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http://dx.doi.org/10.7471/ikeee.2018.22.4.1175

Epileptic Seizure Detection for Multi-channel EEG with Recurrent Convolutional Neural Networks  

Yoo, Ji-Hyun (Dept. of Internet Communications, Jangan University)
Publication Information
Journal of IKEEE / v.22, no.4, 2018 , pp. 1175-1179 More about this Journal
Abstract
In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients using EEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEG signal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatial and temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed a sensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.
Keywords
recurrent CNN; deep learning; epileptic seizure detection; EEG; load balancin and simulation;
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