Expert-Level Detection of Epilepsy Markers in EEG on Short and Long Timescales.

ABSTRACT:

Background

Epileptiform discharges, or spikes, within electroencephalogram (EEG) recordings are essential for diagnosing epilepsy and localizing seizure origins. Artificial intelligence (AI) offers a promising approach to automating detection, but current models are often hindered by artifact-related false positives and often target either event- or EEG-level classification, thus limiting clinical utility.

Methods

We developed SpikeNet2, a deep-learning model based on a residual network architecture, and enhanced it with hard-negative mining to reduce false positives. Our study analyzed 17,812 EEG recordings from 13,523 patients across multiple institutions, including Massachusetts General Brigham (MGB) hospitals. Data from the Human Epilepsy Project (HEP) and SCORE-AI (SAI)

SUBMITTER: Li J 

PROVIDER: S-EPMC12276842 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature