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Aiding diagnosis of normal pressure hydrocephalus with enhanced gait feature separability

Published:23 October 2012Publication History

ABSTRACT

Normal Pressure Hydrocephalus (NPH) is a neurological condition that challenges differential diagnosis, as the symptoms -- cognitive and gait impairment and urinary incontinence -- are similar to those of many aging disorders, including Alzheimer's disease and other forms of dementia. Since NPH is caused by abnormal accumulation of cerebrospinal fluid (CSF) around the brain, a high volume lumbar puncture (HVLP) to remove excess fluid is used as the stimulus for a suspected NPH patient, and a diagnosis is made based on the observed cognitive and functional response.

Gait features have long been used as functional indicators in the pre- and post-HVLP assessment. However, these assessments are limited to visual observation in the clinic. Therefore, only simple gait features such as walking speed (based on time to walk 10m) and average stride length/time (based on the number of steps to walk 10m) are used. However, these features provide limited separability in the NPH diagnosis.

This paper presents methods for enhanced diagnostic separability using additional gait features extracted from an inertial body sensor network (BSN), including stride time variability, double support time, and stability. A pilot study on six HVLP patients -- four of whom were ultimately diagnosed with NPH -- revealed that gait stability assessed by Lyapunov exponent provides better separability and can enhance the differential diagnosis. In addition, these results suggest that additional testing can be performed continuously outside of the clinic to account for patients' variable HVLP response times.

References

  1. G. D. Rigamonti and M. A. Williams. The diagnosis and treatment of idiopathic normal pressure hydrocephalus. Nature Clinical Practice Neurology, 2(7): 375--381, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  2. A. Brean and P. K. Eide. Prevalence of probable idiopathic normal pressure hydrocephalus in a Norwegian population. Acta Neurologica Scandinavica, 118(l): 48--53, 2008.Google ScholarGoogle Scholar
  3. N. Tanaka, S. Yamaguchi, H. Ishikawa, H. Ishii and K. Meguro. Prevalence of possible idiopathic normal-pressure hydrocephalus in Japan: the Osaki-Tajiri project. Neuroepidemiology, 32(3): 171--5, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  4. A. Shrinivasan, M. Brandt-Pearce A. T. Barth, and J. Lach. Analysis of gait in patients with normal pressure hydrocephalus. International Workshop for Mobile Systems, Applications, and Services for Healthcare, pages 3: 1--6, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Bugalho and J. Guimares. Gait disturbance in normal pressure hydrocephalus: A clinical study. Parkinsonism and Related Disorders, pages 13: 434--137, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  6. S. Chen, C. Cunningham, B. C. Bennett, and J. Lach. Extracting spatio-temporal information from inertial body sensor networks for gait speed estimation. International Conference on Body Sensor Networks, pages 71--76, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Chen, C. L. Cunningham, B. C. Bennett, and J. Lach. Enabling longitudinal assessment of ankle-foot orthosis efficacy for children with cerebral palsy. Wireless Health, pages 4: 1--10, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. X. Xu, M. A. Batalin, W. J. Kaiser and B. Dobkin. Robust hierarchical system for classification of complex human mobility characteristics in the presence of neurological disorders. International Conference on Body Sensor Networks, pages 65--70, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Atallah, G. J. Jones, R. Ali, J. Leong, B. Lo and G-Z. Yang. Observing recovery from knee-replacement surgery by using wearable sensors. International Conference on Body Sensor Networks, pages 29--34, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. I. Tien, S. D. Glaser, R. Bajcsy, D. S. Goodin and M. J. Aminoff. Results of using a wireless inertial measuring system to quantify gait motions in control subjects. IEEE Transactions on Information Technology in Biomedicine, 14(4): 904--915, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Tsakanikas and N. Relkin. Normal pressure hydrocephalus. Seminars in Neurology, 27(1): 58--65, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  12. R. K. Wilson and M. A. Williams. Normal pressure hydrocephalus. Clinics in Geriatric Medicine, 22: 935--951, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  13. N. R. Graff-Radford. Normal pressure hydrocephalus. Neurologic Clinics, 25: 809--832, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  14. M. A. Williams, G. Thomas, B. de Lateur, H. Imteyaz, J. G. Rose, W. S. Shore, S. Kharkar, and D. Rigamonti. Objective assessment of gait in normal-pressure hydrocephalus. American Journal of Physical Medicine and Rehabilitation, pages 2--3, 2007.Google ScholarGoogle Scholar
  15. A. T. Barth, M. A. Hanson, H. C. Powell Jr., and J. Lach. TEMPO 3.1: A body area sensor network platform for continuous movement assessment. International Workshop on Wearable and Implantable Body Sensor Networks, pages 71--76, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Q. Li, M. Young, V. Naing, and J. M. Donelan. Walking speed and slope estimation using shank mounted inertial measurement units. Journal of Biomechanics, 43(8): 1640--1643, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  17. K. L. Warnecke. Analysis of gait before and after cerebrospinal fluid lumbar tap test in idiopathic normal pressure hydrocephalus: a literature review and case report. Topics in Geriatric Rehabilitation, 25(3): 203--210, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  18. L. D. Ravdin, H. L. Katzen, A. E. Jackson, D. Tsakanikas, S. Assuras, and N. R. Relkin. Features of gait most responsive to tap test in normal pressure hydrocephalus. Clinical Neurology and Neurosurgery, 110(5): 455--461, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  19. J. B. Dingwell and J. P. Cusumano. Nonlinear time series analysis of normal and pathological human walking. Chaos: An Interdisciplinary Journal of Nonlinear Science, 10(4): 848--886, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  20. K. P. Granata, T. E. Lockhart. Dynamic stability differences in fall-prone and healthy adults. Journal of Electromyography and Kinesiology, 18(2): 172--178, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  21. Y. Hurmuzlu, C. Basdogan, and J. J. Carollo. Presenting joint kinematics of human locomotion using phase plane portraits and Poincaré maps. Journal of Biomechanics, 27(12): 1495--1499, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  22. S. M. Bruijn, D. J. J. Bregman, O. G. Meijer, P. J. Beek, and J. H. van Dieën. Estimating dynamic gait stability using data from non-aligned inertial sensors. Annals of Biomedical Engineering, 38(8): 2588--2593, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  23. F. Cignetti, L. M. Decker, and N. Stergiou. Sensitivity of the Wolf's and Rosenstein's algorithms to evaluate local dynamic stability from small gait data sets. Annals of Biomedical Engineering, 40(5): 1122--1130, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  24. H. G. Kang and J. B. Dingwell. Dynamic stability of superior vs. inferior segments during walking in young and older adults. Gait and Posture, 30(2): 260--263, August 2009.Google ScholarGoogle ScholarCross RefCross Ref
  25. M. T. Rosenstein, J. J. Collins, and C. J. De Luca. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65: 117--134, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano. Determining Lyapunov exponents from a time series. Physica D, 16: 285--317, 1985.Google ScholarGoogle ScholarCross RefCross Ref
  27. S. A. England and K. P. Granata. The influence of gait speed on local dynamic stability of walking. Gait and Posture, 25(2): 172--178, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  28. J. B. Dingwell and H. G. Kang. Differences between local and orbital dynamic stability during human walking. Journal of Biomechanical Engineering, 129(4): 586--593, 2007.Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Other conferences
          WH '12: Proceedings of the conference on Wireless Health
          October 2012
          117 pages
          ISBN:9781450317603
          DOI:10.1145/2448096

          Copyright © 2012 ACM

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          Publication History

          • Published: 23 October 2012

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