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Learning Robust Features using Deep Learning for Automatic Seizure Detection

Pierre Thodoroff, Joelle Pineau, Andrew Lim
Proceedings of the 1st Machine Learning for Healthcare Conference, pp. 178–190, 2016

Abstract

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.

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