Affiliations: [a] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, People’s Republic of China | [b]
Changshu No. 1 People’s Hospital, Changshu, Jiangsu, People’s Republic of China | [c] Department of Nephrology, the Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, People’s Republic of China | [d] Department of Radiotherapy, Affiliated Hospital, Jiangnan University, Wuxi, Jiangsu, People’s Republic of China | [e]
Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, Jiangsu, People’s Republic of China
Abstract: The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods.
Keywords: EEG signal recognition, epilepsy classification, integrated learning
mechanism, domain adaptation learning, semi-supervised learning, TSK fuzzy system
