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主成分分析检测到与睡眠相关的平衡控制变化。

Principal component analysis detects sleepiness-related changes in balance control.

机构信息

Sleep and Performance Research Center, Washington State University Spokane, Spokane, USA.

出版信息

Gait Posture. 2010 Jul;32(3):419-21. doi: 10.1016/j.gaitpost.2010.06.012. Epub 2010 Jul 14.

Abstract

Computerized posturography exploits balance scores that quantify the size, dynamics, or structure of the recorded sway. Since people employ different balance strategies, one single balance score will not detect balance changes in all subjects. Principal component analysis (PCA) can combine balance scores that quantify different features into one new balance score. We tested the score with 20 subjects by measuring their balance every 2 h during 28 h of sustained waking. The new balance score was more sensitive than its components (p<0.001 vs. p≥0.051) to the small sleepiness-related balance decrements that occurred during the short 28-h period. PCA provided a more sensitive balance score that applied to all of the subjects.

摘要

计算机平衡测试利用平衡评分来量化记录的摆动的大小、动态或结构。由于人们采用不同的平衡策略,因此单一的平衡评分并不能检测到所有受试者的平衡变化。主成分分析(PCA)可以将量化不同特征的平衡评分组合成一个新的平衡评分。我们通过在 28 小时的清醒过程中每 2 小时测量 20 名受试者的平衡,用该评分对他们进行了测试。与发生在短暂的 28 小时周期内的与轻度困倦相关的较小的平衡下降相比,新的平衡评分比其组成部分更敏感(p<0.001 比 p≥0.051)。PCA 提供了一个更敏感的平衡评分,适用于所有受试者。

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