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Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

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Abstract

We analysed the electroencephalogram (EEG) from Alzheimer’s disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that nonlinear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.

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Abbreviations

AD:

Alzheimer’s disease

AMI:

Auto mutual information

ApEn:

Approximate entropy

AUC:

Area under the ROC curve

CMI:

Cross mutual information

D 2 :

Correlation dimension

EEG:

Electroencephalogram

L1:

Largest Lyapunov exponent

LZ:

Lempel-Ziv

MI:

Mutual information

MMSE:

Mini-mental state examination

MSE:

Multiscale entropy

ROC:

Receiver operating characteristic

SampEn:

Sample entropy

SD:

Standard deviation

References

  1. Abásolo D, Hornero R, Espino P et al (2005) Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clin Neurophysiol 116:1826–1834. doi:10.1016/j.clinph.2005.04.001

    Article  Google Scholar 

  2. Abásolo D, Hornero R, Espino P et al (2006) Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol Meas 27:241–253. doi:10.1088/0967-3334/27/3/003

    Article  Google Scholar 

  3. Abásolo D, Hornero R, Gómez C et al (2006) Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med Eng Phys 28:315–322. doi:10.1016/j.medengphy.2005.07.004

    Article  Google Scholar 

  4. Alonso JF, Mañanas MA, Hoyer D et al (2007) Evaluation of respiratory muscles activity by means of mutual information function at different levels of ventilatory effort. IEEE Trans Biomed Eng 54:1573–1582. doi:10.1109/TBME.2007.893494

    Article  Google Scholar 

  5. Andrzejak RG, Lehnertz K, Moormann F 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 Stat Nonlin Soft Matter Phys 64:061907. doi:10.1103/PhysRevE.64.061907

    Google Scholar 

  6. Babloyantz A, Destexhe A (1988) The Creutzfeldt-Jakob disease in the hierarchy of chaotic attractors. In: Markus M, Müller S, Nicolis G (eds) From chemical to biological organization. Springer, Berlin, pp 307–316

    Google Scholar 

  7. Bird TD (2001) Alzheimer’s disease and other primary dementias in Harrison’s principles of internal medicine. In: Braunwald E, Fauci AS, Kasper DL et al (eds) The McGraw-Hill Companies Inc., New York, pp 2391–2399

  8. Bruhn J, Röpcke H, Rehberg B et al (2000) Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect. Anesthesiology 93:981–985. doi:10.1097/00000542-200010000-00018

    Article  Google Scholar 

  9. Costa M, Goldberger AL, Peng CK (2005) Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 71:021906. doi:10.1103/PhysRevE.71.021906

    MathSciNet  Google Scholar 

  10. David O, Cosmelli D, Friston KJ (2004) Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21:659–673. doi:10.1016/j.neuroimage.2003.10.006

    Article  Google Scholar 

  11. De Lucia M, Fritschy J, Dayan P et al (2008) A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. Med Biol Eng Comput 46:263–272. doi:10.1007/s11517-007-0289-4

    Article  Google Scholar 

  12. Eckmann JP, Ruelle D (1992) Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D 56:185–187. doi:10.1016/0167-2789(92)90023-G

    Article  MATH  MathSciNet  Google Scholar 

  13. Escudero J, Abásolo D, Hornero R et al (2006) Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol Meas 27:1091–1106. doi:10.1088/0967-3334/27/11/004

    Article  Google Scholar 

  14. Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198. doi:10.1016/0022-3956(75)90026-6

    Article  Google Scholar 

  15. Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140. doi:10.1103/PhysRevA.33.1134

    Article  MathSciNet  MATH  Google Scholar 

  16. Goldberger AL, Peng CK, Lipsitz LA (2002) What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 23:23–26. doi:10.1016/S0197-4580(01)00266-4

    Article  Google Scholar 

  17. Gómez C, Hornero R, Abásolo D et al (2007) Analysis of the magnetoencephalogram background activity in Alzheimer’s disease patients with auto mutual information. Comput Methods Program Biomed 87:239–247. doi:10.1016/j.cmpb.2007.07.001

    Article  Google Scholar 

  18. Hesse CW, James CJ (2007) Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies. Med Biol Eng Comput 45:909–916. doi:10.1007/s11517-006-0103-8

    Article  Google Scholar 

  19. Hornero R, Aboy M, Abásolo D et al (2005) Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng 52:1671–1680. doi:10.1109/TBME.2005.855722

    Article  Google Scholar 

  20. Hoyer D, Pompe B, Chon KH et al (2005) Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales. IEEE Trans Biomed Eng 52:584–592. doi:10.1109/TBME.2005.844023

    Article  Google Scholar 

  21. Hoyer D, Friedrich H, Frank B et al (2006) Autonomic information flow improves prognostic impact of task force HRV monitoring. Comput Methods Program Biomed 81:246–255. doi:10.1016/j.cmpb.2006.01.002

    Article  Google Scholar 

  22. Huang L, Yu P, Ju F et al (2003) Prediction of response to incision using the mutual information of electroencephalogram during anaesthesia. Med Eng Phys 25:321–327. doi:10.1016/S1350-4533(02)00249-7

    Article  Google Scholar 

  23. Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 115:1490–1505. doi:10.1016/j.clinph.2004.01.001

    Article  Google Scholar 

  24. Jeong J, Chae JH, Kim SY et al (2001) Nonlinear dynamic analysis of the EEG in patients with Alzheimer’s disease and vascular dementia. J Clin Neurophysiol 18:58–67. doi:10.1097/00004691-200101000-00010

    Article  Google Scholar 

  25. Jeong J, Gore JC, Peterson BS (2001) Mutual information analysis of the EEG in patients with Alzheimer’s disease. Clin Neurophysiol 112:827–835. doi:10.1016/S1388-2457(01)00513-2

