基于自回归模型和关联向量机的癫痫脑电信号自动分类

Automatic Classification of Epileptic EEG Signals Based on AR Model and Relevance Vector Machine

癫痫脑电信号自动分类方法的研究具有重要意义。基于自回归模型和关联向量机,实现癫痫脑电信号的自动分类。采用自回归模型,进行脑电信号特征提取;通过引入主成分分析和线性判别分析两种特征变换方法,降低特征空间维数;采用关联向量机作为分类器,提高模型稀疏性并可以得到概率式输出。在对波恩大学癫痫研究中心脑电信号的分类中,所提出的方法最高准确率可以达到99.875%;在将特征空间维数降至原始维数的1/15时,分类准确率仍可达到99.500%;采用关联向量机作为分类器,模型稀疏性大幅提高,与支持向量机相比,同等条件下关联向量数仅为支持向量数的几十分之一。所提方法可以很好地应用于癫痫脑电信号的自动分类。

Automatic classification system of epileptic EEG signals is one very important issue. In this paper a new epileptic EEG signal classification method was proposed on the basis of AR model and relevance vector machine. AR model was used to extract EEG features, and then principle components analysis and linear discriminant analysis were adopted to reduce the dimensionality of feature space. In order to obtain a sparser model and a model with probabilistic outputs, relevance vector machine was chosen as classifier. A publicly- available database was used to test the proposed method： the highest accuracy obtained in this paper is 99. 875% ; and even if the dimensionality of feature space is reduced to 1/15 of the original dimensionality, the classification accuracy was still able to reach 99. 500%. The introduction of relevance vector machine makes the model sparser; the number of relevance vectors is just a few tenths of that of support vectors. The results mentioned above suggest that the method can be well applied in epileptic EEG signal classification.