Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human.

ABSTRACT:

Objective

Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial EEG (iEEG) recordings and whether this mechanism-driven distinction could be simulated by a deep generative model.

Methods

In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 HFOs across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input i

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11261948 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature