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.

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
• Electronic ISSN: 2665-9638
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Article Number: 100419
• DOI: https://doi.org/10.1016/j.simpa.2022.100419
• Public URL: https://durham-repository.worktribe.com/output/1193427