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

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    Publication date
    2020-11
    Author
    Williams, S.
    Relton, S.D.
    Fang, H.
    Alty, J.
    Qahwaji, Rami S.R.
    Graham, C.D.
    Wong, D.C.
    Keyword
    Classification
    Parkinson's
    Bradykinesia
    Video
    Computer vision
    Diagnosis
    Support vector machine
    Peer-Reviewed
    Yes
    Open Access status
    Not Open Access
    
    Metadata
    Show full item record
    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.
    URI
    http://hdl.handle.net/10454/18414
    Version
    No full-text in the repository
    Citation
    Williams S, Relton SD, Fang H et al (2020) Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos. Artificial Intelligence in Medicine. 110: 101966.
    Link to publisher’s version
    https://doi.org/10.1016/j.artmed.2020.101966
    Type
    Article
    Collections
    Engineering and Informatics Publications

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