Entropy and complexity analyses in alzheimer's disease: An MEG study

被引：56

作者：

Gómez C. ^{[1
]}

Hornero R. ^{[1
]}

机构：

[1] Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, 47011 - Valladolid, Paseo Belén

来源：

Open Biomedical Engineering Journal | 2010年 / 4卷 / SPEC. ISSUE 2期

关键词：

Alzheimer's disease; Complexity; Entropy; Magnetoencephalogram; Signal processing;

D O I：

10.2174/1874120701004010223

中图分类号：

学科分类号：

摘要：

Alzheimer's disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi's fractal dimension (HFD), Maragos and Sun's fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch's t-test with Bonferroni's correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis. © Gómez and Hornero.

引用

页码：223 / 235

页数：12

共 69 条

[1]

Hari R., Magnetoencephalography in clinical neurophysiological assessment of human cortical functions, Electroencephalography: Basic Principles, Clinical Applications,and Related Fields, pp. 1165-1197, (2005)

[2]

Hamalainen M., Hari R., Ilmoniemi R., Knuutila J., Lounasmaa O.V., Magnetoencephalography: Theory, instrumentation, and applications to noninvasive studies of the working human brain, Rev. Mod. Phys, 65, pp. 413-497, (1993)

[3]

Blennow K., de Leon M.J., Zetterberg H., Alzheimer's disease, Lancet, 368, pp. 387-403, (2006)

[4]

Jorm A.F., Cross-national comparisons of the occurrence of Alzheimer's and vascular dementias, Eur. Arch. Psychiatry Clin. Neurosci, 240, pp. 218-222, (1991)

[5]

Jeong J., EEG dynamics in patients with Alzheimer's disease, Clin. Neurophysiol, 115, 7, pp. 1490-1505, (2004)

[6]

Cummings J.L., Alzheimer's disease, N. Engl. J. Med, 351, pp. 56-67, (2004)

[7]

Jeong J., Kim S.Y., Han S.H., Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension, Electroenceph. Clin. Neurophysiol, 106, 3, pp. 220-228, (1998)

[8]

Jelles B., van Birgelen J.H., Slaets J.P.J., Hekster R.E.M., Jonkman E.J., Stam C.J., Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls, Clin. Neurophysiol, 110, pp. 1159-1167, (1999)

[9]

van Cappellen van Walsum A.M., Pijnenburg Y.A.L., Berendse H.W., van Dijk B.W., Knol D.L., Scheltens P., Stam C.J., A neural complexity measure applied to MEG data in Alzheimer's disease, Clin. Neurophysiol, 114, pp. 1034-1040, (2003)

[10]

Eckmann J.P., Ruelle D., Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems, Physica D, 56, 2-3, pp. 185-187, (1992)

← 1 2 3 4 5 6 7 →