计算机工程Issue(2)：202-205,210,5.DOI:10.3969/j.issn.1000-3428.2014.02.043
基于支持向量机的多通道癫痫发作预测
Multi-channel Seizure Prediction Based on Support Vector Machine
摘要
Abstract
Epilepsy is a brain disease. As the disease is sudden and repeated, which poses a great threat to safety of patients, effective prediction to seizure is of important significance to prevention and treatment. In this paper, dataset comes from University of Freiburg, Germany Prediction Center. Independent Component Analysis(ICA) is used to remove redundancy. Auto regression model is used to extract multi-channel features of changing trend along with time series. Prediction is transferred to classification by Support Vector Machine(SVM) and filter. All the results can be finally got by Monte Carlo statistical methods. Results show that the models can predict seizures in advance 30 min~70 min with false positive rate nearly zero, which may provide good theoretical basic for developing clinical epilepsy warning system.关键词
癫痫发作预测/自回归模型/特征提取/独立成分分析/支持向量机/蒙特卡洛统计方法Key words
seizure prediction/Autoregression(AR) model/feature extraction/Independent Component Analysis(ICA)/Support Vector Machine(SVM)/Monte Carlo statistics method分类
信息技术与安全科学引用本文复制引用
李志萍..基于支持向量机的多通道癫痫发作预测[J].计算机工程,2014,(2):202-205,210,5.基金项目
留学回国人员科研启动基金资助项目“超大规模网络中突变现象的早期特征提取及其在癫痫预测中的应用” （）
