The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate ...The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.展开更多
将语种和说话人识别的方法应用到英语发音错误检测系统,提出一种基于广义线性区分序列支持向量机(Generalized linear dis-criminant sequence based SVM,GLDS-SVM)的发音错误检测方法.主要创新点为:1)提出一种基于状态拼接的特征规整方...将语种和说话人识别的方法应用到英语发音错误检测系统,提出一种基于广义线性区分序列支持向量机(Generalized linear dis-criminant sequence based SVM,GLDS-SVM)的发音错误检测方法.主要创新点为:1)提出一种基于状态拼接的特征规整方案,增强SVM对发音特征的建模能力;2)提出一种基于多模型融合的模型训练策略,该策略可以更加充分地利用训练数据,并在一定程度上解决了由于真实发音错误数据缺乏造成的正负样本不均衡的问题;3)将GLDS-SVM与基于通用背景模型GMM(Universal background modelsbased GMM,GMM-UBM)的方法进行融合,以进一步提高发音检错性能.GLDS-SVM和GMM-UBM的融合系统在仿真测试集和真实测试集上的等错误率(Equal error rate,EER)分别达到9.92%和16.35%.同时,GLDS-SVM在模型占用空间和运算速度方面均比传统径向基函数(Radial basic function,RBF)核方法具有明显优势.展开更多
基金financially supported by the National Natural Science Foundation of China,No.61263011,81000554Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities,No.11ykpy07+1 种基金Natural Science Foundation of Guangdong Province,No.S2011010005309Innovation Fund of Xinjiang Medical University,No.XJC201209
文摘The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
