Chaos Nameplat
  • Volume/Page
  • Keyword
  • DOI
  • Citation
  • Advanced
   
 
 
 

You Tube Flickr Twitter UniPHY Group iResearch App Facebook

Chaos / Volume 22 / Issue 1 / REGULAR ARTICLES

Chaos 22, 013119 (2012); http://dx.doi.org/10.1063/1.3683444 (13 pages)

Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations

C. Lainscsek1,2, P. Rowat1, L. Schettino3, D. Lee1, D. Song4, C. Letellier5, and H. Poizner1

1Institute for Neural Computation, University of California at San Diego, La Jolla, California 92093-0523, USA
2Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA
3Department of Psychology, Lafayette College Easten, Pennsylvania 18042, USA
4Department of Neurosciences, University of California at San Diego, La Jolla, California 92093-9127, USA
5CORIA UMR 6614, Université de Rouen, BP 12, F-76801 Saint-Etienne du Rouvray cedex, France

View MapView Map

(Received 4 August 2010; accepted 10 January 2012; published online 16 February 2012)

Parkinson’s disease is a degenerative condition whose severity is assessed by clinical observations of motor behaviors. These are performed by a neurological specialist through subjective ratings of a variety of movements including 10-s bouts of repetitive finger-tapping movements. We present here an algorithmic rating of these movements which may be beneficial for uniformly assessing the progression of the disease. Finger-tapping movements were digitally recorded from Parkinson’s patients and controls, obtaining one time series for every 10 s bout. A nonlinear delay differential equation, whose structure was selected using a genetic algorithm, was fitted to each time series and its coefficients were used as a six-dimensional numerical descriptor. The algorithm was applied to time-series from two different groups of Parkinson’s patients and controls. The algorithmic scores compared favorably with the unified Parkinson’s disease rating scale scores, at least when the latter adequately matched with ratings from the Hoehn and Yahr scale. Moreover, when the two sets of mean scores for all patients are compared, there is a strong (r = 0.785) and significant (p<0.0015) correlation between them.

© 2012 American Institute of Physics

Lead Paragraph

Parkinson’s disease (PD) is a common disease affecting tens of millions of people worldwide. Its cardinal signs are resting tremor, bradykinesia (slowness clumsiness of movement), rigidity, and loss of postural reflexes. The disease evolves slowly and, to adjust medications to the severity of the disease, there is a need for automatic and objective evaluation of movements. Such objective movement assessments would supplement subjective clinical ratings, which are ordinal rather than metric and often show large inter-rater variability. Rather than using a spectral based technique, we rated dynamical features of each individuals’ finger-tapping—one of the items from the unified Parkinson’s disease rating scale (UPDRS) used for rating the severity of the disease—by using data models based on nonlinear delay differential equations (DDEs). The coefficients of the DDEs are then used to assess the severity of the disease.

Article Outline

  1. INTRODUCTION
  2. PATIENTS AND MEASUREMENTS
  3. DYNAMICAL ANALYSIS
  4. CLASSIFICATION USING DYNAMICAL MODELS
    1. Delay differential equations
    2. Structure selection using genetic algorithm
    3. Selecting the DDE model structure and delays
      1. Selecting the model structure from group ii
      2. Selecting the delays from group ii
    4. Comparison of UPDRS, HY, and DDE scores
    5. Comparison of UPDRS and DDE scores for groups i and ii combined
  5. CONCLUSION
    1. Basic findings
    2. Study limitations
    3. Future studies
    4. DDE data analysis technique used in this paper

RELATED DATABASES

To view database links for this article, you need to log in.

KEYWORDS and PACS

Keywords

biomechanics, diseases, neurophysiology, nonlinear differential equations, numerical analysis, time series

PACS

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3683444

PUBLICATION DATA

ISSN

1054-1500 (print)  
1089-7682 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

For access to fully linked references, you need to log in.
    C. Letellier, L. A. Aguirre, and U. S. Freitas, Chaos 19, 023103 (2009)CHAOEH000019000002023103000001.

    C. Letellier, L. A. Aguirre, and J. Maquet, Phys. Rev. E 71, 066213 (2005).

    C. Letellier and L. A. Aguirre, Chaos 12, 549 (2002)CHAOEH000012000003000549000001.


Figures (13) Tables (5)

Access to article objects (figures, tables, multimedia) requires a subscription; log in to view available files.
(Access to supplementary files, where available, is free for this journal.)

Access to article objects (figures, tables, multimedia) requires a subscription; log in to view available files.
(Access to supplementary files, where available, is free for this journal.)



Close

Google Calendar

ADVERTISEMENT

Your Recent History Recent History Help

Recently Viewed

  1. Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations
  2. Alternation of regular and chaotic dynamics in a simple two-degree-of-freedom system with nonlinear inertial coupling
  3. The structure and resilience of financial market networks
  4. Long-range interactions between adjacent and distant bases in a DNA and their impact on the ribonucleic acid polymerase-DNA dynamics
  5. Geometric and dynamic perspectives on phase-coherent and noncoherent chaos
Go to AIP Home
Copyright © American Institute of Physics
close