Classification of Epileptic IEEG Signals by CNN and Data Augmentation

Xuyang Zhao, Jordi Solé-Casals, Binghua Li, Zihao Huang, Andong Wang, Jianting Cao, Toshihisa Tanaka, Qibin Zhao

Abstract: Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often computationally demanding, requiring a large amount of data labeled by physicians, which is time consuming and impractical. In this paper, we firstly introduce a one-dimensional convolutional neural network (1D-CNN) model for epileptic seizure focus detection which avoids the manual, time-consuming feature extraction Moreover, to reduce the necessary number of training samples, we introduce an approach for data augmentation. The experimental results demonstrate the efficiency of the proposed method, with a nearly 3% improvement in performance using the data enhancement method compared to the best result obtained using the traditional feature extraction method.