Name
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. |
Year | Venue | DocType |
|---|---|---|
2016 | MLHC | Conference |
Volume | Citations | PageRank |
abs/1608.00220 | 18 | 1.01 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Thodoroff | 1 | 20 | 2.13 |
| Joelle Pineau | 2 | 2857 | 184.18 |
| Andrew Lim | 3 | 937 | 89.78 |