    Article  Google Scholar 

  26. Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  27. Lehnertz K, Mormann F, Kreuz T et al (2003) Seizure prediction by nonlinear EEG analysis. IEEE Eng Med Biol 22:57–63. doi:10.1109/MEMB.2003.1191451

    Article  Google Scholar 

  28. Markand ON (1990) Organic brain syndromes and dementias. In: Daly DD, Pedley TA (eds) Current practice of clinical electroencephalography. Raven Press, New York, pp 401–423

    Google Scholar 

  29. Mendez MO, Bianchi AM, Montano N et al (2008) On arousal from sleep: time–frequency análisis. Med Biol Eng Comput 46:341–351. doi:10.1007/s11517-008-0309-z

    Article  Google Scholar 

  30. Na SH, Jin SH, Kim SY et al (2002) EEG in schizophrenic patients: mutual information analysis. Clin Neurophysiol 113:1954–1960. doi:10.1016/S1388-2457(02)00197-9

    Article  Google Scholar 

  31. Palacios M, Friedrich H, Götze C et al (2007) Changes of autonomic information flow due to idiopathic dilated cardiomyopathy. Physiol Meas 28:677–688. doi:10.1088/0967-3334/28/6/006

    Article  Google Scholar 

  32. Palus M (1996) Coarse-grained entropy rates for characterization of complex time series. Physica D 93:64–77. doi:10.1016/0167-2789(95)00301-0

    Article  MATH  Google Scholar 

  33. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301. doi:10.1073/pnas.88.6.2297

    Article  MATH  MathSciNet  Google Scholar 

  34. Pincus SM (2001) Assessing serial irregularity and its implications for health. Ann N Y Acad Sci 954:245–267

    Article  Google Scholar 

  35. Pincus SM, Goldberger AL (1994) Physiological time series analysis: what does regularity quantify? Am J Physiol Heart Circ Physiol 266:H1643–H1656

    Google Scholar 

  36. Pincus SM, Keefe DL (1992) Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol Endocrinol Metab 262:E741–E754

    Google Scholar 

  37. Pompe B (1993) Measuring statistical dependencies in a time series. J Stat Phys 73:587–610. doi:10.1007/BF01054341

    Article  MATH  MathSciNet  Google Scholar 

  38. Pompe B, Blidh P, Hoyer D et al (1998) Using mutual information to measure coupling in the cardiorespiratory system. IEEE Eng Med Biol 17:32–39. doi:10.1109/51.731318

    Article  Google Scholar 

  39. Pritchard WS, Duke DW, Coburn KL et al (1994) EEG-based neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear EEG measures. Electroencephalogr Clin Neurophysiol 91:118–130. doi:10.1016/0013-4694(94)90033-7

    Article  Google Scholar 

  40. Radhakrishnan N, Gangadhar BN (1998) Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. IEEE Eng Med Biol 17:89–94. doi:10.1109/51.677174

    Article  Google Scholar 

  41. Röschke J, Fell J, Beckmann P (1995) Non-linear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent. Psychiatry Res 56:257–269. doi:10.1016/0165-1781(95)02562-B

    Article  Google Scholar 

  42. Rossor M (2001) Alzheimer’s disease. In: Donaghy M (ed) Brain’s diseases of the nervous system. Oxford University Press, Oxford, pp 750–754

    Google Scholar 

  43. Selkoe DJ (1994) Cell biology of the amyloid beta-protein precursor and the mechanism of Alzheimer’s disease. Annu Rev Cell Biol 10:373–403. doi:10.1146/annurev.cb.10.110194.002105

    Article  Google Scholar 

  44. Stam CJ, Jelles B, Achtereekte HAM et al (1995) Investigation of EEG non-linearity in dementia and Parkinson’s disease. Electroencephalogr Clin Neurophysiol 95:309–317. doi:10.1016/0013-4694(95)00147-Q

    Article  Google Scholar 

  45. Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301. doi:10.1016/j.clinph.2005.06.011

    Article  Google Scholar 

  46. Varma NK, Kushwaha R, Beydoun A et al (1997) Mutual information analysis and detection of interictal morphological differences in interictal epileptiform discharges of patients with partial epilepsies. Electroencephalogr Clin Neurophysiol 103:426–433. doi:10.1016/S0013-4694(97)00039-4

    Article  Google Scholar 

  47. Vastano JA, Swinney HL (1988) Information transport in spatiotemporal systems. Phys Rev Lett 60:1773–1776. doi:10.1103/PhysRevLett.60.1773

    Article  MathSciNet  Google Scholar 

  48. Xu J, Liu ZR, Liu R et al (1997) Information transformation in human cerebral cortex. Physica D 106:363–374. doi:10.1016/S0167-2789(97)00042-0

    Article  Google Scholar 

  49. Zhang XS, Roy RJ (2001) Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng 48:312–323. doi:10.1109/10.914794

    Article  Google Scholar 

  50. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    Google Scholar 

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Acknowledgments

This work was supported by grant projects VA102A06 and VA108A06 from Consejería de Educación de la Junta de Castilla y León and by a grant project from Ministerio de Educación y Ciencia and FEDER grant MTM 2005-08519-C02-01.

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Authors and Affiliations

  1. Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011, Valladolid, Spain

    D. Abásolo, J. Escudero, R. Hornero & C. Gómez

  2. Hospital Clínico San Carlos, c/Profesor Lagos s/n, 28040, Madrid, Spain

    P. Espino

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  1. D. Abásolo
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  2. J. Escudero
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Correspondence to D. Abásolo.

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Abásolo, D., Escudero, J., Hornero, R. et al. Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46, 1019–1028 (2008). https://doi.org/10.1007/s11517-008-0392-1

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