Hornero Roberto, Abásolo Daniel, Escudero Javier, Gómez Carlos
Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain.
Philos Trans A Math Phys Eng Sci. 2009 Jan 28;367(1887):317-36. doi: 10.1098/rsta.2008.0197.
The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer's disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel-Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.
本研究的目的是展示非线性方法在分析阿尔茨海默病（AD）患者脑电图（EEG）和脑磁图（MEG）方面的实用性。以下非线性方法已被应用于研究AD患者和对照受试者的EEG和MEG背景活动：近似熵、样本熵、多尺度熵、自互信息和莱姆尔 - 齐夫复杂度。我们讨论了为什么这些非线性方法适用于分析EEG和MEG。此外，在应用于相同的EEG和MEG记录数据库时，对所有这些方法的性能进行了比较。我们的结果表明，AD患者的EEG和MEG背景活动比健康对照受试者的更简单、更规则。与先前的研究一致，我们的工作表明非线性分析技术可能对AD诊断有用。
