Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

ABSTRACT:

Objective

This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.

Methods

We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method.

Results

The MNI detector identified a highe

SUBMITTER: Monsoor T 

PROVIDER: S-EPMC10861270 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature