Spontaneous Pupillary Oscillation Signal Analysis Applying Hilbert Huang Transform

Fabiola M. Villalobos-Castaldi, José Ruiz-Pinales, Nicolás C. Kemper-Valverde, Mercedes Flores-Flores, Laura G. Ramírez-Sánchez, Metztli G. Ortiz-Hernández


This paper proposes a new application of the Hilbert-Huang transform (HHT). Pupillogram recordings were analyzed through the non-traditional HHT to investigate patterns in the time-frequency parameters of Spontaneous Pupillary Oscillation (SPO) signals. The traditional Fourier transform is only useful for linear stationary signals analysis, but the HHT was designed for the analysis of non-linear and non-stationary signals. However, the HHT is a more suitable tool to study SPO signals which are fundamentally non-stationary. The intrinsic properties of the Spontaneous Pupillary Oscillation signals were highlighted by the HHT scheme and the results showed that SPO waves present local and intermittent variations through the time span. The numerical parameters demonstrated that it is a wide inter-subject variation in the intrinsic time-frequency parameters contribution from each yielding mode to the total signal content.


  1. Barnhart, B. and Eichinger, W., (2011). Empirical Mode Decomposition applied to solar irradiance, global temperature, sunspot number, and CO2 concentration data. Journal of Atmospheric and Solar-Terrestrial Physics, 73(13), pp.1771-1779.
  2. Barnhart, B.L., (2011). The Hilbert-Huang Transform: theory, applications, development, PhD thesis, University of Iowa.
  3. Bouma, H., and Baghuis, L., (1971). Hippus of the pupil: Periods of slow oscillations of unknown origin. Vision Research, 11(11), 1345-1351.
  4. Boyina,S.R, Anu.H, Moneca.K, Mahalakshmi Malini.G, Priyadharshini.R, (2012). Pupil Recognition Using Wavelet And Fuzzy Linear Discriminant Analysis For Authentication, International Journal Of Advanced Engineering Technology, IJAET/Vol.III/ Issue II.
  5. Calcagnini, G., Censi, F., Lino, S., and Cerutti, S., (2000). Spontaneous fluctuations of human pupil reflect central autonomic rhythms. Methods Inf Med, 39(2), 142-145.
  6. Cerutti, S., (1997). Cardiovascular autonomic rhythms in spontaneous pupil fluctuations, Computers in Cardiology 1997 CIC-97.
  7. Cerutti, S., (1999). Baroceptor-sensitive fluctuations of human pupil diameter", Computers in Cardiology,Vol 26 (Cat No 99CH37004) CIC-99.
  8. Cerutti, S., (2000). Pupil diameter variability in humans, Proceedings of the 22nd Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society (Cat No 00CH37143) IEMBS-00.
  9. Cong, Z., (2009). Hilbert-Huang transform based physiological signals analysis for emotion recognition, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 12.
  10. De Souza Neto, E., Abry, P., Loiseau, P., Cejka, J., Custaud, M., Frutoso, J., Gharib, C. and Flandrin, P. (2007). Empirical mode decomposition to assess cardiovascular autonomic control in rats. Fundam Clin Pharmacol, 21(5), pp.481-496.
  11. Elsenbruch, S., (2000). Physiological Measurement, 05.
  12. Faust, O. and Bairy, M., (2012). Nonlinear analysis of physiological signals: a review. J. Mech. Med. Biol., 12(04), p.1240015.
  13. Flandrin, P., Rilling, G., and Goncalves, P., (2004). Empirical mode decomposition as a filter bank. Signal Processing Letters, IEEE, 11(2), 112-114.
  14. Goldwater, B., (1972). Psychological significance of pupillary movements. Psychological Bulletin, 77(5), pp.340-355.
  15. Heaver, B., (2012). Psychophysiological indices of recognition memory (Doctoral dissertation, University of Sussex).
  16. Hreidarsson, A. B., and Gundersen, H. J. G., (1988).Reduced Pupillary Unrest: Autonomic Nervous System Abnormality in Diabetes Mellitus, Diabetes.
  17. Huang, N. (2005). Empirical mode decomposition for analyzing acoustical signals. The Journal of the Acoustical Society Of America, 118(2), 593. http://dx.doi.org/10.1121/1.2040268.
  18. Huang, N. E., and Shen, S. S., (2005). Hilbert-Huang transform and its applications (Vol. 5). World Scientific.
  19. Huang, N. E., Long, S. R., and Shen, Z., (1996). The mechanism for frequency downshift in nonlinear wave evolution. Advances in applied mechanics, 32, 59- 117C.
  20. Huang, N. E., Shen, Z., and Long, S. R., (1999). A new view of nonlinear water waves: The Hilbert Spectrum 1. Annual review of fluid mechanics, 31(1), 417-457.
  21. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q. and Liu, H. H.,(1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Mathematical, Physical and Engineering Sciences (Vol. 454, No. 1971, pp. 903-995). The Royal Society.
  22. Huang, N., Wu, M., Qu, W., Long, S., and Shen, S., (2003). Applications of Hilbert-Huang transform to nonstationary financial time series analysis. Appl. Stochastic Models Bus. Ind., 19(3), 245-268.
