Epileptic Seizure Prediction with Multi-View Convolutional Neural Networks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageEnglish
Article number8910555
Pages (from-to)170352-170361
Number of pages10
JournalIEEE Access
StatePublished - 22 Nov 2019


  • Electroencephalograms (EEG)
  • convolutional neural network (CNN)
  • multi-view CNN
  • representation learning
  • seizure prediction


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