融合深浅层次知识的自学习TSK模糊癫痫辅助检测算法  

Selff-Learning TSK Fuzzy Epilepsy Assistant Detection Algorithm Incorporating Shallow and Deep Knowledge

作　　者：施奇环 张雄涛 SHI Qihuan;ZHANG Xiongtao(School of Information Engineering,Huzhou University,Huzhou 313000;Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources,Huzhou University,Huzhou 313000)

机构地区：[1]湖州师范学院信息工程学院,湖州313000 [2]湖州师范学院浙江省现代农业资源智慧管理与应用研究重点实验室,湖州313000

出　　处：《模式识别与人工智能》2025年第1期82-93,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.62376094,U22A20102);浙江省大学生科技创新活动计划项目(新苗人才计划)(No.2024R430B021)资助。

摘　　要：Takagi-Sugeno-Kang(TSK)模糊分类器在癫痫检测中用于处理模糊信息.然而,由于癫痫脑电信号复杂、患者发作表现多样,一阶TSK模糊分类器通常难以从训练样本中获取足够的泛化性能.因此,文中提出融合深浅层次知识的具有自我学习能力的TSK模糊分类算法(Deep-Shallow Mix Self-Learning TSK,DSMT),用于癫痫辅助检测.DSMT引入类似人类“反思-归纳”的深度规则,增强模型对于潜在信息的挖掘能力,并通过静态-动态孪生的网络结构,使用模型的内部知识代替知识蒸馏中常见的教师模型.在静态网络中,DSMT使用不同批次输出中隐藏的浅层知识进行自我学习.在动态网络中,DSMT记录静态孪生网络输出作为深层知识,结合深层知识与浅层知识,借助TSK模糊分类器对模糊信息的敏感性,学习整合深浅层次的知识,实现低阶TSK模型的自我学习,提高癫痫辅助检测系统的自适应性.此外,DSMT采用同温度蒸馏的策略,优化知识传递的效率.在CHB-MIT、TUAB、TUEV真实癫痫数据集上的实验验证DSMT的有效性.The Takagi-Sugeno-Kang(TSK)fuzzy classifier exhibits exceptional performance in handling fuzzy information for epilepsy detection.However,Due to the complexity of epileptic electroencephalogram(EEG)signals and the diverse manifestations of seizures among patients,first-order TSK fuzzy classifiers often struggle to achieve sufficient generalization from training samples.A TSK fuzzy classifier with deep and shallow self-learning knowledge integration,namely deep-shallow mix self-learning TSK(DSMT),is proposed.In DSMT,deep rules akin to human"reflection-induction"are introduced to enhance the ability of the model to mine latent information.The commonly used teacher model in knowledge distillation is replaced by the internal knowledge of the model through a static-dynamic Siamese network structure.In the static network,shallow knowledge hidden in the outputs from different batches is employed.In the dynamic network,the outputs of the static Siamese network are recorded as deep knowledge,and deep knowledge and shallow knowledge are combined.The sensitivity of the TSK fuzzy classifier to fuzzy information is leveraged to integrate both types of knowledge.DSMT enables self-learning of the first-order TSK model and improves the adaptability of the epilepsy detection system.Additionally,an optimal temperature distillation strategy is utilized to optimize knowledge transfer efficiency.Experiments on the real epilepsy datasets,CHB-MIT,TUAB,and TUEV,verify the effectiveness of DSMT.

关 键 词：“反思-归纳”过程 自蒸馏 脑电信号 孪生网络 TSK(Takagi-Sugeno-Kang)模糊分类器 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]