离散差分模块在癫痫脑电分类中的应用

针对癫痫脑电信号多分类的精度提升问题,提出了一种基于信号转差分模块与卷积模块结合的分类算法。信号转差分模块对原始脑电信号进行多阶差分运算,得到描述其波动特征的差分表示;然后卷积模块动态学习的方式将差分脑电信号转换为图片,利用预训练的卷积神经网络来提取信号特征并实现自动分类。分类结果表明,与现有研究相比,所提出的方法的最高提升了8.1%的分类准确率。在两分类问题上达到了99.8%的分类准确率,在三分类问题上获得了92.8%的准确率,在五分类问题上取得了86.7%的准确率。说明信号转差分模块对于脑电信号分类问题有积极作用。

Signal to difference module in epileptic electroencephalogram classification

Targeting at improving the accuracy of multi-classification problems of epileptic EEG signals, one algorithm based on the combination of a signal to difference module and convolutional module is proposed. The signal to difference module performs multi-order differential operations on raw EEG signals to obtain its incremental representation which depicts the fluctuation features of EEG signals. Then, this representation is converted to images by convolutional module using dynamic learning parameters rather than static transformation. And pre-trained convolutional networks are applied to extract features and classify them automatically. The classification results show that this method improves the classification performance by up to 8.1% when compared to recent researches. This method achieved 99.8% accuracy in two-class classification problems, 92.8% accuracy in three-class classification problems and 86.7% accuracy in five-class classification problems, which indicates that signal to difference module has an important effect on EEG classification problem.