Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease

Varun  Jain

doi:10.18502/jbe.v10i3.17926

Varun Jain Faculty of Health Sciences, McMaster University, Hamilton, Canada.

DOI: https://doi.org/10.18502/jbe.v10i3.17926

Keywords: Fourier analysis; Machine learning; Support vector machine (SVM); Frequency analysis; Power spectrum analysis; Biomedical signals

Abstract

Introduction: Parkinson’s disease is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force data, an association can be made between the data and Parkinson’s disease.

Methods: Data from 146 participants; 93 with Parkinson’s disease and 73 without Parkinson’s disease was obtained from a PhysioNet database for use in this article. A Fourier Analysis and several support vector machine learning models were computed in MATLAB to classify whether an individual had Parkinson’s disease.

Results: From the Fourier analysis, it was determined that a statistically significant difference was present between the vertical ground reaction force data of individuals with and without Parkinson’s disease. Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using vertical ground reaction force data at an accuracy of 67.1%, and sensitivity of 80.43%.

Conclusion: Therefore, it can be determined that vertical ground reaction force can predict Parkinson’s Disease with considerable accuracy which could be improved with an increased number of participants.