Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

ABSTRACT:

Objective

In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

Methods

Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome.

Results

Overall, in this study, the networks can produce accurate predictions (

SUBMITTER: Lucasius C 

PROVIDER: S-EPMC10979638 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature