Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

An Efficient Method for Parkinson’s Disease Detection using Handwriting Features

Document Type : Full Research Paper

Authors

Elham Dehghanpur Deharab 1
Peyvand Ghaderyan 2

1 M.Sc. Student, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

2 Assistant Professor, Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

https://doi.org/10.22041/ijbme.2022.534243.1706
Abstract
Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience and the skill of the physician. Therefore, the development of an automated, low cost, and reliable diagnostic system is desirable for researchers. In this study, a handwriting signal including cognitive and motor-perceptual components has been used as a non-invasive, cost effective and reliable characteristic in identifying PD-related cognitive and motor dysfunctions. For this purpose, the matching pursuit algorithm with high time-frequency resolution has been employed to decompose X-Y coordinates. It provides a sparse representation of the handwriting signals and quantifies the basic information about the local changes in the handwriting signals. The proposed method is evaluated on a database with 31 healthy samples and 29 Parkinson's samples using the support vector machine classifier and obtained results yields an average accuracy rate of 90%, sensitivity rate of 91.59% and specificity rate of 90%. Comparing different writing tasks has also demonstrated superior performance of writing an entire sentence for PD detection.

Keywords

Parkinson's Disease
Handwriting
Matching Pursuit
Support Vector Machine

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 15, Issue 4
Winter 2022
Pages 279-287

  • Receive Date 17 July 2021
  • Revise Date 27 December 2021
  • Accept Date 11 January 2022

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