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.
- Publication:
-
arXiv e-prints
- Pub Date:
- July 2016
- DOI:
- 10.48550/arXiv.1608.00220
- arXiv:
- arXiv:1608.00220
- Bibcode:
- 2016arXiv160800220T
- Keywords:
-
- Computer Science - Machine Learning;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA