Parkinson's Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks.

Srivardhini Veeraragavan,Siti Anom Ahmad,Darwin Gouwanda,Alpha Agape Gopalai

doi:10.3389/fphys.2020.587057

Srivardhini Veeraragavan, Siti Anom Ahmad + Show 2 more

Open Access

https://doi.org/10.3389/fphys.2020.587057

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Abstract

Gait analysis plays a key role in the diagnosis of Parkinson’s Disease (PD), as patients generally exhibit abnormal gait patterns compared to healthy controls. Current diagnosis and severity assessment procedures entail manual visual examinations of motor tasks, speech, and handwriting, among numerous other tests, which can vary between clinicians based on their expertise and visual observation of gait tasks. Automating gait differentiation procedure can serve as a useful tool in early diagnosis and severity assessment of PD and limits the data collection to solely walking gait. In this research, a holistic, non-intrusive method is proposed to diagnose and assess PD severity in its early and moderate stages by using only Vertical Ground Reaction Force (VGRF). From the VGRF data, gait features are extracted and selected to use as training features for the Artificial Neural Network (ANN) model to diagnose PD using cross validation. If the diagnosis is positive, another ANN model will predict their Hoehn and Yahr (H&Y) score to assess their PD severity using the same VGRF data. PD Diagnosis is achieved with a high accuracy of 97.4% using simple network architecture. Additionally, the results indicate a better performance compared to other complex machine learning models that have been researched previously. Severity Assessment is also performed on the H&Y scale with 87.1% accuracy. The results of this study show that it is plausible to use only VGRF data in diagnosing and assessing early stage Parkinson’s Disease, helping patients manage the symptoms earlier and giving them a better quality of life.

Highlights

Parkinson’s Disease (PD) is a highly prevalent neuro-degenerative disease that affects more than 10 million people worldwide
The pattern recognition network used for PD diagnosis performs well with a classification accuracy of 97.4% and a mean square error value of 0.0279, which is consistent with literature that proves a high correlation between gait variability and presence of PD (Gaenslen and Daniela, 2010), resulting in an accurate classification
Good classification of PD subjects from healthy controls is achieved with an accuracy of 97.4% using input features extracted from Vertical Ground Reaction Force (VGRF) data

Summary

Introduction

Parkinson’s Disease (PD) is a highly prevalent neuro-degenerative disease that affects more than 10 million people worldwide. While PD usually occurs in adults aged 50 and above, there have been cases of young onsets of this disease, where individuals as young as 18 years old have been diagnosed with PD (Parkinson’s Foundation, 2019). There are five progression stages in PD, where treatment in the early stages (Stages 1 and 2) slows down the onset of the disease, allowing patients to experience a better quality of life (Parkinson’s Foundation, 2019). There is no specific test that exists to diagnose PD, and patients will have to rely on a neurologist for a diagnosis. Neurologists typically base their diagnosis on several factors such as the patients’ medical history, signs and symptoms exhibited, and a neurological and physical examination.

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