【摘要】 脑电信号的非线性特征会随癫痫发作而改变,脑电信号的特征分析和检测对癫痫的诊断和治疗具有重要意义。提出对癫痫脑电信号进行毯子维和分形截距的特征分析,并将分形截距应用于癫痫脑电信号的检测。首先提取脑电信号的分形截距和毯子维特征,并对两种特征的均值和方差进行比较,最后使用支持向量机分类器,实现脑电信号的分类检测。发现癫痫发作时脑电信号的分形截距显著高于发作间期,而脑电信号的毯子维在发作前后变化规律则不明显。将分形截距作为分类特征,能有效地区分癫痫脑电与间歇期脑电,具有较强的癫痫脑电检测性能,分类检测的准确率达到96%以上。更多还原

【Abstract】 Nonlinear features of electroencephalogram(EEG) vary with epileptic seizure,and the feature analysis and detection of epileptic EEG are significant in diagnosis and therapy of epilepsy.This paper presents an epileptic EEG analysis approach based on blanket dimension and fractal intercept features,and applies fractal intercept to epileptic EEG detection.We extract fractal intercept and blanket dimension features of EEG,and compare the mean and variance of those two features.Then,a support vector machine is applied to classify epileptic EEG signals.It is found that the fractal intercept features of EEG during epileptic seizure are significantly higher than interictal EEG’s,while the blanket dimension features of EEG show no significant differences before and after seizures.The fractal intercept as a classification feature could be used to distinguish epileptic EEG from interical EEG with high performance for seizure detection,and the classification accuracy is up to 96%.更多还原