The Hurst Exponent: A Novel Approach for Assessing Focus During Trauma Resuscitation

  • Ikechukwu P. Ohu
  • Jestin N. Carlson
  • Davide PiovesanEmail author


Current assessment of resuscitation team performance is often based on evaluations using checklists that evaluate verbal communication. However, highly efficient teams may function with several non-verbal cues that may not be measured by current assessment methods. Previous work assessing these non-verbal cues has been accomplished by tracking head movements in providers which however have not been attempted in trauma teams. We sought to perform a preliminary, proof-of-concept study to assess the ability to perform head tracking during a simulated trauma scenario. We enrolled a convenience sample of two simulated trauma teams utilizing undergraduate health professional students from four disciplines available at our institution: 2nd year Radiologic Science (RS), 4th year Physician Assistant (PA), 2nd year Respiratory Care (RT), and 4th year Registered Nurse (RN) students. Each team performed a simulated trauma resuscitation two times while wearing Xsens® MTw motion trackers to track head movements during the resuscitation. These motions were analyzed using a standard measure of discriminating movement patterns known as the Hurst exponent (H). Pre- and post- communication training movement patterns were compared to establish reliability of H in trainees learning trauma resuscitation. There was no difference between the pre- and post- communication training H values for either roll or yaw for any of the four disciplines indicating that non-verbal communications were avoided. The Hurst exponent reliably measures the direction of focus of the participants during some simulated trauma resuscitation scenarios. Future research will be needed to evaluate this analytic technique across providers and in the clinical setting.


Hurst exponent Simulated trauma Resuscitation Hidden team-based assessment TeamSTEPPS 



This work was supported by an educational research grant from the Society for Academic Emergency Medicine (SAEM). We would like to thank SAEM for funding this work.


