用眼球注视模式的聚类有效性指标区分自闭症与正常儿童。
Exploring Gaze Pattern Differences Between Autistic and Neurotypical Children: Clustering, Visualisation, and Prediction
- 通过7种聚类算法提取63个内部有效性指标，捕捉注视模式差异。
- 在三个数据集上实现81%的AUC，可有效预测自闭症诊断。
- 为自闭症早期筛查提供无需人工标注的量化分析工具。
自闭症谱系障碍（ASD）影响儿童的社会与沟通能力，眼动追踪被广泛用于识别异常注视模式。尽管无监督聚类可自动生成兴趣区域以提取注视特征，但利用内部聚类有效性指标（如轮廓系数）区分自闭症（ASD）与典型发育（TD）儿童的注视模式仍缺乏探索。本文研究内部聚类有效性指标是否能有效区分两类儿童。具体地，将七种聚类算法应用于注视点数据，提取63个内部聚类有效性指标，分析其与自闭症诊断的相关性，并基于这些指标训练预测模型。在三个数据集上的实验表明，该方法可达到81%的AUC，验证了这些指标在区分自闭症与典型发育儿童中的有效性。
原文摘要 · Abstract (English)
Autism Spectrum Disorder (ASD) affects children's social and communication abilities, with eye-tracking widely used to identify atypical gaze patterns. While unsupervised clustering can automate the creation of areas of interest for gaze feature extraction, the use of internal cluster validity indices, like Silhouette Coefficient, to distinguish gaze pattern differences between ASD and typically developing (TD) children remains underexplored. We explore whether internal cluster validity indices can distinguish ASD from TD children. Specifically, we apply seven clustering algorithms to gaze points and extract 63 internal cluster validity indices to reveal correlations with ASD diagnosis. Using these indices, we train predictive models for ASD diagnosis. Experiments on three datasets demonstrate high predictive accuracy (81\% AUC), validating the effectiveness of these indices.
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