File(s) under embargo

Reason: Publisher requirement. Embargo date will be amended following publication.

3629

days

7

hours

until file(s) become available

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

journal contribution
posted on 05.10.2020 by Stefan Williams, Samuel D Relton, Hui Fang, Jane Alty, Rami Qahwaji, Christopher D Graham, David C Wong
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 (Naive 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 Naive Bayes model predicted the presence of Parkinson’s disease with estimated test accuracy 0.70. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of fingertapping 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.

History

School

  • Science

Department

  • Computer Science

Published in

Artificial Intelligence in Medicine

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal Artificial Intelligence in Medicine and the definitive published version is available at [insert DOI link].

Acceptance date

02/10/2020

ISSN

0933-3657

Language

en

Depositor

Dr Hui Fang. Deposit date: 2 October 2020

Exports

Logo branding

Categories

Exports