Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing
► 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 asse...
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| Vydáno v: | Accident analysis and prevention Ročník 50; s. 992 - 1002 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Kidlington
Elsevier Ltd
01.01.2013
Elsevier |
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| ISSN: | 0001-4575, 1879-2057, 1879-2057 |
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| Abstract | ► 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. |
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| AbstractList | 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 – 52 hours. 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. 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.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. 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. ► 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. |
| Author | St. Hilaire, Melissa A. Anderson, Clare Klerman, Elizabeth B. Sullivan, Jason P. Lockley, Steven W. Cohen, Daniel A. Barger, Laura K. |
| AuthorAffiliation | c Division of Sleep Medicine, Harvard Medical School, 401 Park Drive, 2nd Floor East Boston, Massachusetts 02215, USA a Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, Massachusetts 02115, USA b Division of Sleep Medicine, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, Massachusetts 02115, USA d Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, Massachusetts 02215, USA |
| AuthorAffiliation_xml | – name: c Division of Sleep Medicine, Harvard Medical School, 401 Park Drive, 2nd Floor East Boston, Massachusetts 02215, USA – name: b Division of Sleep Medicine, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, Massachusetts 02115, USA – name: a Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, Massachusetts 02115, USA – name: d Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, Massachusetts 02215, USA |
| Author_xml | – sequence: 1 givenname: Melissa A. surname: St. Hilaire fullname: St. Hilaire, Melissa A. email: msthilaire@rics.bwh.harvard.edu organization: Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 2 givenname: Jason P. surname: Sullivan fullname: Sullivan, Jason P. organization: Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 3 givenname: Clare surname: Anderson fullname: Anderson, Clare organization: Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 4 givenname: Daniel A. surname: Cohen fullname: Cohen, Daniel A. organization: Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 5 givenname: Laura K. surname: Barger fullname: Barger, Laura K. organization: Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 6 givenname: Steven W. surname: Lockley fullname: Lockley, Steven W. organization: Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA – sequence: 7 givenname: Elizabeth B. surname: Klerman fullname: Klerman, Elizabeth B. organization: Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA |
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| Cites_doi | 10.3109/00207459008994241 10.1152/japplphysiol.00877.2007 10.1016/0304-3940(94)90841-9 10.1093/sleep/26.2.117 10.1177/074873099129000920 10.1093/sleep/32.10.1393 10.1038/npp.2010.63 10.1162/jocn.1997.9.3.392 10.1001/jama.294.9.1025 10.3758/BF03195580 10.1111/j.1532-5415.2009.02303.x 10.5665/sleep.2128 10.1016/j.bbr.2005.11.018 10.1016/j.ijpsycho.2011.09.012 10.1093/sleep/30.9.1129 10.1111/j.1365-2869.1992.tb00021.x 10.1046/j.1365-2869.2003.00337.x 10.1126/scitranslmed.3000458 10.1093/sleep/33.2.197 10.1093/sleep/27.3.374 10.1056/NEJMoa041404 10.1080/07420520601067931 10.1371/journal.pone.0001233 10.1016/j.cub.2008.06.047 |
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| Keywords | Performance impairment Sleep deprivation Pattern recognition Performance evaluation Poverty Loss Medical screening Algorithm Decision tree Response Sleep Sleep wake cycle Performance Recognition Inequality |
| Language | English |
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| SubjectTerms | Adolescent Adult Aged Algorithms Arousal - physiology Assessments Attention - physiology Biological and medical sciences Female Humans Impairment Male Medical sciences Middle Aged Miscellaneous Pattern recognition Pattern Recognition, Automated Performance impairment Predictive Value of Tests Prevention and actions Psychomotor Performance - classification Public health. Hygiene Public health. Hygiene-occupational medicine Reaction Time - physiology Sensitivity and Specificity Sleep Deprivation Sleep loss Sleepiness Time of use Training Wakefulness - physiology |
| Title | Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing |
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