论文标题
根据自然观察期间的眼动检测ADHD
Detection of ADHD based on Eye Movements during Natural Viewing
论文作者
论文摘要
注意缺乏/多动症（ADHD）是一种高度普遍的神经发育障碍，需要临床专家才能诊断。众所周知，个人的观看行为反映在眼睛运动中，直接与注意机制和高阶认知过程有关。因此，我们探讨了是否可以根据记录的眼动物以及在免费观看任务中的视频刺激信息以及有关视频刺激的信息来探索多动症。为此，我们开发了一个基于端到端的深度学习序列模型，我们将其预先培训在相关任务上可以使用更多数据。我们发现该方法实际上能够检测ADHD并胜过相关的基线。我们在消融研究中研究了输入特征的相关性。有趣的是，我们发现该模型的性能与视频内容密切相关，该视频为将来的实验设计提供了见解。
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose. It is known that an individual's viewing behavior, reflected in their eye movements, is directly related to attentional mechanisms and higher-order cognitive processes. We therefore explore whether ADHD can be detected based on recorded eye movements together with information about the video stimulus in a free-viewing task. To this end, we develop an end-to-end deep learning-based sequence model which we pre-train on a related task for which more data are available. We find that the method is in fact able to detect ADHD and outperforms relevant baselines. We investigate the relevance of the input features in an ablation study. Interestingly, we find that the model's performance is closely related to the content of the video, which provides insights for future experimental designs.
