Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2448096.2448099acmotherconferencesArticle/Chapter ViewAbstractPublication PageswhConference Proceedingsconference-collections
research-article

Aiding diagnosis of normal pressure hydrocephalus with enhanced gait feature separability

Published: 23 October 2012 Publication 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.
[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.
[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.
[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.
[5]
P. Bugalho and J. Guimares. Gait disturbance in normal pressure hydrocephalus: A clinical study. Parkinsonism and Related Disorders, pages 13: 434--137, 2007.
[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.
[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.
[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.
[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.
[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.
[11]
D. Tsakanikas and N. Relkin. Normal pressure hydrocephalus. Seminars in Neurology, 27(1): 58--65, 2007.
[12]
R. K. Wilson and M. A. Williams. Normal pressure hydrocephalus. Clinics in Geriatric Medicine, 22: 935--951, 2006.
[13]
N. R. Graff-Radford. Normal pressure hydrocephalus. Neurologic Clinics, 25: 809--832, 2007.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.

Cited By

View all
  • (2021)DAid pressure socks system: Performance evaluationGait & Posture10.1016/j.gaitpost.2021.01.00784(368-376)Online publication date: Feb-2021
  • (2017)Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects ModelsSensors10.3390/s1703046617:3(466)Online publication date: 25-Feb-2017
  • (2016)Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic ReviewIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2016.260872020:6(1521-1537)Online publication date: Nov-2016
  • Show More Cited By

Index Terms

  1. Aiding diagnosis of normal pressure hydrocephalus with enhanced gait feature separability

        Recommendations

        Comments

        Please enable JavaScript to view thecomments powered by Disqus.

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        WH '12: Proceedings of the conference on Wireless Health
        October 2012
        117 pages
        ISBN:9781450317603
        DOI:10.1145/2448096
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Sponsors

        • WLSA: Wireless-Life Sciences Alliance

        In-Cooperation

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 23 October 2012

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Lyapunov exponent
        2. diagnosis
        3. evaluation
        4. gait features
        5. gait stability
        6. inertial BSN
        7. normal pressure hydrocephalus

        Qualifiers

        • Research-article

        Funding Sources

        Conference

        WH '12
        Sponsor:
        • WLSA
        WH '12: Wireless Health 2012
        October 23 - 25, 2012
        California, San Diego

        Acceptance Rates

        Overall Acceptance Rate 35 of 139 submissions, 25%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)9
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 20 Nov 2024

        Other Metrics

        Citations

        Cited By

        View all
        • (2021)DAid pressure socks system: Performance evaluationGait & Posture10.1016/j.gaitpost.2021.01.00784(368-376)Online publication date: Feb-2021
        • (2017)Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects ModelsSensors10.3390/s1703046617:3(466)Online publication date: 25-Feb-2017
        • (2016)Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic ReviewIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2016.260872020:6(1521-1537)Online publication date: Nov-2016
        • (2016)Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic EnhancementIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2016.258990220:5(1273-1280)Online publication date: Sep-2016

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media