Nonlinear analysis of pupillary dynamics

Biomed Tech (Berl). 2016 Feb;61(1):95-106. doi: 10.1515/bmt-2015-0027.


Pupil size reflects autonomic response to different environmental and behavioral stimuli, and its dynamics have been linked to other autonomic correlates such as cardiac and respiratory rhythms. The aim of this study is to assess the nonlinear characteristics of pupil size of 25 normal subjects who participated in a psychophysiological experimental protocol with four experimental conditions, namely “baseline”, “anger”, “joy”, and “sadness”. Nonlinear measures, such as sample entropy, correlation dimension, and largest Lyapunov exponent, were computed on reconstructed signals of spontaneous fluctuations of pupil dilation. Nonparametric statistical tests were performed on surrogate data to verify that the nonlinear measures are an intrinsic characteristic of the signals. We then developed and applied a piecewise linear regression model to detrended fluctuation analysis (DFA). Two joinpoints and three scaling intervals were identified: slope α0, at slow time scales, represents a persistent nonstationary long-range correlation, whereas α1 and α2, at middle and fast time scales, respectively, represent long-range power-law correlations, similarly to DFA applied to heart rate variability signals. Of the computed complexity measures, α0 showed statistically significant differences among experimental conditions (p<0.001). Our results suggest that (a) pupil size at constant light condition is characterized by nonlinear dynamics, (b) three well-defined and distinct long-memory processes exist at different time scales, and (c) autonomic stimulation is partially reflected in nonlinear dynamics.

MeSH terms

  • Algorithms
  • Autonomic Nervous System / physiology*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Diagnostic Techniques, Ophthalmological*
  • Emotions / physiology*
  • Humans
  • Nonlinear Dynamics*
  • Pattern Recognition, Automated / methods*
  • Pupil / physiology*
  • Regression Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity