Abásolo Daniel, Hornero Roberto, Escudero Javier, Espino Pedro
Biomedical Engineering Group, Department of Signal Theory and Communications, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain.
IEEE Trans Biomed Eng. 2008 Sep;55(9):2171-9. doi: 10.1109/TBME.2008.923145.
We studied the EEG background activity of Alzheimer's disease (AD) patients with detrended fluctuation analysis (DFA). DFA provides an estimation of the scaling information and long-range correlations in time series. We recorded the EEG in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels (for limited time scales from 0.01 to 0.04 s and from 0.08 to 0.43 s, respectively), with a clear bend when their corresponding slopes (alpha(1) and alpha(2)) were different. No significant differences between groups were found with alpha(1). However, alpha(2) values were significantly lower in control subjects at electrodes T5, T6, and O1 (p < 0.01, Student's t-test). These findings suggest that the scaling behavior of the EEG is sensitive to AD. Although alpha(2) values allowed us to separate AD patients and controls, accuracies were lower than with spectral analysis. However, a forward stepwise linear discriminant analysis with a leave-one-out cross-validation procedure showed that the combined use of DFA and spectral analysis could improve the diagnostic accuracy of each individual technique. Thus, although spectral analysis outperforms DFA, the combined use of both techniques may increase the insight into brain dysfunction in AD.
我们使用去趋势波动分析（DFA）研究了阿尔茨海默病（AD）患者的脑电图背景活动。DFA可对时间序列中的标度信息和长程相关性进行估计。我们记录了11例AD患者和11例年龄匹配的对照者的脑电图。我们的结果显示，所有受试者通道中存在两个标度区域（分别对应于0.01至0.04秒以及0.08至0.43秒的有限时间尺度），当它们相应的斜率（α(1)和α(2)）不同时会出现明显的转折。两组之间在α(1)方面未发现显著差异。然而，在电极T5、T6和O1处，对照者的α(2)值显著更低（p < 0.01，学生t检验）。这些发现表明，脑电图的标度行为对AD敏感。尽管α(2)值能够区分AD患者和对照者，但其准确性低于频谱分析。然而，采用留一法交叉验证程序的向前逐步线性判别分析表明，DFA和频谱分析联合使用可提高每种单独技术的诊断准确性。因此，尽管频谱分析优于DFA，但两种技术联合使用可能会增强对AD脑功能障碍的认识。
