Self-Learning TSK Fuzzy Epilepsy Assistant Detection Algorithm Incorporating Shallow and Deep Knowledge
SHI Qihuan1,2, ZHANG Xiongtao1,2
1. School of Information Engineering, Huzhou University, Huzhou 313000; 2. Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000
Abstract 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.
Fund:National Natural Science Foundation of China(No.62376094,U22A20102), Zhejiang Provincial Undergraduate Science and Technology Innovation Program(Xinmiao Program)(No.2024R430B021)
Corresponding Authors:
ZHANG Xiongtao, Ph.D., associate professor. His research interests include artificial intelligence, pattern recognition and machine learning.
About author:: SHI Qihuan, Master student. His research interests include data mining, machine learning and pattern recognition.
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