Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction

Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction

ObjectiveSeizure prediction could improve quality of life for patients through removing uncertainty and providing an opportunity for acute treatments. Most seizure prediction models use feature engineering to process the EEG recordings. Long-Short Term Memory (LSTM) neural networks are a recurrent n...

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Bibliographic Details
Main Authors: Jordan D. Chambers, Mark J. Cook, Anthony N. Burkitt, David B. Grayden
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Neuroscience
Subjects:
epilepsy
EEG
seizure prediction
machine learning
long short-term memory
LSTM
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2024.1472747/full

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https://www.frontiersin.org/articles/10.3389/fnins.2024.1472747/full

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