標題: Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks
作者: Liu, Chien-Liang
Xiao, Bin
Hsaio, Wen-Hoar
Tseng, Vincent S.
交大名義發表
資訊工程學系
工業工程與管理學系
National Chiao Tung University
Department of Computer Science
Department of Industrial Engineering and Management
關鍵字: Electroencephalography;Feature extraction;Brain modeling;Time-domain analysis;Epilepsy;Time-frequency analysis;Electroencephalograms (EEG);seizure prediction;convolutional neural network (CNN);multi-view CNN;representation learning
公開日期: 1-一月-2019
摘要: The unpredictability of seizures is often considered by patients to be the most problematic aspect of epilepsy, so this work aims to develop an accurate epilepsy seizure predictor, making it possible to enable devices to warn patients of impeding seizures. To develop a model for seizure prediction, most studies relied on Electroencephalograms (EEGs) to capture physiological measurements of epilepsy. This work uses the two domains of EEGs, including frequency domain and time domain, to provide two different views for the same data source. Subsequently, this work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features. By conducting experiments on Kaggle data set, we demonstrated that the proposed method outperforms all methods listed in the Kaggle leader board. Additionally, our proposed model achieves average area under the curve (AUCs) of 0.82 and 0.89 on two subjects of CHB-MIT scalp EEG data set. This work serves as an effective paradigm for applying deep learning approaches to the crucial topic of risk prediction in health domains.
URI: http://dx.doi.org/10.1109/ACCESS.2019.2955285
http://hdl.handle.net/11536/155357
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2955285
期刊: IEEE ACCESS
Volume: 7
起始頁: 170352
結束頁: 170361
顯示於類別:期刊論文