Download PDF
Methods Inf Med 2003; 42(03): 282-286
DOI: 10.1055/s-0038-1634362
Original article
Schattauer GmbH

Detection of Juvenile Sleep Deprivation by Stochastic Optimization of Pupillographic Records

W. O’Neill
1   Bioengineering Department, University of Illinois at Chicago
,
P. Mercer
2   Sleep Medicine Center, Children’s Memorial Hospital, Northwestern University Medical School
,
S. Sheldon
2   Sleep Medicine Center, Children’s Memorial Hospital, Northwestern University Medical School
,
T. Kotsos
1   Bioengineering Department, University of Illinois at Chicago
› Author Affiliations
Further Information

Publication History

Received 17 June 2002

Accepted 17 December 2002

Publication Date:
07 February 2018 (online)

    Permissions and Reprints

Summary

Objective: To address the challenging problem of measuring juvenile sleep deprivation, we test the hypothesis that a pupillographic method found successful for adult narcoleptics might also discriminate between sleep deprived juveniles acting as their own controls.

Methods: A linear, nonstationary model relating pupillary diameter and a random photic stimulus are estimated by recursive regressions from pupillographic records of 8 juveniles of median age 7 years acting as their own rested controls. The estimated pupillary impulse response noise functions are stochastically optimized using the Kullback divergence measure to maximally separate the sleep deprived records from the control records.

Results: Both the average and covariance statistics of the estimated pupillary noise functions exhibit statistically significant differences between sleep deprived and rested subjects. The main result is that sleep deprivation decreases pupillary noise variance; a finding consistent with a previous study of adult narcoleptics. Further, it was found that virtually the same stochastic parameters were optimal for the juvenile sleep deprived data and for the previous adult narcoleptic study.

Conclusions: Although our results are preliminary, the consistent reduction of pupillary noise appears to justify a comprehensive clinical trial across a broad range of age classes. In addition, the finding that the same parameters stochastically optimze both juvenile and adult recordings suggests the procedure holds promise as a clinical test which could produce sleep deprivation measures simultaneous with data collection.

Keywords

Sleep deprivation - juveniles - pupillography - stochastic optimization.
 

  • References

  • 1 Gozel D. Sleep disordered breathing and school performance in children. Pediatrics 1998; 102: 616-20.
    CrossrefPubMedGoogle Scholar
  • 2 Wiggs L, Stores G. Behavioural treatment for sleep problems in children with severe learning disabilities and challenging daytime behaviour: Effect on sleep patterns of mother and child. J. Sleep Res. 1998; 7: 119-26.
    CrossrefPubMedGoogle Scholar
  • 3 Johnstone S. et al. Nasal bilevel positive airway pressure therapy in children with a sleep-related breathing disorder and attention-deficit hyperactivity disorder: effects on electro-physiological measures of brain function. Sleep Med 2001; 2: 407-16.
    CrossrefPubMedGoogle Scholar
  • 4 O’Neill W. et al Pupillary noise and narcolepsy. J. Sleep Res. 1996; 5: 265-71.
    CrossrefPubMedGoogle Scholar
  • 5 O’Neill W, and Trick K. The narcoleptic cognitive pupillary response. IEEE Trans Biomed Eng 2001; 48: 963-8.
    CrossrefPubMedGoogle Scholar
  • 6 O’Neill W, Zimmerman S. Neurological interpretations and the information in the cognitive pupillary response. Methods and Inform in Med 2000; 39: 122-4.
    PubMedGoogle Scholar
  • 7 Wilhelm B. et al Daytime variations in central nervous system activation measured by a pupillographic sleepiness test. J. Sleep Res 2001; 10: 1-18.
    CrossrefPubMedGoogle Scholar
  • 8 Ranzijn R, Lack L. The pupillary light reflex cannot be used to measure sleeepiness. Psycho-physiol. 1997; 34: 17-22.
    CrossrefPubMedGoogle Scholar
  • 9 Sillito A, Zbrozyna A. The localization of the pupilloconstrictor function in the mid-brain of the cat. J. Physiol. 1970; 211: 461-3.
    CrossrefPubMedGoogle Scholar
  • 10 Smith J. et al. Midbrain single units correlation with the PLR. Science 1968; 162: 1302-3.
    CrossrefPubMedGoogle Scholar
  • 11 Stanton S, Stark L. A statistical analysis of pupil noise. IEEE Trans. Biomed Eng. 1966; 13: 140-49.
    PubMedGoogle Scholar
  • 12 Stark L, and Cornsweet T. Testing a servoanalytic hypothesis for pupil oscillations. Science 1958; 127: 588-92.
    PubMedGoogle Scholar
  • 13 Usui S, Stark L. A model for nonlinear stochastic behavior of the pupil. Biol Cyber 1982; 45: 13-21.
    CrossrefPubMedGoogle Scholar
  • 14 Ljung L. System Identification: Theory for the User,. Prentice-Hall; Upper Saddle River, N.J.: 1999
    Google Scholar
  • 15 Kullback S. Information Theory and Statistics. Dover, New York: 1968
    Google Scholar
  • 16 Wilks SS. Mathematical Statistics,. Wiley and Sons; New York: 1962
    Google Scholar