基于小波变换和AdaBoost极限学习机的癫痫脑电信号分类

针对单一极限学习机(ELM)在癫痫脑电信号研究中分类结果不稳定、泛化能力差的缺陷,提出一种基于互信息(MI)的AdaBoost极限学习机分类算法。该算法将AdaBoost引入到极限学习机中,并嵌入互信息输入变量选择,以强学习器最终的性能作为评价指标,实现对输入变量以及网络模型的优化。利用小波变换(WT)提取脑电信号特征,并结合提出的分类算法对UCI脑电数据集以及波恩大学癫痫脑电数据进行分类。实验结果表明,所提方法相比传统方法以及其他同类型研究,在分类精度和稳定性上有着明显提高,并具有较好的泛化性能。

Epileptic EEG signals classification based on wavelet transform and AdaBoost extreme learning machine

Aiming at solving the problem of unstable predicted results and poor generalization ability when a single Extreme Learning Machine (ELM) was treated as a classifier in the research of automatic epileptic ElectroEncephaloGram (EEG) signals classification, a classification method of AdaBoost ELM based on Mutual Information (MI) was put forward. The algorithm embedded the MI variable selection into AdaBoost ELM, regarded the final performance of the strong leaner as evaluation index, and realized the optimization of input variables and network model. Wavelet Transform (WT) was used to extract the feature of EEG signal, and the proposed classification algorithm was used to classify the UCI EEG datasets and epileptic EEG datasets of the University of Bonn. The experimental results show that compared to traditional methods and other similar studies, the proposed method significantly has improvement in the classification accuracy and stability, and has better generalization performance.