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A new type of fuzzy rule-based system with chaotic swarm intelligence for multi-classification of pain perception from fMRI
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2020-06-01 , DOI: 10.1109/tfuzz.2020.2979150
Ahmed M. Anter , Gan Huang , Linling Li , Li Zhang , Zhen Liang , Zhiguo Zhang

Machine learning has been increasingly used in decoding brain states from functional magnetic resonance imaging (fMRI). One important application is to classify the levels of pain perception from patients’ fMRI for clinical pain assessment. However, the huge number of fMRI features and the complex relationships between fMRI and pain levels affect the performance of pain classification models heavily. In this article, we introduce a new fuzzy-rule-based hybrid optimization approach for dimension reduction and multiclassification problems using chaotic map, crow search optimization (CSO), and self-organizing fuzzy logic prototype (SOFLP). The approach is named as CCSO–SOFLP. In the proposed approach, chaotic map-based CSO is employed to find the optimal features from ultra-high-dimensional fMRI, and the fuzzy-rule-based SOFLP is employed for multiclassification of pain levels. In this sense, CSO is provided to avoid being stuck in local minima and to increase the computational performance. On the other hand, multilayer SOFLP classifier can continuously learn from new data and identify prototypes from the observed data and use them to build fuzzy rules, to define a suitable local area for each prototype, and to avoid overlapping. The proposed approach is applied on a pain-evoked fMRI data set to classify the levels of pain. Results indicate that the proposed approach can decode levels of pain and identify predictive fMRI patterns with higher accuracy and convergence speed and shorter execution time. Therefore, the new type of fuzzy-rule-based system with chaotic swarm intelligence holds great potential to predict pain perception in clinical uses.

中文翻译:

一种基于混沌群智能的新型模糊规则系统,用于 fMRI 疼痛感知的多分类

机器学习越来越多地用于从功能磁共振成像 (fMRI) 解码大脑状态。一个重要的应用是根据患者的 fMRI 对疼痛感知水平进行分类,以进行临床疼痛评估。然而,大量的 fMRI 特征以及 fMRI 与疼痛水平之间的复杂关系严重影响了疼痛分类模型的性能。在本文中,我们介绍了一种新的基于模糊规则的混合优化方法,用于使用混沌映射、乌鸦搜索优化 (CSO) 和自组织模糊逻辑原型 (SOFLP) 来解决降维和多分类问题。该方法被命名为 CCSO-SOFLP。在所提出的方法中,采用基于混沌图的 CSO 从超高维 fMRI 中寻找最佳特征,基于模糊规则的 SOFLP 用于疼痛程度的多分类。从这个意义上说,提供 CSO 是为了避免陷入局部最小值并提高计算性能。另一方面,多层 SOFLP 分类器可以不断地从新数据中学习,从观察到的数据中识别原型,并使用它们来构建模糊规则,为每个原型定义合适的局部区域,并避免重叠。所提出的方法应用于疼痛诱发的 fMRI 数据集,以对疼痛程度进行分类。结果表明,所提出的方法可以解码疼痛程度并以更高的准确性和收敛速度以及更短的执行时间识别预测性 fMRI 模式。所以,
更新日期:2020-06-01
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