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Identify autism spectrum disorder via dynamic filter and deep spatiotemporal feature extraction
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2021-02-16 , DOI: 10.1016/j.image.2021.116195
Weijie Wei , Zhi Liu , Lijin Huang , Ziqiang Wang , Weiyu Chen , Tianhong Zhang , Jijun Wang , Lihua Xu

Early intervention and treatment are crucial for individuals with autism spectrum disorder (ASD). However, it is challenging to identify individuals with ASD at an early age, i.e. under 3 years old, due to the lack of an effective and objective identification method. The mainstream clinical diagnosis relies on long-term observation of children’s behaviors, which is time-consuming and expensive, and thus how to accurately and quickly distinguish children with ASD in early childhood has become a critical issue. In this paper, we propose an eye movement based model to identify children with ASD. Specifically, children are required to freely observe some images. At the same time, their eye movements are recorded to analyze. Both the observed image and eye movements are input into our model. The input data are processed by the embedding layer, dynamic filters and LSTM block, respectively. Eventually, the spatiotemporal features are extracted to identify the eye movements belonging to a child with ASD or a typically developed child. Experiments on the Saliency4ASD dataset demonstrate that the proposed model achieves state-of-the-art performance in identifying children with ASD.



中文翻译:

通过动态滤波器和深时空特征提取识别自闭症谱系障碍

早期干预和治疗对自闭症谱系障碍(ASD)的个体至关重要。然而,要在早期识别出患有自闭症的人是具有挑战性的,3岁以下儿童,由于缺乏有效和客观的识别方法。主流的临床诊断依赖于对儿童行为的长期观察,这既耗时又昂贵,因此如何准确,快速地区分儿童早期的ASD已成为一个关键问题。在本文中,我们提出了一种基于眼动的模型来识别患有ASD的儿童。具体来说,要求儿童自由观察一些图像。同时,记录他们的眼动以进行分析。观察到的图像和眼睛的运动都输入到我们的模型中。输入数据分别由嵌入层,动态过滤器和LSTM块处理。最终,提取时空特征以识别属于ASD儿童或典型发育儿童的眼球运动。在Saliency4ASD数据集上的实验表明,该模型在识别ASD儿童方面达到了最新水平。

更新日期:2021-02-24
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