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CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy

Zhang, Haozheng; Ho, Edmund S.L.; Shum, Hubert P.H.

CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy Thumbnail


Authors

Haozheng Zhang haozheng.zhang@durham.ac.uk
PGR Student Doctor of Philosophy

Edmund S.L. Ho



Abstract

Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA). We design a Pytorch-based attention-informed graph convolutional network to early identify infants at risk of CP from skeletal data extracted from RGB videos. We also design a frequencybinning module for learning the CP movements in the frequency domain while filtering noise. Our system only requires consumer-grade RGB videos for training to support interactive-time CP prediction by providing an interpretable CP classification result.

Citation

Zhang, H., Ho, E. S., & Shum, H. P. (2022). CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy. Software impacts, 14, Article 100419. https://doi.org/10.1016/j.simpa.2022.100419

Journal Article Type Article
Acceptance Date Sep 1, 2022
Online Publication Date Sep 17, 2022
Publication Date 2022-11
Deposit Date Sep 2, 2022
Publicly Available Date Sep 2, 2022
Journal Software Impacts
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 14
Article Number 100419
DOI https://doi.org/10.1016/j.simpa.2022.100419

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