Medical Engineering & Physics
Volume 28, Issue 4 , Pages 315-322 , May 2006

Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure

Received 17 March 2005 ,Revised 7 June 2005 ,Accepted 4 July 2005.

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: McGraw-Hill; 2001;p. 2391–2399
  2. Markand ON. Organic brain syndromes and dementias. In:  Daly DD,  Pedley TA editor. Current practice of clinical electroencephalography. New York: Raven Press; 1990;p. 401–423
  3. Jeong J. Nonlinear dynamics of EEG in Alzheimer's disease. Drug Dev Res. 2002;56:57–66
  4. Jeong J. EEG dynamics in patients with Alzheimer's disease. Clin Neurophysiol. 2004;115:1490–1505
  5. Kantz H, Schreiber T. Nonlinear time series analysis. Cambridge: Cambridge University Press; 1997;
  6. 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
  7. Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D. 1983;9:189–208
  8. Babloyantz A, Destexhe A. Low-dimensional chaos in an instance of epilepsy. Proc Natl Acad Sci USA. 1986;83:3513–3517
  9. 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
  10. 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
  11. Jeong J, Kim DJ, Chae JH, Kim SY, Ko HJ, Paik IH. Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension. Med Eng Phys. 1998;20:669–676
  12. Pijn JP, van Neerven J, Noest A, Lopes da Silva FH. Chaos or noise in EEG signals; dependence on state and brain site. Electroenceph Clin Neurophysiol. 1991;79:371–381
  13. Ferri R, Alicata F, Del Gracco S, Elia M, Musumeci SA, Stefanini MC. Chaotic behavior of EEG slow-wave activity during sleep. Electroenceph Clin Neurophysiol. 1996;99:539–543
  14. 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
  15. 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. Electroenceph Clin Neurophysiol. 1994;91:118–130
  16. Jeong J, Kim SJ, Han SH. Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension. Electroenceph Clin Neurophysiol. 1998;106:220–228
  17. 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
  18. Wolf A, Swift JB, Swinney HL, Vastano JA. Determining Lyapunov exponents from a time-series. Physica D. 1985;16:285–317
  19. Meyer-Lindenberg A. The evolution of complexity in human brain development: an EEG study. Electroenceph Clin Neurophysiol. 1996;99:405–411
  20. Eckmann JP, Ruelle D. Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D. 1992;56:185–187
  21. Pezard L, Martinerie J, Varela FJ, Bouchet F, Guez D, Derousné C, et al. Entropy maps characterize drug effects on brain dynamics in Alzheimer's disease. Neurosci Lett. 1998;253:5–8
  22. Jeong J, Gore JC, Peterson BS. Mutual information analysis of the EEG in patients with Alzheimer's disease. Clin Neurophysiol. 2001;112:827–835
  23. Pijnenburg YAL, vd Made Y, van Cappellen van Walsum AM, Knol DL, Scheltens P, Stam CJ. EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task. Clin Neurophysiol. 2004;115:1332–1339
  24. Lempel A, Ziv J. On the complexity of finite sequences. IEEE Trans Inform Theory. 1976;22:75–81
  25. Cohen ME, Hudson DL, Deedwania PC. Applying continuous chaotic modeling to cardiac signals. IEEE Eng Med Biol Mag. 1996;15:97–102
  26. 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
  27. Cohen ME, Hudson DL. New chaotic methods for biomedical signal analysis. In: Proceedings of the IEEE EMBS International Conference on Information Technology Applications in Biomedicine. 2000;p. 123–128
  28. Cohen ME, Hudson DL. EEG analysis based on chaotic evaluation of variability. In: Proceedings of the 23rd Annual EMBS International Conference. 2001;p. 3827–3830
  29. Hudson DL, Cohen ME, Kramer M, Szeri A, Chang FL. Diagnostic implications of EEG analysis in patients with dementia. In: Proceedings of the Second International IEEE EMBS Conference on Neural Engineering. 2005;p. 629–632
  30. Zhang XS, Zhu YS, Thakor NV, Wang ZZ. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng. 1999;46:548–555
  31. Radhakrishnan N, Gangadhar BN. Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. IEEE Eng Med Biol. 1998;17:89–94
  32. Wu X, Xu J. Complexity and brain functions. Acta Biophys Sinica. 1991;7:103–106
  33. Xu J, Liu ZR, Liu R, Yang QF. Information transformation in human cerebral cortex. Physica D. 1997;106:363–374
  34. Zhang XS, Roy RJ. Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng. 2001;48:312–323
  35. 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
  36. Nagarajan R. Quantifying physiological data with Lempel–Ziv complexity—Certain issues. IEEE Trans Biomed Eng. 2002;49:1371–1373
  37. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–577
  38. 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
  39. 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. Electroenceph Clin Neurophysiol. 1997;103:241–248
  40. Röschke J, Fell J, Beckmann P. Non-linear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent. Psychiatr Res. 1995;56:257–269
  41. Kaspar F, Schuster HG. Easily calculable measure for the complexity of spatiotemporal patterns. Phys Rev A. 1987;36:842–848
  42. Lehnertz K. Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy—an overview. Int J Psychophysiol. 1999;34:45–52
  43. Kyriazis M. Practical applications of chaos theory to the modulation of human ageing: nature prefers chaos to regularity. Biogerontology. 2003;4:75–90

PII: S1350-4533(05)00142-6

doi: 10.1016/j.medengphy.2005.07.004

Medical Engineering & Physics
Volume 28, Issue 4 , Pages 315-322 , May 2006