McCay, Kevin D., Ho, Edmond, Sakkos, Dimitris, Woo, Wai Lok, Marcroft, Claire, Dulson, Patricia and Embleton, Nicholas D. (2021) Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework. In: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE EMBS International Conference on Biomedical and Health Informatics . IEEE, Piscataway, p. 9508603. ISBN 9781665403580, 9781665447706
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Abstract
Providing early diagnosis of cerebral palsy (CP) is key to enhancing the developmental outcomes for those affected. Diagnostic tools such as the General Movements Assessment (GMA), have produced promising results in early diagnosis, however these manual methods can be laborious.
In this paper, we propose a new framework for the automated classification of infant body movements, based upon the GMA, which unlike previous methods, also incorporates a visualization framework to aid with interpretability. Our proposed framework segments extracted features to detect the presence of Fidgety Movements (FMs) associated with the GMA spatiotemporally. These features are then used to identify the body-parts with the greatest contribution towards a classification decision and highlight the related body-part segment providing visual feedback to the user.
We quantitatively compare the proposed framework's classification performance with several other methods from the literature and qualitatively evaluate the visualization's veracity. Our experimental results show that the proposed method performs more robustly than comparable techniques in this setting whilst simultaneously providing relevant visual interpretability.
Item Type: | Book Section |
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Additional Information: | IEEE International Conference on Biomedical and Health Informatics (BHI) : Reshaping healthcare through advanced AI-enabled health informatics for a better quality of life : Athens, Greece, 27/07/21 → 30/07/21 |
Uncontrolled Keywords: | infants, cerebral palsy, general movements assessment, machine learning, explainable AI, visualization |
Subjects: | B900 Others in Subjects allied to Medicine G400 Computer Science |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | John Coen |
Date Deposited: | 15 Jun 2021 07:49 |
Last Modified: | 11 Oct 2021 13:30 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/46441 |
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