Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework

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: IEEE BHI 2021: IEEE International Conference on Biomedical and Health Informatics (BHI) : Reshaping healthcare through advanced AI-enabled health informatics for a better quality of life, 27-30 Jul 2021, Virtual. (In Press)

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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: Conference or Workshop Item (Paper)
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: 15 Jun 2021 08:00

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