基于希尔伯特黄变换的癫痫自动检测

希尔伯特黄变换是由经验模态分解和希尔伯特变换所组成的，在非线性、非稳态信号处理方面具有独特的优势。本文首先对脑电波信号进行模态分解，然后根据希尔伯特变换求得各本征模态函数的瞬时频率并依此计算出均值、方差及其核心频率区间等特征，并选取若干个本征模态函数的频率特征组成一个长的特征向量,称之为希尔伯特黄频率特征环。根据该特征向量,本文进一步采用支持向量机对癫痫和非癫痫脑电波信号进行学习和分类，并采用格点搜索的方法来选取支持向量机中的最优参数。通过在典型癫痫脑电波数据集上的5重交叉验证得出本文所提出的新方法在分类准确率上已经超越或接近现有的分类方法。 

Automatic Detection of Epileptic Seizure Through Hilbert Huang Transform

Hilbert Huang Transform (HHT) consists of the Empirical Mode Decomposition (EMD) and Hilbert Transform (HT), which has certain advantages over traditional signal processing methods on nonlinear and non stationary signal analysis due to its complete adaptability and flexibility of signal decomposition. In this paper, we begin to use the EMD to analyze the EEG signals, i.e., decompose each EEG signal into a number of Intrinsic Mode Functions (IMFs). Then, HT is implemented on these IMFs and thus the mean, variance as well as the core frequency interval of each IMF can be extracted to form the IMFs feature. The important IMFs of an EEG signal are selected and their features are combined together to form the feature vector of the EEG signal, being referred to as the Hilbert Huang frequency ring. According to these feature vectors of the EGG signals, we utilize the support vector machine to learn and make the epileptic seizure and epileptic seizure free classification and the grid search technique is used to optimize the parameters. It is demonstrated by the experiments on a typical epileptic seizure dataset using the five fold cross validation that our proposed method can achieve the state of the art performance on epileptic seizure automatic detection.