Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation

Frontiers in Neuroengineering

Volumn , Issue FEBRUARY, 2012, Pages

  Valenza, Gaetano ^a   Allegrini, Paolo ^b   Lanatà, Antonio ^a   Scilingo, Enzo Pasquale ^a  

^a UNIVERSITY OF PISA   (Italy)

^b INSTITUTE OF CLINICAL PHYSIOLOGY   (Italy)

Author keywords

Affective computing; Approximate entropy; Dominant lyapunov exponent; Emotion recognition; Heart rate variability; Non linear analysis

Indexed keywords

ADULT; AFFECT; AROUSAL; ARTICLE; CONTROLLED STUDY; EMOTIONALITY; ENTROPY; HEART RATE VARIABILITY; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; STIMULUS RESPONSE; VISUAL STIMULATION;

EID: 84857848745     PISSN: 16626443     EISSN: None     Source Type: Journal    
DOI: 10.3389/fneng.2012.00003     Document Type: Article

References (53)

1. Atyabi, F., Livari, M., Kaviani, K., and Tabar, M. (2006). Two statistical methods for resolving healthy individuals and those with congestive heart failure based on extended selfsimilarity and a recursive method. J. Biol. Phys. 32, 489-495.
2. Berger, R., Akselrod, S., Gordon, D., and Cohen, R. (1986). An efficient algorithm for spectral analysis of heart rate variability. IEEE Trans. Biomed. Eng. 33, 900-904.
3. Berntson, G. G., Bigger, J. T. Jr, Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., Nagaraja, H. N., Porges, S. W., Saul, J. P., Stone, P. H., and van der Molen, M. W. (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34, 623-648.
4. Breazeal, C. (2003). Emotion and sociable humanoid robots. Int. J. Hum. Comput. Stud. 59, 119-155.
5. Casdagli, M. (1989). Nonlinear prediction of chaotic time series. Physica D 35, 335-356.
6. Duffy, F., Chung, H., Trivedi, M., Rae, D., Regier, D., and Katzelnick, D. (2008). Systematic use of patient-rated depression severity monitoring: is it helpful and feasible in clinical psychiatry? Psychiatr. Serv. 59, 1148-1154.
7. Eckmann, J., Kamphorst, S., and Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhys. Lett. 4, 973.
8. Egges, A., Kshirsagar, S., and Magnenat-Thalmann, N. (2003). "A model for personality and emotion simulation," in KnowledgeBased Intelligent Information and Engineering Systems, eds V. Palade, R. J. Howlett, and L. C. Jain (Springer), 453-461.
9. Ekman, P. (1999). "Basic emotions," in Handbook of Cognition and Emotion, eds T. Dalgleish, and M. Power (Sussex, UK: John Wiley & Sons), 45-60.
10. Ellner, S., Gallant, A. R., McCaffrey, D., and Nychka, D. (1991). Convergence rates and data requirements for Jacobian-based estimates of Lyapunov exponents from data. Phys. Lett. A 153, 357-363.
11. Farmer, J., and Sidorowich, J. (1987). Predicting chaotic time series. Phys. Rev. Lett. 59, 845-848.
12. Fusheng, Y., Bo, H., and Qingyu, T. (2000). Approximate Entropy and its application in biosignal analysis. Nonlin. Biomed. Signal Process. 2, 72.
13. Garcia-Gonzalez, M., Fernández-Chimeno, M., and Ramos-Castro, J. (2009). Errors in the estimation of approximate entropy and other recurrence-plot-derived indices due to the finite resolution of RR time series. IEEE Trans. Biomed. Eng. 56, 345-351.
14. Glass, L. (2001). Synchronization and rhythmic processes in physiology. Nature 410, 277-284.
15. Grandjean, D., Sander, D., and Scherer, K. (2008). Conscious emotional experience emerges as a function of multilevel, appraisaldriven response synchronization. Conscious. Cogn. 17, 484-495.
16. Grimm, S., Schmidt, C., Bermpohl, F., Heinzel, A., Dahlem, Y., Wyss, M., Hell, D., Boesiger, P., Boeker, H., and Northoff, G. (2006). Segregated neural representation of distinct emotion dimensions in the prefrontal cortex-an fMRI study. Neuroimage 30, 325-340.
17. Hagerman, I., Berglund, M., Lorin, M., Nowak, J., and Sylvén, C. (1996). Chaos-related deterministic regulation of heart rate variability in time- and frequency domains: effects of autonomic blockade and exercise. Cardiovasc. Res. 31, 410.
18. Hariri, A., Mattay, V., Tessitore, A., Fera, F., and Weinberger, D. (2003). Neocortical modulation of the amygdala response to fearful stimuli. Biol. Psychiatry 53, 494-501.
19. Kantz, H., Schreiber, T., and Mackay, R. (1997). Nonlinear Time Series Analysis, Vol. 2000. Cambridge, MA: Cambridge University Press.
20. Katsis, C., Katertsidis, N., and Fotiadis, D. (2010). An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomed. Signal Process. Control 6, 261-268.
21. Kim, J., and André, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2067-2083.
