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Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos
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.
History
Publication title
Artificial Intelligence in MedicineVolume
110Article number
101966Number
101966Pagination
1-9ISSN
0933-3657Department/School
Wicking Dementia Research Education CentrePublisher
Elsevier Science BvPlace of publication
Po Box 211, Amsterdam, Netherlands, 1000 AeRights statement
© 2020 Elsevier B.V. All rights reservedRepository Status
- Restricted
