Seizure detection using ultra-long-term subcutaneous electroencephalography: A deep learning CNN-BiLSTM approach.

ABSTRACT:

Objective

This study reports development and validation of a deep learning seizure detection algorithm for two-channel subscalp electroencephalographic (EEG) recordings. Ultra-long-term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra-long-term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers.

Methods

The convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded

SUBMITTER: Park S 

PROVIDER: S-EPMC12927827 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature