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Parsimonious Identification of Physiological Indices for Monitoring Cognitive Fatigue

  • Lance J. Myers
  • J. Hunter Downs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

The objective of this study was to identify a parsimonious set of physiological measures that could be used to best predict cognitive fatigue levels. A 37 hour sleep deprivation study was conducted to induce reduced levels of alertness and cognitive impairment as measured by a psychomotor vigilance test. Non-invasive, wearable and ambulatory sensors were used to acquire cardio-respiratory and motion data during the sleep deprivation. Subsequently 23 potential predictors were derived from the raw sensor data. The least absolute shrinkage and selection operator, along with a cross validation strategy was used to create a sparse model and identify a minimum predictor subset that provided the best prediction accuracy. Final predictor selection was found to vary with task and context. Depending on context selected predictors indicated elevated levels of sympathetic nervous system activity, increased restlessness during engaging tasks and increased cardio-respiratory synchronization with increasing cognitive fatigue.

Keywords

cognitive fatigue heart rate variability feature selection wearable sensors 

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References

  1. 1.
    Kleitman, N.: Duration of sleep. In: Sleep and Wakefulness, pp. 114–121. University of Chicago Press, Chicago (1963)Google Scholar
  2. 2.
    National Sleep Foundation. In: 2005 Sleep in America poll. Washington, National Sleep Foundation (2005) Google Scholar
  3. 3.
    Kripke, D.F., Garfinkel, L., Wingard, D.L., Klauber, M.R., Marler, M.R.: Mortality associated with sleep duration and insomnia. Arch Gen Psychiat 59, 131–136 (2002)CrossRefPubMedGoogle Scholar
  4. 4.
    Carskadon, M.A., Roth, T.: Sleep restriction. In: Monk, T.H. (ed.) Sleep, sleepiness and performance. John Wiley & Sons, New York (1991)Google Scholar
  5. 5.
    Arnedt, J.T., Wilde, J.S., Munt, P.W., Maclean, A.W.: Simulated driving performance following prolonged wakefulness and alcohol consumption: Separate and combined contributions to impairment. J. Sleep Res. 9, 233–241 (2000)CrossRefPubMedGoogle Scholar
  6. 6.
    Gundel, A., Marsalek, K., ten Thoren, C.A.: Critical review of existing mathematical models for alertness. Somnologie - Schlafforschung und Schlafmedizin 11, 148–156 (2007)CrossRefGoogle Scholar
  7. 7.
    Zhang, C., Zheng, C., Yu, X.: Automatic recognition of cognitive fatigue from physiological indices by using wavelet packet transform and kernel learning algorithms. Expert Systems with Applications 36, 4664–4671 (2009)CrossRefGoogle Scholar
  8. 8.
    Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology: Heart Rate Variability. Standards of Measurement, Physiological Interpretation, and Clinical Use.: Circulation 93, 1043–1065 (1996)Google Scholar
  9. 9.
    Grossman, P., Kollai, M.: Respiratory sinus arrhythmia, cardiac vagal tone, and respiration: Within- and between-individual relations. Psychophysiology 30, 486–495 (1993)CrossRefPubMedGoogle Scholar
  10. 10.
    Rajendra Acharya, U., et al.: Heart rate variability: a review. Medical and Biological Engineering and Computing 44, 1031–1051 (2006)CrossRefPubMedGoogle Scholar
  11. 11.
    Lotric, M.B., Stefanovska, A.: Synchronization and modulation in the human cardiorespiratory system. Physica A: Statistical Mechanics and its Applications 283, 451–461 (2000)CrossRefGoogle Scholar
  12. 12.
    Wilhelm, F.H., Trabert, W., Roth, W.T.: Physiologic instability in panic disorder and generalized anxiety disorder. Biological Psychiatry 49, 596–605 (2001)CrossRefPubMedGoogle Scholar
  13. 13.
    Dinges, D.F., et al.: Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4-5 hours per night. Sleep 20, 267–277 (1997)PubMedGoogle Scholar
  14. 14.
    Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267–288 (1996)Google Scholar
  15. 15.
    Efron, B., et al.: Least Angle Regression. The Annals of Statistics 32, 407–451 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lance J. Myers
    • 1
  • J. Hunter Downs
    • 1
  1. 1.Archinoetics, LLC.HonoluluUSA

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