clinical applications, and related fields. Philadelphia: Lippincontt Williams & Wilkins; 2005;p. 1165–1197
  6. Vrba J, Robinson SE. Signal processing in magnetoencephalography. Methods. 2001;25:249–271
  7. Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol. 2005;116:2266–2301
  8. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. 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. 2001;64:061907
  9. Accardo A, Affinito M, Carrozzi M, Bouquet F. Use of fractal dimension for the analysis of electroencephalographic time series. Biol Cybern. 1997;77:339–350
  10. Jelles B, van Birgelen JH, Slaets JPJ, Hekster REM, Jonkman EJ, Stam CJ. Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls. Clin Neurophysiol. 1999;110:1159–1167
  11. Kotini A, Anninos P. Detection of non-linearity in schizophrenic patients using magnetoencephalography. Brain Topogr. 2002;15:107–113
  12. Jeong J, Chae JH, Kim SY, Han SH. Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia. J Clin Neurophysiol. 2001;18:58–67
  13. Anninos PA, Adamopoulos AV, Kotini A, Tsagas N. Nonlinear analysis of brain activity in magnetic influenced Parkinson patients. Brain Topogr. 2000;13:135–144
  14. Stam CJ, Jelles B, Achtereekte HAM, Rombouts SARB, Slaets JPJ, Keunen RWM. Investigation of EEG nonlinearity in dementia and Parkinson's disease. Electroenceph Clin Neurophysiol. 1995;95:309–317
  15. Hornero R, Espino P, Alonso A, López M. Estimating complexity from EEG background activity of epileptic patients. IEEE Eng Med Biol. 1999;18:73–79
  16. Kim YW, Krieble KK, Kim CB, Reed J, Rae-Grant AD. Differentiation of alpha coma from awake alpha by nonlinear dynamics of electroencephalography. Electroenceph Clin Neurophysiol. 1996;98:35–41
  17. Nandrino JL, Pezard L, Martinerie J, el Massioui F, Renault B, Jouvent R, et al. Decrease of complexity in EEG as a symptom of depression. NeuroReport. 1994;5:528–530
  18. Babloyantz A, Destexhe A. The Creutzfeldt–Jakob disease in the hierarchy of chaotic attractors. In:  Markus M,  Müller S,  Nicolis G editor. From chemical to biological organization. Berlin: Springer-Verlag; 1988;p. 307–316
  19. Abatzoglou I, Anninos P, Adamopoulos A, Koukourakis M. Nonlinear analysis of brain magnetoencephalographic activity in Alzheimer disease patients. Acta Neurol Belg. 2007;107:34–39
  20. van Cappellen van Walsum AM, Pijnenburg YAL, Berendse HW, van Dijk BW, Knol DL, Scheltens Ph, et al. A neural complexity measure applied to MEG data in Alzheimer's disease. Clin Neurophysiol. 2003;114:1034–1040
  21. Besthorn C, Sattel H, Geiger-Kabisch C, Zerfass R, Förstl H. Parameters of EEG dimensional complexity in Alzheimer's disease. Electroenceph Clin Neurophysiol. 1995;95:84–89
  22. Eckmann JP, Ruelle D. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D. 1992;56:185–187
  23. Grassberger P, Procaccia I. Characterization of strange attractors. Phys Rev Lett. 1983;50:346–349
  24. Mandelbrot BB. How long is the coast of Britain? Statistical selfsimilarity and fractional dimension. Science. 1967;156:636–638
  25. Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D. 1988;31:277–283
  26. Maragos P, Sun FK. Measuring the fractal dimension of signals: morphological covers and iterative optimization. IEEE Trans Signal Proc. 1983;41:108–121
  27. Katz M. Fractals and the analysis of waveforms. Comput Biol Med. 1988;18:145–156
  28. Petrosian A. Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. Proc IEEE Symp Comp Med Syst. 1995;212–217
  29. Mandelbrot B. Fractals: Form, chance, and dimension. San Francisco: Freeman; 1977;
  30. Esteller R, Vachtsevanos G, Echauz J, Litt B. A comparison of waveform fractal dimension algorithms. IEEE Trans Circuits Syst. 2001;48:177–183
  31. Block A, von Bloh W, Schellnhuber HJ. Efficient box-counting determination of generalized fractal dimensions. Phys Rev A. 1990;42:1869–1874
  32. Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198
  33. Bendat J, Piersol A. Random data: Analysis and measurement procedures. New York: Willey; 2000;
  34. Doyle TLA, Dugan EL, Humphries B, Newton RU. Discriminating between elderly and young using a fractal dimension analysis of centre of pressure. Int J Med Sci. 2004;1:11–20
  35. Theiler J, Eubank S, Longtin A, Galdrikian B, Doyne Farmer J. Testing for nonlinearity in time series: the method of surrogate data. Physica D. 1992;58:77–94
  36. Timmer J. Power of surrogate data testing with respect to nonstationarity. Phys Rev E. 1998;58:5153–5156
  37. Ferenets R, Lipping T, Anier A, Jäntti V, Melto S, Hovilehto S. Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans Biomed Eng. 2006;53:1067–1077
  38. Acharya R, Bhat PS, Kannathal N, Rao A, Lim CM. Analysis of cardiac health using fractal dimension and wavelet transformation. ITBM-RBM. 2005;26:133–139
  39. Klonowsky W, Olejarczyk E, Stepien R. Epileptic seizures in economic organism. Physica A. 2004;342:701–707
  40. Duchene J, Goubel F. Surface electromyogram during voluntary contractions: processing tools and relation to physiological events. Crit Rev Biomed Eng. 1993;21:313–397
  41. Chau T, Chau D, Casas M, Berall G, Kenny DJ. Investigating the stationarity of paediatric aspiration signals. IEEE Trans Neural Syst Rehab Eng. 2005;13:99–105
  42. Simon R, Radmacher MD, Dobbin K, McShane KM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003;95:14–18
  43. Jeong J, Kim SJ, Han SH. Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension. Electroencephalogr Clin Neurophysiol. 1998;106:220–228
  44. Woyshville MJ, Calabrese JR. Quantification of occipital EEG changes in Alzheimer's disease utilizing a new metric: the fractal dimension. Biol Psychiatry. 1994;35:381–387
  45. Gómez C, Hornero R, Abásolo D, Fernández A, López M. Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients. Med Eng Phys. 2006;28:851–859
  46. Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information. Comput Meth Programs Biomed. 2007;87:239–247

PII: S1350-4533(08)00112-4

doi: 10.1016/j.medengphy.2008.06.010

Medical Engineering & Physics
Volume 31, Issue 3 , Pages 306-313 , April 2009

Article Tools

* Image Usage & Resolution