Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing - St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B. (Accident analysis and prevention, 2013)
► Insufficient sleep results in cognitive performance impairment. ► There is widespread inter-individual variability in response to sleep loss. ► Current methods to assess impairment rely on tracking an individual over time. ► Our proposed methods can classify level of impairment from one field assessment. ► Such methods may identify individuals at risk before dangerous levels are reached. 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.