Epileptic Seizure Prediction with Multi-View Convolutional Neural Networks

Chien-Liang Liu*, Bin Xiao, Wen Hoar Hsaio, Vincent Shin-Mu Tseng

*此作品的通信作者

研究成果: Article同行評審

77 引文 斯高帕斯(Scopus)

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.

原文English
文章編號8910555
頁(從 - 到)170352-170361
頁數10
期刊IEEE Access
7
DOIs
出版狀態Published - 22 11月 2019

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