Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Xuyang Zhao; Jordi Solé-Casals; Binghua Li; Zihao Huang; Andong Wang; Jianting Cao; Toshihisa Tanaka; Qibin Zhao

Publisher name:  IEEE

DOI:  10.1109/ICASSP40776.2020.9052948

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9052948