  1. 1.
    Classen, D. C., Resar, R., Griffin, F., Federico, F., Frankel, T., Kimmel, N., et al. (2011). ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Affairs (Millwood), 30, 581–589. Scholar
  2. 2.
    O’Connor, P. J., Sperl-Hillen, J. A. M., Johnson, P. E., & Rush, W. A. (2005). Clinical inertia and outpatient medical errors. In K. Henriksen, J. B. Battles, E. S. Marks, & D. I. Lewin (Eds.), Advances in patient safety: From research to implementation. Rockville, MD: Agency for Healthcare Research and Quality (US).Google Scholar
  3. 3.
    Neily, J., Mills, P. D., Young-Xu, Y., Carney, B. T., West, P., Berger, D. H., et al. (2010). Association between implementation of a medical team training program and surgical mortality. Journal of the American Medical Association, 304, 1693–1700. Scholar
  4. 4.
    Reagans, R., Argote, L., & Brooks, D. (2005). Individual experience and experience working together: Predicting learning rates from knowing who knows what and knowing how to work together. Management Science, 51, 869–881.CrossRefGoogle Scholar
  5. 5.
    Carlson, J. N., Das, S., De la Torre, F., Callaway, C. W., Phrampus, P. E., & Hodgins, J. (2012). Motion capture measures variability in laryngoscopic movement during endotracheal intubation: A preliminary report. Simulation in Healthcare, 7, 255–260. Scholar
  6. 6.
    Carlson, J. N., Quintero, J., Guyette, F. X., Callaway, C. W., & Menegazzi, J. J. (2012). Variables associated with successful intubation attempts using video laryngoscopy: A preliminary report in a helicopter emergency medical service. Prehospital Emergency Care, 16, 293–298. Scholar
  7. 7.
    Saeb, S., Weber, C., & Triesch, J. (2011). Learning the optimal control of coordinated eye and head movements. PLoS Computational Biology, 7(11), e1002253.CrossRefGoogle Scholar
  8. 8.
    Ramseyer, F., & Tschacher, W. (2014). Nonverbal synchrony of head- and body-movement in psychotherapy: Different signals have different associations with outcome. Frontiers in Psychology, 5, 979. Scholar
  9. 9.
    Ohu, I., Cho, S., Zihni, A., Cavallo, J. A., & Awad, M. M. (2015). Analysis of surgical motions in minimally invasive surgery using complexity theory. International Journal of Biomedical Engineering and Technology, 17, 24–41. Scholar
  10. 10.
    Melendez-Calderon, A., Komisar, V., & Burdet, E. (2015). Interpersonal strategies for disturbance attenuation during a rhythmic joint motor action. Physiology & Behavior, 147, 348–358. Scholar
  11. 11.
    Melendez-Calderon, A., Komisar, V., Ganesh, G., & Burdet, E. (2011, August 30-September 3). Classification of strategies for disturbance attenuation in human-human collaborative tasks. Paper presented at the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Google Scholar
  12. 12.
    Reed, K., Peshkin, M., Hartmann, M. J., Grabowecky, M., Patton, J., & Vishton, P. M. (2006). Haptically linked dyads: Are two motor-control systems better than one? Psychological Science, 17(5), 365–366. Scholar
  13. 13.
    Takagi, A., Beckers, N., & Burdet, E. (2016). Motion plan changes predictably in dyadic reaching. PLoS one, 11(12), e0167314.CrossRefGoogle Scholar
  14. 14.
    Rattenborg Niels, C. (2017). Sleeping on the wing. Interface Focus, 7(1), 20160082. Scholar
  15. 15.
    Stahl, J. S. (2001). Eye-head coordination and the variation of eye-movement accuracy with orbital eccentricity. Experimental Brain Research, 136(2), 200–210. Scholar
  16. 16.
    Kim, S.-Y., Moon, B.-Y., & Cho, H. G. (2016). Smooth-pursuit eye movements without head movement disrupt the static body balance. Journal of Physical Therapy Science, 28(4), 1335–1338. Scholar
  17. 17.
    Piovesan, D., Melendez-Calderon, A., & Mussa-Ivaldi, F. A. (2013). Haptic recognition of dystonia and spasticity in simulated multi-joint hypertonia. IEEE International Conference on Rehabilitation Robotics, 2013, 6650449. Scholar
  18. 18.
    Wang, H. E., Schmicker, R. H., Daya, M. R., Stephens, S. W., Idris, A. H., Carlson, J. N., et al. (2018). Effect of a strategy of initial laryngeal tube insertion vs endotracheal intubation on 72-hour survival in adults with out-of-hospital cardiac arrest: A randomized clinical trial. JAMA, 320(8), 769–778.CrossRefGoogle Scholar
  19. 19.
    Ho, K. K. L., Moody, G. B., Peng, C.-K., Mietus, J. E., Larson, M. G., Levy, D., & Goldberger, A. L. (1997). Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation, 96(3), 842–848. Scholar
  20. 20.
    Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049. Scholar
  21. 21.
    Omidvarnia, A., Mesbah, M., Pedersen, M., & Jackson, G. (2018). Range entropy: A bridge between signal complexity and self-similarity. Entropy, 20(12), 962. Retrieved from Scholar
  22. 22.
    Pearlmutter, B. A. (1989). Learning state space trajectories in recurrent neural networks. Neural Computation, 1(2), 263–269.CrossRefGoogle Scholar
  23. 23.
    Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a time series. Physical Review Letters, 45(9), 712.CrossRefGoogle Scholar
  24. 24.
    De la Fuente, I., Martinez, L., Aguirregabiria, J., & Veguillas, J. (1998). R/S analysis strange attractors. Fractals, 6(2), 95–100.CrossRefGoogle Scholar
