eCite Digital Repository

Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

Citation

Williams, S and Relton, SD and Fang, H and Alty, J and Qahwaji, R and Graham, CD and Wong, DC, Supervised classification of bradykinesia in Parkinson's disease from smartphone videos, Artificial Intelligence in Medicine, 110 Article 101966. ISSN 0933-3657 (2020) [Refereed Article]

Copyright Statement

© 2020 Elsevier B.V. All rights reserved

DOI: doi:10.1016/j.artmed.2020.101966

Abstract

Background

Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.

Aim

We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.

Methods

We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis.

Results

A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.

Conclusion

The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Item Details

Item Type:Refereed Article
Keywords:computer vision, artificial intelligence, Parkinson's, bradykinesia, video, diagnosis, support vector machine
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Alty, J (Dr Jane Alty)
ID Code:142725
Year Published:2020
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2021-02-09
Last Modified:2021-04-29
Downloads:0

Repository Staff Only: item control page