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

journal contribution
posted on 05.10.2020, 13:56 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

Volume

110

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Artificial Intelligence in Medicine and the definitive published version is available at https://doi.org/10.1016/j.artmed.2020.101966

Acceptance date

02/10/2020

Publication date

2020-10-06

Copyright date

2020

ISSN

0933-3657

Language

en

Depositor

Dr Hui Fang. Deposit date: 2 October 2020

Article number

101966

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