Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance
Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance
复制标题
利用精神运动警觉性测试成绩的基线特征对注意力易受完全睡眠剥夺影响的程度进行分类
DOI：
10.1038/s41598-019-48280-4
复制
发表时间：
2019-08-20
影响因子：
4.6
通讯作者：
Gooley, Joshua J.
中科院分区：
文献类型：
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作者：
Chua, Eric Chern-Pin;Sullivan, Jason P.;Gooley, Joshua J.
睡眠剥夺期间的表现存在很大的个体差异。我们评估了精神运动警惕性测试（PVT）表现的基线特征是否可用于对参与者对完全睡眠剥夺的相对注意力脆弱性进行分类。在实验室中，健康成年人（n = 160，年龄 18-30 岁）每 2 小时完成一次 10 分钟的 PVT，同时保持清醒 >= 24 小时。根据一晚睡眠不足期间 PVT 失效的次数，参与者被分为易受影响 (n = 40)、中等 (n = 80) 或弹性 (n = 40)。对于每个基线 PVT（在起床后 4-14 小时进行），使用具有基于包装器的特征选择的线性判别模型来对参与者随后睡眠剥夺的脆弱性进行分类。在各个模型中，使用分层 5 倍交叉验证时，分类准确度约为 70%（范围 65-76%）。这些模型对弹性参与者的分类提供了约 78% 的敏感性和 86% 的特异性，对弱势参与者的分类提供了约 70% 的敏感性和 89% 的特异性。这些结果表明，从基线时单个 10 分钟 PVT 得出的特征可以提供大量但不完整的信息，说明一个人相对注意力容易受到完全睡眠剥夺的影响。从长远来看，当睡眠剥夺不可避免时，结合基线表现特征的建模方法可能会改善注意力表现的个性化预测。
There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants' relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n = 160, aged 18-30 years) completed a 10-min PVT every 2 h while being kept awake for >= 24 hours. Participants were categorized as vulnerable (n = 40), intermediate (n = 80), or resilient (n = 40) based on their number of PVT lapses during one night of sleep deprivation. For each baseline PVT (taken 4-14 h after wake-up time), a linear discriminant model with wrapper-based feature selection was used to classify participants' vulnerability to subsequent sleep deprivation. Across models, classification accuracy was about 70% (range 65-76%) using stratified 5-fold cross validation. The models provided about 78% sensitivity and 86% specificity for classifying resilient participants, and about 70% sensitivity and 89% specificity for classifying vulnerable participants. These results suggest features derived from a single 10-min PVT at baseline can provide substantial, but incomplete information about a person's relative attentional vulnerability to total sleep deprivation. In the long term, modeling approaches that incorporate baseline performance characteristics can potentially improve personalized predictions of attentional performance when sleep deprivation cannot be avoided.