Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks

haozheng zhang, hubert p. h. shum and edmond s. l. ho
Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022

Citation: 1## Oral Presentation

Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
## Citation counts from Google Scholar as of 2022

Abstract

Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference between CP infants’ frequency of human movement and that of the healthy group, which improves prediction performance. However, the existing deep learning-based methods did not use the frequency information of infants’ movement for CP prediction. This paper proposes a frequency attention informed graph convolutional network and validates it on two consumer-grade RGB video datasets, namely MINI-RGBD and RVI-38 datasets. Our proposed frequency attention module aids in improving both classification performance and system interpretability. In addition, we design a frequency-binning method that retains the critical frequency of the human joint position data while filtering the noise. Our prediction performance achieves state-of-the-art research on both datasets. Our work demonstrates the effectiveness of frequency information in supporting the prediction of CP non-intrusively and provides a way for supporting the early diagnosis of CP in the resource-limited regions where the clinical resources are not abundant.

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BibTeX

@inproceedings{zhang22cerebral,
 author={Zhang, Haozheng and Shum, Hubert P. H. and Ho, Edmond S. L.},
 booktitle={Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
 series={EMBC '22},
 title={Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks},
 year={2022},
 numpages={7},
 publisher={IEEE},
 location={Glasgow, UK},
}

RIS

TY  - CONF
AU  - Zhang, Haozheng
AU  - Shum, Hubert P. H.
AU  - Ho, Edmond S. L.
T2  - Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
TI  - Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
PY  - 2022
PB  - IEEE
ER  - 

Plain Text

Haozheng Zhang, Hubert P. H. Shum and Edmond S. L. Ho, "Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks," in EMBC '22: Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow, UK, IEEE, 2022.

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Kevin D. McCay, Edmond S. L. Ho, Hubert P. H. Shum, Gerhard Fehringer, Claire Marcroft and Nicholas Embleton, "Abnormal Infant Movements Classification with Deep Learning on Pose-based Features", IEEE Access, 2020
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