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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Individualized Performance Prediction During Total Sleep Deprivation: Accounting for Trait Vulnerability to Sleep Loss
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Individualized Performance Prediction During Total Sleep Deprivation: Accounting for Trait Vulnerability to Sleep Loss

机译：总睡眠剥夺期间的个性化性能预测：占睡眠损失的特质脆弱性的核算

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Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotypespecific group-average model and used these models to identify each individual's phenotype. We then used the phenotypespecific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, -85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.
机译：易受睡眠损失漏洞的个体差异可以相当大，因此，最近的努力集中在开发个性化模型上，以预测睡眠损失对性能的影响。使用贝叶斯配方构建的个性化模型，该模型将个体的可用性数据与来自组平均模型的先验性能预测相结合，通常需要至少40小时的单个数据，然后在群体平均模型预测上显示出显着改善。在这里，我们通过观察人们可以将个体分为三种睡眠损失表型：弹性，平均和脆弱的患者，改善了开发个体化模型的基本贝叶斯制剂。对于每种表型，我们开发了一种表型群体平均模型，并使用这些模型来识别每个单独的表型。然后，我们使用贝叶斯配方中的表型模型来制作个性化预测。结果来自48人的精神运动警察试验数据表明，平均而言，在30小时内，平均鉴定了-85％的个体表型。在10-H-前方预测中提出的拟议方法的百分比为弹性受试者为16％，弱势受试者为6％。这些改进的权衡是平均对象预测准确性的略微下降。

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