Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.

ABSTRACT:

Objective

The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms. S-EPIC is validated on Lennox-Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms.

Methods

We retrospectively collected 10-min scalp EEG clips during non-rapid eye movement (NREM) sleep from 20 subjects with

SUBMITTER: Hu DK 

PROVIDER: S-EPMC11827736 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature