Comparing postural stability analyses to differentiate fallers and

Comparing postural stability analyses to differentiate fallers and

Comparing postural stability analyses to differentiate fallers and non-fallers ESM 6984: Frontiers in Dynamical Systems Final presentation Sponsor: Dr. Lockhart Team Members: Khaled Adjerid, Peter Fino, Mohammad Habibi, Ahmad Rezaei Fall risk assessment The injuries due to fall and slip pose serious problems to human life. Risk worsens with age Hip fractures and slips 15,400 American deaths $43.8 billion annually Technical approach How can we assess fall risk in the elderly? Walking and balance is complex Multiple mechanisms involved in slip and fall

Most assessment focused on age Prediction of fall is still a big challenge in human factor science. What data do we actually have? 60 second postural stability COP data Eyes open Eyes closed 41 fallers and 78 non-fallers Fallers categorized by one or more falls in past 12 months Average age: 76.3 7.4 Time Series Analysis Several methods have been developed for complexity and recurrence measures in time series: Shannon entropy (ShanEn) State Entropies

Renyi entropy (RenyEn) Approximate entropy (ApEn) Sample entropy (SaEn) Multiscale entropy (MSE) Composite multiscale entropy (CompMSE) Recurrence quantification analysis (RQAEn) Detrended fluctuation analysis (DFA) Sequence Entropies Input parameters were based of those used in throughout the literature for similar studies Complexity Method Acronym Type of Entropy Input Parameters

Index Renyi Entropy RenyEn State - =2,M Shannon Entropy ShanEn State - = 1, M Approximate Entropy ApEn Sequence - r = 0.2 std, m = 3 Sample Entropy SaEn Sequence -

r = 0.2 std, m =3 r = 0.2 std, m = 3, = 1, Multi-Scale Entropy MSE Sequence Slope and Area ,10 Composite Multi-scale r = 0.2 std, m = 3, = 1, CompMSE Sequence Slope and Area Entropy ,10 Recurrence Quantification m = 8, T = 6, RQAEn Analysis Entropy Sequence 0.30*meanmean =

Prior to analyzing, data was converted from 2D to 1D time series The Following Decision making process was adopted to test sensitivity (=0.05) of methods=0.05) of methods) of methods COP Data eyes closed vs eyes open Sensitivity to differences between method Significant difference Not significant difference, throw out method Comparison to prediction vs actual falls Comparison to previous methods Method Measure Status Angle

Radius Angle Radius Angle Slope Angle Area Radius Area Radius Slope Angle Area Angle Slope Radius Area

Radius Slope Angle Radius ShanEn Entropy RenyEn Entropy ApEn Eyes open vs Eyes close SaEn CompMSE MSE RQAEn

Method Measure Status Angle Radius Angle Radius Angle Slope Angle Area Radius Area Radius Slope

Angle Area Angle Slope Radius Area Radius Slope Angle Radius ShanEn Entropy RenyEn Entropy ApEn Fallers

vs nonfallers SaEn CompMSE MSE RQAEn Conclusion ShaEn could not detect eyes open and eyes close. SampEn, MSE and CompMSE could detect fallers and non-fallers. We showed increase in complexity among fallers Costa et al 2007 showed decrease in complexity among fallers Ramdani et al 2013 found a difference between fallers and nonfallers using RQAEn. We used radius and angle but previous studies used x and y coordinates. Previous studies had limited sample size (14 fallers) while in our

study we had robust sample size (41 fallers and 78 non-fallers) We recommend MSE and CompMSE for postural entropy analysis. Future works Statistical significance between certain groups within each method Obese vs normal BMI Medications Repeatability of each method with different data sets QUESTIONS? References 1. Maki, B.E., P.J. Holliday, and A.K. Topper, A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. Journal of Gerontology, 1994. 49(2): p. M72-M84. 2. Bergland, A., G.-B. Jarnlo, and K. Laake, Predictors of falls in the elderly by location. Aging clinical and experimental research, 2003. 15(1): p. 43-50. 3. Boulgarides, L.K., et al., Use of clinical and impairment-based tests to predict falls by community-dwelling older adults. Physical Therapy, 2003. 83(4): p. 328-339. 4. Norris, J.A., et al., Ability of static and statistical mechanics posturographic measures to distinguish between age and fall risk. Journal of biomechanics, 2005. 38(6): p. 1263-1272. 5. Thapa, P.B., et al., Clinical and biomechanical measures of balance fall predictors in ambulatory nursing home residents. The Journals of Gerontology Series A:

Biological Sciences and Medical Sciences, 1996. 51(5): p. M239-M246. 6. Pajala, S., et al., Force platform balance measures as predictors of indoor and outdoor falls in community-dwelling women aged 6376 years. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2008. 63(2): p. 171-178. 7. Piirtola, M. and P. Era, Force platform measurements as predictors of falls among older peoplea review. Gerontology, 2006. 52(1): p. 1-16. 8. Borg, F.G. and G. Laxback, Entropy of balance- some recent results. Journal of neuroengineering and rehabilitation, 2010. 7: p. 38-38. 9. Costa, M., et al., Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonancebased therapy. EPL (Europhysics Letters), 2007. 77(6): p. 68008. 10. Ramdani, S., et al., Recurrence quantification analysis of human postural fluctuations in older fallers and non-fallers. Annals of biomedical engineering, 2013. 41(8): p. 1713-1725. 11. Gao, J., et al., Shannon and Renyi entropies to classify effects of mild traumatic brain injury on postural sway. PloS One, 2011. 6(9): p. e24446. 12. Shannon, C.E., A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 2001. 5(1): p. 3-55. 13. Pincus, S.M., Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991. 88(6): p. 2297-301. 14. Richman, J.S. and J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000. 278(6): p. H2039-49. References 15. Lake, D.E., et al., Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2002. 283(3): p. R789-R797. 16. Costa, M., A.L. Goldberger, and C.-K. Peng, Multiscale entropy analysis of complex physiologic time series. Physical review letters, 2002. 89(6): p. 068102. 17. Liu, Q., et al., Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery. Entropy, 2012. 14(6): p. 978-992. 18. Wu, S.-D., et al., Time Series Analysis Using Composite Multiscale Entropy. Entropy, 2013. 15(3): p. 1069-1084. 19. Marwan, N., et al., Recurrence plots for the analysis of complex systems. Physics Reports, 2007. 438(5): p. 237-329.

20. 973. Webber, C. and J.P. Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology, 1994. 76(2): p. 965- 21. Rhea, C.K., et al., Noise and complexity in human postural control: Interpreting the different estimations of entropy. PloS one, 2011. 6(3): p. e17696. 22. Lord, S.R. and H.B. Menz, Visual contributions to postural stability in older adults. Gerontology, 2000. 46(6): p. 306-310. 23. Chiari, L., L. Rocchi, and A. Cappello, Stabilometric parameters are affected by anthropometry and foot placement. Clinical Biomechanics, 2002. 17(9): p. 666-677. 24. Ihara, S., Information theory for continuous systems. Vol. 2. 1993: World Scientific. 25. 2004. Bromiley, P., N. Thacker, and E. Bouhova-Thacker, Shannon entropy, Renyi entropy, and information. Statistics and Inf. Series (2004-004), Available: www. tina-vision. net, 26. Hasson, C.J., et al., Influence of embedding parameters and noise in center of pressure recurrence quantification analysis. Gait & posture, 2008. 27(3): p. 416-422.

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