Detection of juvenile sleep deprivation by stochastic optimization of pupillographic records.
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 optimize both juvenile and adult recordings suggests the procedure holds promise as a clinical test which could produce sleep deprivation measures simultaneous with data collection.
O'Neill
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Bioengineering Department, University of Illinois at Chicago, Chicago, IL 60607, USA. woneill@usc.edu
Mercer
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P
Sheldon
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Kotsos
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D000465:Algorithms; D002648:Child; D006801:Humans; D011681:Pupil Disorders / Q000503:physiopathology*; D012892:Sleep Deprivation / Q000175:diagnosis* / Q000503:physiopathology; D013269:Stochastic Processes; D014481:United States