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计算机选择的强直性快速眼动睡眠中早期阿尔茨海默病的脑电图标志物。

EEG markers of early Alzheimer's disease in computer selected tonic REM sleep.

作者信息

Prinz P N, Larsen L H, Moe K E, Vitiello M V

机构信息

American Lake Veterans Administration Medical Center, Tacoma, WA 98493.

出版信息

Electroencephalogr Clin Neurophysiol. 1992 Jul;83(1):36-43. doi: 10.1016/0013-4694(92)90130-a.

Abstract

All night sleep/wake EEGs were examined for diagnostic features sensitive to early Alzheimer's disease (AD) using computer automated techniques. Thirty-nine mild AD patients and 43 normal controls underwent 9 h of EEG recording in the sleep laboratory. All-night EEGs were screened for ideal, low artifact tonic REM sleep using autoregressive and power spectral techniques. The frequency spectra during tonic REM sleep revealed a significant shift towards slower wave forms in AD vs. control subjects. Beta (greater than 12 Hz) was reduced and theta and delta (2-8 Hz) increased in AD compared to control groups. This frequency shift was demonstrated by several analytic techniques, including binned spectral energies and unique zones in the frequency spectra. Discriminant analyses using optimal binned EEG variables correctly classified 74% of AD and 98% of control subjects, and unique zone scores correctly classified 92% of AD and 95% of control subjects, indicating that these sleep EEG changes are apparently predictive of AD status.

摘要

使用计算机自动化技术对整夜睡眠/觉醒脑电图进行检查,以寻找对早期阿尔茨海默病(AD)敏感的诊断特征。39名轻度AD患者和43名正常对照在睡眠实验室接受了9小时的脑电图记录。使用自回归和功率谱技术对整夜脑电图进行筛选,以寻找理想的、低伪迹的紧张性快速眼动睡眠。与对照组相比,紧张性快速眼动睡眠期间的频谱显示AD患者的波形明显向慢波形式转变。与对照组相比,AD患者的β波(大于12赫兹)减少,θ波和δ波(2 - 8赫兹)增加。这种频率变化通过几种分析技术得到证实,包括分箱频谱能量和频谱中的独特区域。使用最佳分箱脑电图变量进行判别分析,正确分类了74%的AD患者和98%的对照对象,独特区域分数正确分类了92%的AD患者和95%的对照对象,表明这些睡眠脑电图变化显然可预测AD状态。

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