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.

翻译：早期诊断和干预在临床中被认为是治疗脑性瘫痪（CP）的关键环节，因此设计高效且可解释的CP自动预测系统至关重要。我们发现CP患儿与健康群体在人体运动频率上存在显著差异，这一发现有助于提升预测性能。然而，现有基于深度学习的方法尚未利用婴幼儿运动的频率信息进行CP预测。本文提出了一种基于频率注意力的图卷积网络，并在两个消费级RGB视频数据集（MINI-RGBD和RVI-38数据集）上进行了验证。我们提出的频率注意力模块有助于提升分类性能和系统可解释性。此外，我们设计了一种频率分箱方法，在保留人体关节位置数据关键频率的同时过滤噪声。我们的预测性能在两个数据集上均达到了当前最优水平。本研究证明了频率信息在无创支持CP预测中的有效性，并为此类临床资源匮乏地区开展CP早期诊断提供了一种可行方案。