面向自闭症辅助诊断的联合组稀疏TSK建模方法

doi:10.3778/j.issn.1673-9418.1908067

摘要/Abstract

摘要：

自闭症是一种神经发育障碍类疾病，其诊断过程存在着很大的不确定性。目前已有的面向自闭症诊断的建模方法没有针对诊断过程的不确定性进行有效研究。为此，以TSK模糊系统为基础，结合功能连接之间的关联信息，提出一种新型的不确定性联合组稀疏建模方法JGSL-TSK，并将其用于自闭症的辅助诊断。首先，对原始rs-fMRI数据进行预处理和特征提取，得到低维特征数据；然后，基于TSK模糊系统框架，从特征之间的相关性出发，在后件参数学习过程中引入联合组稀疏正则化项，从而引导同一规则内特征和规则之间特征的联合选择；最后，采用交替优化方法求解模型。与已有方法相比，该方法具有可解释性强、分类准确率高等优点，实验结果证明了该方法有利于自闭症的辅助诊断。

关键词: 自闭症谱系障碍, 静息态功能磁共振成像, TSK模糊系统, 联合组稀疏

Abstract:

Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism.

Key words: autism spectrum disorder (ASD), resting-state functional magnetic resonance imaging (rs-fMRI), TSK fuzzy system, joint group sparse

张春香，王骏，张嘉旭，邓赵红，潘祥，王士同. 面向自闭症辅助诊断的联合组稀疏TSK建模方法[J]. 计算机科学与探索, 2020, 14(12): 2083-2093.

ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, DENG Zhaohong, PAN Xiang, WANG Shitong. Novel TSK Modeling Method with Joint Group Sparse Learning for Autism Aided Diagnosis[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(12): 2083-2093.

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