Detecting Epileptic Seizures from EEG Data using Neural Networks
This work addresses a critical medical need for early seizure detection in epilepsy patients, but it is incremental as it applies an existing neural network method to a specific dataset.
The paper tackled the problem of predicting epileptic seizures from EEG data using neural networks with dropout, achieving high sensitivity and specificity on unseen patient records through leave-one-out cross-validation.
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.