脑电波信号多域变换与深度学习癫痫诊断  

Multi-domain Transformation of EEG and Deep Learning Epilepsy Diagnosis

作　　者：陈浩滨 葛薇 杨超[1] 郑霖[1] CHEN Haobin;GE Wei;YANG Chao;ZHENG Lin(Guangxi Key Laboratory of Wireless Wideband Communications and Signal Processing,Guilin University of Electronic Technology,Guilin 541004,China;College of Humanities and Management,Guilin Medical University,Guilin 541004,China)

机构地区：[1]桂林电子科技大学广西无线宽带通信与信号处理重点实验室,广西桂林514004 [2]桂林医学院人文与管理学院,广西桂林514004

出　　处：《现代信息科技》2022年第20期6-10,共5页Modern Information Technology

基　　金：广西自然科学基金资助项目(2020GXNSFAA159067);教育部认知无线电重点实验室基金资助项目(CRKL200102,CRKL200105)。

摘　　要：单一维度的特征检测使现有基于EEG的癫痫诊断准确性受到限制。通过将EEG转换成格拉姆角场图和小波时频图,构建一种由2个2维CNN和1个DNN的集成深度学习模型,2个2维CNN分别提取格拉姆角场图和小波时频图的特征并融合,将融合特征输出至DNN以进行癫痫融合识别。借助波恩大学的脑电数据集测试了该集成深度学习模型的有效性,结果表明,该模型对癫痫EEG识别的准确度、特异性以及敏感度分别为96.5%、95.0%以及96.0%,整体识别性能优于传统的单神经网络模型,可为癫痫等疾病的诊断提供更好的辅助功能。Feature detection in a single dimension limits the accuracy of existing EEG-based epilepsy diagnosis.By converting EEG into Gram angle field map and wavelet time-frequency map,an integrated deep learning model consisting of two 2D CNNs and one DNN is constructed.Two 2D CNNs extract and fuse the features of Gram angle field map and wavelet time-frequency map respectively,and output the fused features to DNN for epileptic fusion recognition.The effectiveness of the integrated deep learning model is tested with the EEG dataset of the University of Bonn.The results show that the accuracy,specificity and sensitivity of the model for EEG recognition of epilepsy are 96.5%,95.0% and 96.0%,respectively.The overall recognition performance is better than the traditional single neural network model,which can provide better auxiliary functions for the diagnosis of epilepsy and other diseases.

关 键 词：深度学习 癫痫 卷积神经网络 连续小波变换 格拉姆角场 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]