Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection

Xiaobin Tian, Zhaohong Deng, Wenhao Ying, Kup Sze Choi, Dongrui Wu, Bin Qin, Jun Wang, Hongbin Shen, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)


Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram (EEG) signals of patients with epilepsy, their conditions can be monitored and seizure can be detected and intervened in time. As the identification of effective features in EEG signals is important for accurate seizure detection, this paper proposes a multi-view deep feature extraction method in attempt to achieve this goal. The method first uses fast Fourier transform (FFT) and wavelet packet decomposition (WPD) to construct the initial multi-view features. Convolutional neural network (CNN) is then used to automatically learn deep features from the initial multi-view features, which reduces the dimensionality and obtain the features with better seizure identification ability. Furthermore, the multi-view Takagi-Sugeno-Kang fuzzy system (MV-TSK-FS), an interpretable rule-based classifier, is used to construct a classification model with strong generalizability based on the deep multi-view features obtained. Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD. The classification accuracy is also at least 4% higher than the average accuracy achieved with single-view deep features.

Original languageEnglish
Article number8832223
Pages (from-to)1962-1972
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number10
Publication statusPublished - Oct 2019


  • deep learning
  • EEG
  • feature extracting
  • multi-view
  • seizure detection

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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