基于多尺度卷积特征融合的癫痫脑电信号识别

脑电信号(electroencephalography,EEG)已成为医生诊断神经系统疾病最 广泛使用的工具,实现癫痫EEG的自动识别对 于癫痫患者的临床诊断和治疗具有重要意义。为了提高癫痫EEG的识别精度,提出了一 种基于多尺 度卷积特征融合的癫痫EEG自动识别模型。首先采用多尺度卷积特征融合方法提取多粒 度数据特征, 实现卷积神经网络(convolutional neural network,CNN)中不同层次的信息互补；然后经过长短期记忆网络(long short-term memory network,LSTM)提取时间 特征,利用 softmax分类器给出最终的识别结果。为了评估提出方法的识别性能,在波恩大学癫痫病研 究中心数据集 中进行实验,并与CNN-LSTM模型、单一的LSTM等模型的识别性能进行了比较,实验结果表 明,提出 方法的识别精度明显高于其余方法, 平均可达到99.19%。该模型能够 有效识别癫痫EEG类别,具有较高的识别性能和临床应用潜力。

Epileptic EEG signal recognition based on multi-scale convolution feature fusio n

Electroencephalography (EEG) has become the most widely used tool for doctors to diagnose nervous system diseases.It is of great significance to realize automatic recognition of epileptic EEG signals of the clinical diagnosis and treatment of epilepsy patients.In order to improve the recognition precision,this paper proposes a kind of automatic recognition model based on multi-scale convolution feature fusion of epileptic EEG signals.First of all,the multi-scale convolution features fusion method is used to extract more granularity data and solve the problem of information complementation at different levels in convolutional neural network (CNN).Then,the temporal features are extracted by long short-term memory network (LSTM),and the final recognition results are given by softmax classifier.The experiment is completed on the Epilepsy Research Center at the University of Bonn experiment data set.The proposed model is compared with CNN-LSTM model, the single LSTM model,et al.The experimental results show that the recognition precision of the proposed method is higher than other method,the average accuracy is 99.19%.The model could recognize epileptic EEG category,has excellent recognition performance and clinical application potential.