Classification of Epileptic IEEG Signals by CNN and Data Augmentation

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.

Recommended citation: Xuyang Zhao, Jordi Sol ́e-Casals, Binghua Li, Zihao Huang, Andong Wang, Jianting Cao, Qibin Zhao. Classification of Epileptic IEEG Signals by CNN and Data Augmentation. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020). IEEE, 2020: 926-930.
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