Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes - Gu, Peipei; Wu, Ting; Zou, Mingyang; Pan, Yijie; Guo, Jiayang; Xiahou, Jianbing; Peng, Xueping; Li, Hailong; Ma, Junxia; Zhang, Ling (Frontiers in Physiology, 2020)
As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and featured with 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 the TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort 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 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 achieve supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision and 83.4% on F1-score, respectively, which outperforms its counterparts. Furthermore, effectiveness of varying head number of multi-head self-attention is assessed, which helps choose optimal number of multi-head. The self-attention learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.