  23. Huang, N.E., and Long, S., (2006). On the Normalized Hilbert Transform and Its Applications in Remote Sensing, Signal and Image Processing for Remote Sensing.
  24. Huang, N.E., (2003). Applications of Hilbert-Huang transform to non-stationary financial time series analysis, Applied Stochastic Models in Business and Industry, 7.
  25. Huang, Y., Schmitt, F. G., Lu, Z., and Liu, Y.,(2007). Empirical mode decomposition analysis of experimental homogeneous turbulence time series. In 21° Colloque GRETSI, Troyes, FRA, 11-14 septembre 2007. GRETSI, Groupe d'Etudes du Traitement du Signal et des Images.
  26. Jain, S., Siegle, G., Gu, C., Moore, C., Ivanco, L., Studenski, S., Greenamyre, J. and Steinhauer, S., (2011).Pupillary unrest correlates with arousal symptoms and motor signs in Parkinson disease. Movement Disorders, 26(7), pp.1344-1347.
  27. Khademul, I.M., (2006)."Empirical mode decomposition analysis of climate changes with special reference to rainfall data", Discrete Dynamics in Nature and Society.
  28. Longtin, A., (1989). Nonlinear Oscillations, Noise and Chaos in Neural Delayed Feedback department of Piysics, McGill University, Montréal, Canada.
  29. Lowenstein, O., (1950). Role of Sympathetic and Parasympathetic Systems in Reflex Dilatation of the Pupil. Archives of Neurology and Psychiatry, 64(3), 313.
  30. Lüdtke, H., Wilhelm, B., Adler, M., Schaeffel, F., and Wilhelm, H., (1998). Mathematical procedures in data recording and processing of pupillary fatigue waves. Vision Research, 38(19), 2889-2896.
  31. Malpas, S., (2010). Sympathetic Nervous System Overactivity and Its Role in the Development of Cardiovascular Disease. Physiological Reviews, 90(2), pp.513-557.
  32. McLaren, J. W., Erie, J. C., and Brubaker, R. F., (1992). Computerized analysis of pupillograms in studies of alertness. Invest. Ophthalmol. Vis. Sci, 33(3), 671-676.
  33. Naber, M., Alvarez, G., and Nakayama, K., (2013). Tracking the allocation of attention using human pupillary oscillations. Frontiers in Psychology, 4.
  34. Newman, S., (2008). Clinical Neuro-Ophthalmology. A Practical Guide. Journal of Neuro-Ophthalmology, 28(4), p.362.
  35. Nowak, W., Hachol, A. and Kasprzak, H., (2008). Timefrequency analysis of spontaneous fluctuation of the pupil size of the human eye. Optica Applicata, XXXVIII, No. 2, pp.269-280.
  36. Papoulis, A., and Saunders, H., (1989). Probability, Random Variables and Stochastic Processes (2nd Edition). J. Vib. Acoust. 111(1), 123.
  37. Pedrotti, M., Mirzaei, M., Tedesco, A., Chardonnet, J., Mérienne, F., Benedetto, S., and Baccino, T., (2014).Automatic Stress Classification with Pupil Diameter Analysis. International Journal of HumanComputer Interaction, 30(3), 220-236.
  38. Regen, F., Dorn, H., and Danker-Hopfe, H., (2013). Association between pupillary unrest index and waking electroencephalogram activity in sleep-deprived healthy adults. Sleep Medicine, 14(9), 902-912.
  39. Rosenberg, M. L., and Kroll, M. H., (1999). Pupillary hippus: an unrecognized example of biologic chaos. Journal of Biological Systems, 7(01), 85-94.
  40. Sylvain, S., and Brisson,J., (2014). "Pupillometry: Pupillometry", Wiley Interdisciplinary Reviews Cognitive Science.
  41. Villalobos-Castaldi, F., and Suaste-Gómez, E. (2013). A new spontaneous pupillary oscillation-based verification system. Expert Systems with Applications, 40(13), 5352-5362. http://dx.doi.org/10.1016/ j.eswa.2013.03.042.
  42. Warga, M., Lüdtke, H., Wilhelm, H., and Wilhelm, B., (2009). How do spontaneous pupillary oscillations in light relate to light intensity? Vision research, 49(3), 295-300.
  43. Wu, Z. and Huang, N.E., (2005).Statistical Significance Test of Intrinsic Mode Functions", Interdisciplinary Mathematical Sciences.

Paper Citation

in Harvard Style

Villalobos-Castaldi F., Ruiz-Pinales J., Kemper-Valverde N., Flores-Flores M., Ramírez-Sánchez L. and Ortiz-Hernández M. (2016). Spontaneous Pupillary Oscillation Signal Analysis Applying Hilbert Huang Transform . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 67-77. DOI: 10.5220/0005697400670077

in Bibtex Style

author={Fabiola M. Villalobos-Castaldi and José Ruiz-Pinales and Nicolás C. Kemper-Valverde and Mercedes Flores-Flores and Laura G. Ramírez-Sánchez and Metztli G. Ortiz-Hernández},
title={Spontaneous Pupillary Oscillation Signal Analysis Applying Hilbert Huang Transform},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},

in EndNote Style

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Spontaneous Pupillary Oscillation Signal Analysis Applying Hilbert Huang Transform
SN - 978-989-758-170-0
AU - Villalobos-Castaldi F.
AU - Ruiz-Pinales J.
AU - Kemper-Valverde N.
AU - Flores-Flores M.
AU - Ramírez-Sánchez L.
AU - Ortiz-Hernández M.
PY - 2016
SP - 67
EP - 77
DO - 10.5220/0005697400670077