Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes

作者：Peipei Gu, Ting Wu, Mingyang Zou, Yijie Pan, Jiayang Guo, Jianbing Xiahou, Xueping Peng, Hailong Li, Junxia Ma, Ling Zhang · 发表于：Frontiers in Physiology · 年份：2020 · DOI：10.3389/fphys.2020.604764 · 被引用次数：20 · 研究领域：Epilepsy research and treatment、EEG and Brain-Computer Interfaces、Fractal and DNA sequence analysis

As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of...