Learning Robust Features Using Deep Learning for Automatic Seizure Detection
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.
Cite
Text
Thodoroff et al. "Learning Robust Features Using Deep Learning for Automatic Seizure Detection." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.Markdown
[Thodoroff et al. "Learning Robust Features Using Deep Learning for Automatic Seizure Detection." Proceedings of the 1st Machine Learning for Healthcare Conference, 2016.](https://mlanthology.org/mlhc/2016/thodoroff2016mlhc-learning/)BibTeX
@inproceedings{thodoroff2016mlhc-learning,
title = {{Learning Robust Features Using Deep Learning for Automatic Seizure Detection}},
author = {Thodoroff, Pierre and Pineau, Joelle and Lim, Andrew},
booktitle = {Proceedings of the 1st Machine Learning for Healthcare Conference},
year = {2016},
pages = {178-190},
volume = {56},
url = {https://mlanthology.org/mlhc/2016/thodoroff2016mlhc-learning/}
}