融合深度和浅层特征的多视角癫痫检测算法

Multi-view Epilepsy Detection Algorithm Combining Deep and Shallow Features

癫痫是一种常见的精神疾病。通过分析EEG信号可以监控癫痫病人的状态,在病人发病时及时地发现并且介入来保护病人的生命安全。在癫痫检测研究中,如何获得有效的特征和构建有效的分类器是癫痫检测和识别的关键。为了获得更好的癫痫检测效果,提出了一种融合深度和浅层特征的多视角癫痫检测算法。该算法首先使用FFT和WPD来获取EEG信号频域和时频域的浅层特征;然后使用CNN网络学习得到频域和时频域的深度特征;进一步使用多视角TSK模糊系统对浅层和深度特征进行分类模型的构建。实验研究表明,在EEG信号癫痫检测方面,提出的浅层特征和深度特征的效果与PCA、LDA等常用的特征提取方法相比均高出1%以上;使用融合深度特征和浅层特征的多视角癫痫检测算法的分类效果比单视角算法的检测效果均高出1%以上,比单视角算法的平均检测效果高出5%以上。

Epilepsy is a common neurological illness.By analyzing the electroencephalo-graph(EEG)signals of patients with epilepsy,their conditions can be monitored so that seizure can be detected and intervened in time.In epilepsy research,how to obtain effective features and construct an effective classifier is the key to epilepsy detection and recognition.In order to obtain better epilepsy detection effect,this paper proposes a multi-view seizure detection algorithm that combines deep and shallow features.The method first uses FFT(fast Fourier transform)and WPD(wavelet packet decomposition)to obtain the shallow features in the frequency domain and the time-frequency domain.Then,CNN(convolutional neural network)is used to obtain the deep features in the frequency domain and the time-frequency domain.Further the multi-view TSK fuzzy system is used to construct the classification model of shallow and deep features.Experimental studies show that in the detection of EEG signal epilepsy,the effects of shallow features and deep features proposed in this paper are more than 1%higher than those of commonly used feature extraction methods such as PCA(principal component analysis),LDA(linear discriminant analysis)and etc.The multi-view epileptic detection algorithm which combines deep features and shallow features has a classification effect higher than that of the single-view algorithm by more than 1%,and the average detection effect is more than 5%higher than that of the single-view algorithm.