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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

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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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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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