• 人工智能 •

### 融合深度和浅层特征的多视角癫痫检测算法

1. 江南大学 数字媒体学院，江苏 无锡 214122
• 出版日期:2020-10-01 发布日期:2020-10-12

### Multi-view Epilepsy Detection Algorithm Combining Deep and Shallow Features

TIAN Xiaobin, DENG Zhaohong, WANG Shitong

1. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China
• Online:2020-10-01 Published:2020-10-12

Abstract:

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.