Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals
Citations Over TimeTop 1% of 2021 papers
Abstract
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. In conclusion, the Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.
Related Papers
- → Ictal and interictal high frequency oscillations in patients with focal epilepsy(2010)177 cited
- → The relationship between interictal and ictal paroxysms in an in vitro model of focal hippocampal epilepsy(1988)154 cited
- → Electroencephalography and video-electroencephalography(2012)17 cited
- → CHILDREN WITH INTRACTABLE FOCAL EPILEPSY: ICTAL AND INTERICTAL 99TcVi HMPAO SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY(1995)40 cited
- → B.03 Registered EEG technologists can accurately identify ictal and interictal epileptiform patterns on routine EEG(2018)