22. Kobayashi, M., and Musha, T. (2007). 1/f fluctuation of heartbeat period. IEEE Trans. Biomed. Eng. 29, 456-457.
23. Kohler, B., Hennig, C., and Orglmeister, R. (2002). The principal of Software QRS Detection. IEEEEng. Med. Biol. 6, 42-57.
24. Kroenke, K., Spitzer, R., and Williams, J. (2003). The patient health questionnaire-2: validity of a twoitem depression screener. Med. Care 41, 1284.
25. Kruskal, W. H., and Wallis, W. A. (1952). Use of ranks in onecriterion variance analysis. J. Am. Stat. Assoc. 583-621.
26. Lang, P. J. (1980). "Behavioral treatment and bio-behavioral assessment: computer applications," in Technology in Mental Health Care Delivery Systems, eds J. B. Sidowski, J. H. Johnson, and T. A. Williams (Norwood, NJ: Ablex), 119-137.
27. Lang, P., Bradley, M., and Cuthbert, B. (2005). International affective picture system (IAPS): Digitized photographs, instruction manual and affective ratings. Technical report A-6. University of Florida.
28. Lang, P., Greenwald, M., Bradley, M., and Hamm, A. (1993). Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30, 261-273.
29. Lippman, N., Stein, K., and Lerman, B. (1993). Nonlinear predictive interpolation. A new method for the correction of ectopic beats for heart rate variability analysis. J. Electrocardiol. 26, 14.
30. Lippman, N., Stein, K., and Lerman, B. (1994). Comparison of methods for removal of ectopy in measurement of heart rate variability. Am. J. Physiol. Heart Circ. Physiol. 267, H411.
31. Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., and Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 66, 26702.
32. Ortony, A., Clore, G., and Collins, A. (1990). The Cognitive Structure of Emotions. Cambridge: Cambridge University Press.
33. Paleari, M., Chellali, R., and Huet, B. (2010). "Features for multimodal emotion recognition: an extensive study," in Cybernetics and Intelligent Systems (CIS), 2010 IEEE Conference On. (Singapore: IEEE), 90-95.
34. Pan, J., and Tompkins, W. (1985). A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230-236.
35. Peng, C., Havlin, S., Hausdorff, J., Mietus, J., Stanley, H., and Goldberger, A. (1995). Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease. J. Electrocardiol. 28, 59-65.
36. Picard, R. (2000). Affective Computing. Cambridge: The MIT Press.
37. Picard, R. (2010). Emotion research by the people, for the People. Emot. Rev. 2, 250.
38. Picard, R., Vyzas, E., and Healey, J. (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1175-1191.
39. Pincus, S. (1991). Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U.S.A. 88, 2297.
40. Poon, C., and Merrill, C. (1997). Decrease of cardiac chaos in congestive heart failure. Nature 389, 492-495.
41. Posner, J., Russell, J., and Peterson, B. (2005). The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715-734.
42. Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C., and Suri, J. (2006). Heart rate variability: a review. Med. Biol. Eng. Comput. 44, 1031-1051.
43. Richman, J., and Moorman, J. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039.
44. Rosenstein, M., Collins, J., and De Luca, C. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117-134.
45. Ruelle, D. (1979). Sensitive dependence on initial condition and turbulent behavior of dynamical systems. Ann. N.Y. Acad. Sci. 316, 408-416.
46. Russell, J. (1980). A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161-1178.
47. Sato, S., Sano, M., and Sawada, Y. (1987). Practical methods of measuring the generalized dimension and the largest Lyapunov exponent in high dimensional chaotic systems. Prog. Theor. Phys. 77, 1-5.
48. Saul, J. (1990). Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. Physiology 5, 32.
49. Takens, F. (1981). "Detecting strange attractors in turbulence," in Dynamical Systems and Turbulence (Warwick 1980), 898, 366-381.
50. Tarvainen, M., Ranta-Aho, P., and Karjalainen, P. (2002). An advanced detrending method with application to hrv analysis. IEEE Trans. Biomed. Eng. 49, 172-175.
51. Valenza, G., Lanata, A., and Scilingo, E. (2011). "The role of nonlinear dynamics in affective valence and arousal recognition," Affective Computing, IEEE Transactions On, 1-1.
52. West, B., Geneston, E., and Grigolini, P. (2008). Maximizing information exchange between complex networks. Phys. Rep. 468, 1-99.
53. Wolf, A., Swift, J., Swinney, H., and Vastano, J. (1985). Determining Lyapunov exponents from a time series. Physica D 16, 285-317.