  25. 25.
    Torre, K., & Balasubramaniam, R. (2011). Disentangling stability, variability and adaptability in human performance: Focus on the interplay between local variance and serial correlation. Journal of Experimental Psychology: Human Perception and Performance, 37(2), 539.Google Scholar
  26. 26.
    Golomb, D., Hansel, D., Shraiman, B., & Sompolinsky, H. (1992). Clustering in globally coupled phase oscillators. Physical Review A, 45(6), 3516.CrossRefGoogle Scholar
  27. 27.
    Crowley, P. (1992). Density dependence, boundedness, and attraction: Detecting stability in stochastic systems. Oecologia, 90(2), 246–254.CrossRefGoogle Scholar
  28. 28.
    Radii, R., & Politi, A. (1985). Statistical description of chaotic attractors: The dimension function. Journal of Statistical Physics, 40(5–6), 725–750.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Cajueiro, D. O., & Tabak, B. M. (2005). The rescaled variance statistic and the determination of the Hurst exponent. Mathematics and Computers in Simulation, 70(3), 172–179.MathSciNetCrossRefGoogle Scholar
  30. 30.
    Gorman, J. C., Hessler, E. E., Amazeen, P. G., Cooke, N. J., & Shope, S. M. (2012). Dynamical analysis in real time: Detecting perturbations to team communication. Ergonomics, 55(8), 825–839. Scholar
  31. 31.
    Klauer, S. G., Olsen, E. C., Simons-Morton, B. G., Dingus, T. A., Ramsey, D. J., & Ouimet, M. C. (2008). Detection of road hazards by novice teen and experienced adult drivers. Transportation Research Record Journal, 2078, 26–32. Scholar
  32. 32.
    Baker, V. O. T., Cuzzola, R., Knox, C., Liotta, C., Cornfield, C. S., Tarkowski, R. D., et al. (2015). Teamwork education improves trauma team performance in undergraduate health professional students. Journal of Educational Evaluation for Health Professions, 12, 36–36. Scholar
  33. 33.
    Ohu, I. P., Piovesan, D., & Carlson, J. N. (2018, December 1). The Hurst exponent—A novel approach for assessing focus during trauma resuscitation. Paper presented at the 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).Google Scholar
  34. 34.
    Qian, B., & Rasheed, K. (2004). Hurst exponent and financial market predictability. Paper presented at the IASTED conference on Financial Engineering and Applications.Google Scholar
  35. 35.
    Anis, A. A., & Lloyd, E. H. (1976). The expected value of the adjusted rescaled Hurst range of independent Normal summands. Biometrika, 63(1), 111–116.Google Scholar
  36. 36.
    Peters, E. E. (1994). John Wiley & Sons.Google Scholar
  37. 37.
    Baylis, J., Fernando, S., Szulewski, A., & Howes, D. (2013). Data gathering in resuscitation scenarios: Novice versus expert physicians. Canadian Journal of Emergency Medicine, 15(1).Google Scholar
  38. 38.
    Chapman, P. R., & Underwood, G. (1998). Visual search of driving situations: Danger and experience. Perception, 27(8), 951–964.Google Scholar
  39. 39.
    Christenson, J., et al. (2009). Resuscitation outcomes consortium investigators: Chest compression fraction determines survival in patients with out-of-hospital ventricular fibrillation. Circulation, 120(13), 1241–1247.CrossRefGoogle Scholar
  40. 40.
    Torab, P., & Piovesan, D. (2015). Vibrations of fractal structures: On the nonlinearities of damping by branching. Journal of Nanotechnology in Engineering and Medicine, 6(3), 034502.CrossRefGoogle Scholar
  41. 41.
    Capella, J., Smith, S., Philp, A., Putnam, T., Gilbert, C., Fry, W., et al. (2010). Teamwork training improves the clinical care of trauma patients. Journal of Surgical Education, 67(6), 439–443.CrossRefGoogle Scholar
  42. 42.
    Gorman, J. C., Dunbar, T. A., Grimm, D., & Gipson, C. L. (2017). Understanding and modeling teams as dynamical systems. Frontiers in Psychology, 8, 1053.CrossRefGoogle Scholar
  43. 43.
    Mattei, T. A. (2014). Unveiling complexity: Non-linear and fractal analysis in neuroscience and cognitive psychology. Frontiers in Computational Neuroscience, 8, 17.CrossRefGoogle Scholar
  44. 44.
    Likens, A. D., Amazeen, P. G., Stevens, R., Galloway, T., & Gorman, J. C. (2014). Neural signatures of team coordination are revealed by multifractal analysis. Social Neuroscience, 9(3), 219–234. Scholar
  45. 45.
    Stevens, R. (2014). Modeling the neurodynamics of submarine piloting and navigation teams. Retrieved fromGoogle Scholar
  46. 46.
    Masters, C., Baker, V. O. T., & Jodon, H. (2013). Multidisciplinary, team-based learning: The simulated interdisciplinary to multidisciplinary progressive-level education (SIMPLE©) approach. Clinical Simulation in Nursing, 9(5), e171–e178. CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ikechukwu P. Ohu
    • 1
    • 2
  • Jestin N. Carlson
    • 2
    • 3
  • Davide Piovesan
    • 1
    • 2
    Email author
  1. 1.Biomedical Industrial and Systems Engineering DepartmentGannon UniversityErieUSA
  2. 2.Patient Simulation CenterMorosky College of Health Professions and Sciences, Gannon UniversityErieUSA
  3. 3.Department of Emergency MedicineSaint Vincent Health SystemErieUSA

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