Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
Accid Anal Prev. 2013 Jan;50:992-1002. doi: 10.1016/j.aap.2012.08.003. Epub 2012 Sep 5.
There is currently no "gold standard" marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the "real world" or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26-52h. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual's behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss.
目前，没有一种“金标准”标志物可以用来衡量因睡眠不足而导致的认知表现受损。我们利用模式识别算法来确定在仅进行一次测试的情况下（例如在“现实世界”或现场条件下），从受控实验室条件下收集的数据中哪些特征可以最可靠地识别出因睡眠不足而导致的认知表现受损。使用来自实验室研究的客观心理运动警觉任务（PVT）和主观 Karolinska 睡眠量表（KSS）数据来开发用于测试模式识别算法的训练集，在这些研究中，受试者被剥夺睡眠 26-52 小时。然后，该算法在来自实验室和现场实验的数据中进行了测试。该模式识别算法能够使用 PVT、KSS、清醒时间和时间信息来识别在实验室条件下进行研究的个体单次测试中的表现受损，其灵敏度和特异性高达 82%。当该算法在从不在训练集中的数据个体的真实世界条件下收集的数据上进行测试时，对被归类为低表现受损个体的预测准确率高达 98%。对中度和严重表现受损的预测准确性较低。我们得出结论，模式识别算法可能是一种很有前途的方法，可以仅使用个体当前的行为信息来识别个体的表现受损。与实验室环境中表现受损高度相关的单次测试特征（例如，PVT 失误次数）可能不是真实世界条件下表现受损的最佳指标。应进一步测试模式识别算法，以确定它们是否能够与其他在真实世界条件下评估嗜睡的评估方法结合使用，从而量化因睡眠不足而导致的表现受损。
