Patanaik Amiya, Kwoh Chee Keong, Chua Eric C P, Gooley Joshua J, Chee Michael W L
School of Computer Engineering, Nanyang Technological University, Singapore.
Centre for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore.
Sleep. 2015 May 1;38(5):723-34. doi: 10.5665/sleep.4664.
To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.
Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.
Two academic sleep laboratories.
Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.
In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.
In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.
Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.
确定从基线心理运动警觉任务（PVT）表现中得出的能够可靠预测睡眠剥夺易感性的指标。
受试者在受控的实验室环境中经历完全睡眠剥夺，并每1 - 2小时完成一次10分钟的PVT。根据睡眠剥夺后出现失误的中位数划分，将参与者分为睡眠剥夺易感性高或抗性高的类别。标准反应时间、漂移扩散模型（DDM）和小波指标是从基线时收集的PVT反应时间中得出的。使用结合了最大相关性和最小冗余特征选择以及基于包装器的启发式算法的支持向量机模型，利用休息时的数据将受试者分类为易感性高或抗性高。
两个学术睡眠实验室。
135名（69名女性，年龄18至25岁）和45名（3名女性，年龄22至32岁）健康成年人的独立样本。
在两个数据集中，模型选择了DDM指标、相差超过250毫秒的连续反应时间数量以及两个小波特征作为预测睡眠剥夺易感性的特征。使用在每个数据集中选择的最佳特征集，通过五重分层交叉验证，分类准确率分别为77%和82%。
在两个数据集中，模型选择了DDM指标、相差超过250毫秒的连续反应时间数量以及两个小波特征作为预测睡眠剥夺易感性的特征。使用在每个数据集中选择的最佳特征集，通过五重分层交叉验证，分类准确率分别为77%和82%。
尽管各研究的实验条件存在差异，但漂移扩散模型参数与完全睡眠剥夺期间表现的个体差异可靠相关。这些结果证明了基线表现的漂移扩散模型在估计睡眠剥夺后心理运动警觉性下降易感性方面的实用性。
