We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG). The input to the neural network is a 126 feature vector containing 9 features for each of the 14 EEG channels obtained over 1-second, non-overlapping windows. The models in our experiments achieved high sensitivity and specificity on patient records not used in the training process. This is demonstrated using leave-one-out-cross-validation across patient records, where we hold out one patient's record as the test set and use all other patients' records for training; repeating this procedure for all patients in the database.
@article{arxiv.1412.6502,
title = {Detecting Epileptic Seizures from EEG Data using Neural Networks},
author = {Siddharth Pramod and Adam Page and Tinoosh Mohsenin and Tim Oates},
journal= {arXiv preprint arXiv:1412.6502},
year = {2019}
}
Comments
This paper has been withdrawn by the authors due to an error discovered in the experiments