An automated system for epilepsy detection using EEG brain signals based on deep learning approach

作者：

Highlights：

• Proposed P-1D-CNN model for detecting epilepsy that has far less learnable parameters.

• To deal with the small amount of available data, proposed two augmentation schemes.

• Proposed an epilepsy detection system as an ensemble of P-1D-CNN models.

• Thoroughly evaluated the augmentation schemes and the deep models.

• The system gives an accuracy of 99.1 ± 0.9% on the University of Bonn dataset.

摘要

•Proposed P-1D-CNN model for detecting epilepsy that has far less learnable parameters.•To deal with the small amount of available data, proposed two augmentation schemes.•Proposed an epilepsy detection system as an ensemble of P-1D-CNN models.•Thoroughly evaluated the augmentation schemes and the deep models.•The system gives an accuracy of 99.1 ± 0.9% on the University of Bonn dataset.

论文关键词：Electroencephalogram (EEG),Epilepsy,Seizure,Ictal,Interictal,1D-CNN

论文评审过程：Received 8 July 2017, Revised 13 April 2018, Accepted 14 April 2018, Available online 18 April 2018, Version of Record 27 April 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.04.021