Journal of Frontiers of Computer Science and Technology ›› 2023, Vol. 17 ›› Issue (2): 442-452.DOI: 10.3778/j.issn.1673-9418.2104089

• Artificial Intelligence·Pattern Recognition • Previous Articles     Next Articles

Multi-head Self-attention Neural Network for Detecting EEG Epilepsy

TONG Hang, YANG Yan, JIANG Yongquan   

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
  • Online:2023-02-01 Published:2023-02-01



  1. 西南交通大学 计算机与人工智能学院,成都 611756

Abstract: Epilepsy is a life-threatening and challenging nervous system disease. There are still many challenges in the detection of epilepsy based on electroencephalogram (EEG). Because the EEG signal is unstable, different patients show different seizure patterns. In addition, EEG detection is time-consuming and laborious, which will not only bring heavy burden to medical staff, but also easily lead to false detection. Therefore, it is necessary to study an efficient automatic epilepsy detection technology across multiple patients. In this paper, an epileptic EEG detection method (convolutional attention bidirectional long short-term memory network, CABLNet) based on the multi-head self-attention mechanism neural network is proposed. Firstly, the convolution layer is used to capture short-term temporal patterns of EEG time series and local dependence among channels. Secondly, this paper uses the multi-head self-attention mechanism to capture the long-distance dependence and time dynamic correlation of the short-term time pattern feature vectors with temporal relationship. Thirdly, the context representation is sent into a bidirectional long short-term memory (BiLSTM) to extract the information in the front and back directions. Finally, logsoftmax function is used for training and classification. Using CHB-MIT scalp EEG database data, the sensitivity, specificity, accuracy and F1-score are 96.18%, 97.04%, 96.61% and 96.59% respectively. The results show that the proposed method is superior to the existing methods and significantly improved in epilepsy detection performance, which is of great significance to the auxiliary diagnosis of epilepsy.

Key words: electroencephalogram (EEG), epilepsy, seizure detection, deep learning, multi-head self-attention mechanism

摘要: 癫痫是一种危及生命且具有挑战性的神经系统疾病,目前基于脑电图(EEG)的癫痫检测方法依然存在很多挑战,脑电图信号是不稳定的,不同的病人表现出的癫痫发作模式不同,检测脑电信号耗时费力,不仅会给医务人员带来沉重的负担,还容易造成误检情况的发生。因此,研究高效的跨多患者的癫痫自动检测技术是非常有必要的。提出了一种基于多头自注意力机制神经网络的癫痫脑电检测方法(CABLNet),利用卷积层捕获脑电时序信号的短期时间模式和各通道之间的局部依赖关系,使用多头自注意力机制进一步捕获具有时序关系的短期时间模式特征向量的长距离依赖关系和时间动态相关性,将上下文表示送入双向长短时记忆网络(BiLSTM)提取前后方向的信息,用logsoftmax函数进行训练和分类。实验使用CHB-MIT头皮脑电数据库数据,灵敏度、特异性、准确率、F1-score分别为96.18%、97.04%、96.61%、96.59%,结果表明,提出的方法优于现有方法,在癫痫检测性能方面有显著提高,对癫痫的辅助诊断具有重要意义。

关键词: 脑电图, 癫痫, 癫痫检测, 深度学习, 多头自注意力机制