Eye movement abnormalities have been effective biomarkers that provide the possibility of distinguishing patients with schizophrenia from healthy controls. The existing methods for measuring eye movement abnormalities mostly focus on synchronic parameters, such as fixation duration and saccade amplitude, which can be directly obtained from eye movement data, while lack of considering more thorough features. In this paper, to better characterize eye-tracking dysfunction, we create a dataset containing 100 images with eye movement data of 40 patients and 30 healthy controls via a free-viewing task, and propose two types of features for effective schizophrenia recognition, i.e. the hand-crafted discriminative eye movement features and the model-metric based features via utilizing the computational models of fixation prediction and the metrics of evaluating their prediction performance. Using the proposed features, two commonly used classifiers including support vector machine and random forest have been trained for classification between patients and controls. Experimental results demonstrate the effectiveness of the proposed features for improving classification performance, and the potential that our method can serve as an alternative and promising approach for the computer-aided diagnosis of schizophrenia.

眼球运动异常是有效的生物标志物，提供了将精神分裂症患者与健康对照区分开的可能性。现有的测量眼球运动异常的方法主要集中在同步参数上，例如注视时间和扫视幅度，这些参数可以直接从眼球运动数据中获得，而没有考虑更全面的特征。在本文中，为了更好地描述眼球追踪功能障碍，我们创建了一个包含100幅图像的数据集，该图像集包含40位患者的眼球运动数据和30位健康对照，通过自由观看任务，并提出了两种有效的精神分裂症识别功能，即通过利用注视预测的计算模型和评估其预测性能的度量，手工制作出具有区别性的眼动特征和基于模型度量的特征。使用提出的功能，对两个常用的分类器（包括支持向量机和随机森林）进行了训练，以进行患者和对照之间的分类。实验结果证明了所提出的功能对于改善分类性能的有效性，以及我们的方法可以作为精神分裂症的计算机辅助诊断的替代和有希望的方法的潜力。