Medical Engineering & Physics
Volume 28, Issue 9 , Pages 851-859 , November 2006

Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients

  • Carlos Gómez

      Affiliations

    • E.T.S. Ingenieros de Telecomunicación, Universidad de 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 423000x5589; fax: +34 983 423667.
    • ,
  • Roberto Hornero

      Affiliations

    • E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Spain
    • ,
  • Daniel Abásolo

      Affiliations

    • E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Spain
    • ,
  • Alberto Fernández

      Affiliations

    • Centro de Magnetoencefalografía Dr. Pérez-Modrego, Universidad Complutense de Madrid, Spain
    • ,
  • Miguel López

      Affiliations

    • E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Spain

Received 11 October 2005 ,Accepted 13 January 2006.

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. Cummings BJ, Pike CJ, Shankle R, Cotman CW. Beta-amyloid deposition and other measures of neuropathology predict cognitive status in Alzheimer's disease. Neurobiol Aging. 1996;17:921–933
  3. Trojanowski JQ. The cellular and molecular correlates of cognitive impairments in the Alzheimer's disease brain. Neurobiol Aging. 1996;17:941–943
  4. Reisberg B. Functional assessment staging (FAST). Psychopharmacol Bull. 1988;24:653–659
  5. 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
  6. 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
  7. Signorino M, Pucci E, Belardinelli N, Nolle G, Angeleri F. EEG spectral analysis in vascular and Alzheimer dementia. Electroencephalogr Clin Neurophysiol. 1995;94:313–325
  8. Osipova D, Ahveninen J, Jensen O, Ylikoski A, Pekkonen E. Altered generation of spontaneous oscillations in Alzheimer's disease. Neuroimage. 2005;27:835–841
  9. Fernández A, Maestú F, Amo C, Gil P, Fehr T, Wienbruch C, et al. Focal tempoparietal slow activity in Alzheimer's disease revealed by magnetoencephalography. Biol Psychiatry. 2002;52:764–770
  10. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clin Neurophysiol. 2004;115:1490–1505
  11. Grassberger P, Procaccia I. Characterization of strange attractors. Phys Rev Lett. 1983;50(5):346–349
  12. Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D. 1983;9:189–208
  13. 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
  14. Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A. 1992;45:3403–3411
  15. 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
  16. 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
  17. 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
  18. Eckmann JP, Ruelle D. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D. 1992;56:185–187
  19. Tononi G, Edelman GM, Sporns O. Complexity and coherency: integrating information in the brain. Trends Cogn Sci. 1998;2(12):474–484
  20. Pritchard WS, Duke DW, Coburn KL, Moore NC, Tucker KA, Jann MW, et al. EEG-based neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures. Electroencephalogr Clin Neurophysiol. 1994;91:118–130
  21. Stam CJ, Jelles B, Achtereekte HAM, Rombouts SARB, Slaets JPJ, Keunen RWM. Investigation of EEG nonlinearity in dementia and Parkinson's disease. Electroencephalogr Clin Neurophysiol. 1995;95:309–317
  22. Besthorn C, Zerfass R, Geiger-Kabisch C, Sattel H, Daniel S, Schreiter-Gasser U, et al. Discrimination of Alzheimer's disease and normal aging by EEG data. Electroencephalogr Clin Neurophysiol. 1997;103:241–248
  23. 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
  24. Kolmogorov AN. Three approaches to the quantitative definition of information. Inform Trans. 1965;1:3–11
  25. Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA. 1994;91:5033–5037
  26. Zozor S, Ravier P, Buttelli O. On Lempel–Ziv complexity for multidimensional data analysis. Physica A. 2005;345:285–302
  27. Lempel A, Ziv J. On the complexity of finite sequences. IEEE Trans Inform Theory. 1976;22:75–81
  28. Zhang XS, Roy RJ, Jensen EW. EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans Biomed Eng. 2001;48:1424–1433
  29. Zhang XS, Roy RJ. Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng. 2001;48:312–323
  30. Huang L, Yu P, Ju F, Cheng J. Prediction of response to incision using the mutual information of electroencephalogram during anesthesia. Med Eng Phys. 2003;25:321–327
  31. Zhang XS, Roy RJ. Predicting movement during anaesthesia by complexity analysis of electroencephalograms. Med Biol Eng Comput. 1999;37(3):327–334
  32. Zhang JW, Zheng CX, Jiang DZ. EEG complexity measurement of focal ischemic cerebral injury. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1998;20(4):2027–2029
  33. Wu X, Xu J. Complexity and brain functions. Acta Biophys Sinica. 1991;7:103–106
  34. Xu J, Liu ZR, Liu R, Yang QF. Information transformation in human cerebral cortex. Physica D. 1997;106:363–374
  35. Amigó JM, Szczepánski J, Wajnryb E, Sanchez-Vives MV. Estimating the entropy rate of spike trains via Lempel–Ziv complexity. Neural Comput. 2004;16:717–736
  36. Szczepánski J, Amigó JM, Wajnryb E, Sanchez-Vives MV. Application of Lempel–Ziv complexity to the analysis of neural discharges. Network-Comput Neural Syst. 2003;14:335–350
  37. Zhang XS, Zhu YS, Thakor NV, Wang ZZ. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng. 1999;46:548–555
  38. Nagarajan R. Quantifying physiological data with Lempel–Ziv complexity — certain issues. IEEE Trans Biomed Eng. 2002;49:1371–1373
  39. Radhakrishnan N, Gangadhar BN. Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. IEEE Eng Med Biol. 1998;17:89–94
  40. McKhann G, Drachman D, Folstein M. Clinical diagnosis of Alzheimer's disease: report of NINCDS–ADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease. Neurology. 1984;34:939–944
  41. Kaspar F, Schuster HG. Easily calculable measure for the complexity of spatiotemporal patterns. Phys Rev A. 1987;36(2):842–848
  42. Yan R, Gao RX. Complexity as a measure for machine health evaluation. IEEE Trans Instrum Meas. 2004;53(4):1327–1334
  43. Kaiser HF. The application of electronic computers to factor analysis. Educ Phychol Meas. 1960;20:141–151
  44. Cattell RB. The scree test for the number of factors. Multivar Behav Res. 1966;1(2):140–161
  45. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–577
  46. Abásolo D, Hornero R, Gómez C, García M, López M. Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and Central Tendency Measure. Med Eng Phys. 2006;28:315–322
  47. Na SH, Jin SH, Kim SY, Ham BJ. EEG is schizophrenic patients: mutual information analysis. Clin Neurophysiol. 2002;113:1954–1960
  48. Jing H, Takigawa M. Comparison of human ictal, interictal and normal non-linear component analyses. Clin Neurophysiol. 2000;111:1282–1292

PII: S1350-4533(06)00016-6

doi: 10.1016/j.medengphy.2006.01.003

Medical Engineering & Physics
Volume 28, Issue 9 , Pages 851-859 , November 2006

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