Learning Robust Features using Deep Learning for Automatic Seizure Detection

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.

• Publication year

  2016

• Venue

  Machine Learning in Health Care

• Publication date

  2016-07-31

• Fields of study

  Medicine, Computer Science, Mathematics

• Identifiers
  arXiv 1608.00220
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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