Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models.

ABSTRACT: This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The

SUBMITTER: Pal Attia T 

PROVIDER: S-EPMC9582039 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature