Multiview Transfer Representation Learning With TSK Fuzzy System for EEG Epilepsy Detection

Abstract

Automatic analysis of epileptic encephalography (EEG) signals with intelligent models can greatly reduce the workload of doctors. However, the lack of data, insufficient labels, and inconsistent data distribution in real-world scenarios significantly affect the performance of intelligent models. Transfer learning plays an important role in solving the above problems but some challenges remain. First, while various feature extraction methods are available to extract features from the original epilepsy signal, it is difficult to determine which features are effective. Second, transfer learning may lead to domain information loss since the original feature representation from different domains is changed. Third, most of the existing models lack transparency to provide medical practitioners confidence of use. To this end, this paper proposes the novel method Multi-view Information Preservation Transfer Representation Learning based on Fuzzy Systems (MIP-TRL-FS) to address the issues. First, MIP-TRL-FS utilizes multiple views to get rid of the feature selection process. Second, information preservation techniques are utilized to maintain the data information from the aspects of sample level and feature level, thus minimizing information loss during the transfer learning process. Third, by using Takagi-Sugeno-Kang Fuzzy Systems (TSK-FS) as the base model, the output of the proposed method can be interpreted linguistically with IF-THEN rules to makes the model transparent. Extensive experiments were conducted on the CHB-MIT dataset and the results demonstrate the effectiveness of the proposed method.