Multi-View Learning TSK Fuzzy System Construction Method And Its Application To Epilepsy Diagnosis

Epilepsy is a common neurological disease characterized by recurrent brain dysfunction,which seriously affects the daily life of patients.Traditional epilepsy diagnosis methods mainly rely on the subjective judgment and experience of doctors,which are easily affected by misdiagnosis and missed diagnosis.At the same time,patients need to perform long-term EEG signal recording,resulting in low diagnostic efficiency and long waiting time for patients.With the continuous development of computer technology and artificial intelligence technology,automatic diagnosis of epilepsy using EEG signal automatic recognition and machine learning classification technology has become a research hotspot.Based on the epileptic EEG signal data,this paper realizes the analysis and processing of the EEG signal,and combines the multiview learning mechanism and the TSK fuzzy system algorithm to realize the recognition and diagnosis of the epileptic EEG signal.The specific research work of this paper is as follows:(1)Since the EEG signal is a very complex bioelectrical signal with a high degree of spatiotemporal variability and noise interference,in view of the characteristics of the EEG signal,this paper performs noise reduction processing and feature extraction on the epileptic EEG signal.First use independent component analysis to denoise the data,remove random noise and interference noise in the signal,and improve signal quality;then use wavelet transform,shorttime Fourier transform and kernel principal component analysis methods to extract features,these features can be used for Train a classifier to realize automatic recognition and diagnosis of epilepsy.(2)Compared with the single-view learning method,the multi-view learning method can integrate information from multiple perspectives to understand the characteristics and laws of EEG signals more comprehensively,thereby improving the accuracy and generalization performance of the model,and increasing the robustness of the classification model.Stickiness and reliability.This paper proposes a dual-view and multi-view TSK fuzzy system classification algorithm.By introducing a multi-view collaborative learning mechanism and a multi-view adaptive weighting strategy,the connection between multiple views is strengthened.The algorithm was compared with the experiment and the results were analyzed,which verified that the multi-view TSK fuzzy system classification algorithm proposed in this paper can effectively detect the characteristic waveforms of epileptic EEG signals,thereby realizing the rapid diagnosis of epilepsy diseases and improving the accuracy of epileptic EEG signal recognition rate and is interpretable.(3)The multi-view TSK fuzzy system epilepsy detection algorithm proposed in this paper has achieved good results in the classification of epilepsy EEG signals.In order to further improve the accuracy and efficiency of epilepsy diagnosis,this paper designs an epilepsy EEG signal recognition algorithm based on this algorithm.and diagnostic system.The system can automatically analyze and process EEG signals,and realize accurate epilepsy diagnosis and classification,greatly reducing the risk of misdiagnosis and missed diagnosis.Through this system,doctors can diagnose and treat patients more quickly and accurately.