Recognition of multivariate epilepsy EEG signals based on view-to-rule deep TSK fuzzy classifier
Traditional machine learning methods perform poorly in classification and detection epilepsy electroencephalogram(EEG)signals,while the state-of-the-art deep learning models show excellent predictive performance due to their powerful feature abstraction capabilities,but their behavior is black-box,leading to uninterpretable and not well suited for clinical diagnosis.Moreover,the existing multi-view deep TSK fuzzy system is difficult to effectively represent the correlation between the features of each view.To address the problems above,in this paper,we propose a view-to-rule deep TSK fuzzy classifier,i.e.,VR-TSK-FC,and apply it to multivariate epilepsy EEG signal detection.The proposed classifier constructs antecedent-part of fuzzy rules on the original data to ensure interpretability,and the one-dimensional convolutional neural network(1D-CNN)learns deep features of multivariate EEG signals from multi-view.The consequent-part of each fuzzy rule adopts the EEG signal deep feature of each view as its consequent-part variable,and the fuzzy-deep view-to-rule learning method improves the representation ability of the proposed VR-TSK-FC.Experiments on the Bonn and CHB-MIT datasets demonstrate that,the fuzzy logic inference process of the proposed VR-TSK-FC achieves better classification results as well as concise interpretability.
Takagi-Sugeno-Kang(TSK)fuzzy classifiermulti-view deep featuresview-to-ruleepileptic EEG signal detectioninterpretability
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