RNN-Based Models for Predicting Seizure Onset in Epileptic Patients
Journal:
arXiv
Published Date:
Dec 24, 2024
Abstract
Early management and better clinical outcomes for epileptic patients depend
on seizure prediction. The accuracy and false alarm rates of existing systems
are often compromised by their dependence on static thresholds and basic
Electroencephalogram (EEG) properties. A novel Recurrent Neural Network
(RNN)-based method for seizure start prediction is proposed in the article to
overcome these limitations. As opposed to conventional techniques, the proposed
system makes use of Long Short-Term Memory (LSTM) networks to extract temporal
correlations from unprocessed EEG data. It enables the system to adapt
dynamically to the unique EEG patterns of each patient, improving prediction
accuracy. The methodology of the system comprises thorough data collecting,
preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are
then used for model training and validation. Results show a considerable
reduction in false alarm rates (average of 6.8%) and an improvement in
prediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-ROC of 93).
Additionally, computational efficiency is significantly higher than that of
existing systems (12 ms processing time, 45 MB memory consumption). About
improving seizure prediction reliability, these results demonstrate the
effectiveness of the proposed RNN-based strategy, opening up possibilities for
its practical application to improve epilepsy treatment.
