Delay Time-Based Epileptic EEG Detection Using Artificial Neural Network
The electroencephalogram (EEG) signal is very important for the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. A neural-network-based automated epileptic EEG detection method is proposed in this paper, which uses delay time as the input feature of an artificial neural network. Mutual information method is applied in this paper for computing the delay time parameter of EEG signals. The results indicate that the delay time values of EEG signals during an epileptic seizure become larger than those of normal EEG signals obviously, and then this phenomenon is utilized for automated epileptic EEG detection combined with probabilistic neural networks (PNN). Delay time parameter is used as the input feature of the neural network for the first time for the detection of epilepsy. It is shown that the overall accuracy as high as 100％ can be achieved by using the method proposed in this paper.
delay time electroencephalogram (EEG) mutual information artificial neural network (ANN) epilepsy probabilistic neural network (PNN) seizure
Ye Yuan Yue Li Dongyan Yu Danilo P. Mandic
College of Communication Engineering Jilin University Changchun, China Department of Electrical and Electronic Engineering Imperial College London, UK
国际会议
上海
英文
502-505
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）