牛云云:Identification of epilepsy from intracranial EEG signals by using different neural network models
2021-03-04 发布:[信息工程学院] 点击:22次
In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis.
上述成果发表在期刊《COMPUTATIONAL BIOLOGY AND CHEMISTRY》上:Gong,C(Gong,Chen)[1];Zhang,XX(Zhang,Xiaoxiong)[2];Niu,YY(Niu,Yunyun)[1]. Identification of epilepsy from intracranial EEG signals by using different neural network models. COMPUTATIONAL BIOLOGY AND CHEMISTRY,87.
校址:北京市海淀区学院路29号 邮编:100083
版权所有中国地质大学(北京) 文保网安备案:1101080023
校址:北京市海淀区学院路29号
邮编:100083
技术支持:信息网络与数据中心
@版权所有:中国地质大学(北京)
文保网安备案:1101080023