An automatic non-invasive method for Parkinson's disease classification.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 18, 2017

Abstract

BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various analysis and classification approaches using spatiotemporal gait variables have been presented earlier in classifying Parkinson's gait. In this paper, we present a wavelet transform based representation of spatiotemporal gait variables to explore the potential of such representation in the identification of Parkinson's gait.

Authors

  • Deepak Joshi
    Center for Biomedical Engineering, Indian Institute of Technology, Delhi, India.
  • Aayushi Khajuria
    Department of Electrical Engineering, Graphic Era University, Dehradun, India. Electronic address: aayushikhajuria@gmail.com.
  • Pradeep Joshi
    Quantum Business School, Roorkee, India.

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