Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study.

ABSTRACT:

Background

Visual identification of interictal epileptiform discharge (IED) is expert-biased and time-consuming. Accurate automated IED detection models can facilitate epilepsy diagnosis. This study aims to develop a multimodal IED detection model (vEpiNetV2) and conduct a multi-center validation.

Methods

We constructed a large training dataset to train vEpiNetV2, which comprises 26,706 IEDs and 194,797 non-IED 4-s video-EEG epochs from 530 patients at Peking Union Medical College Hospital (PUMCH). The automated IED detection model was constructed using deep learning based on video and electroencephalogram (EEG) features. We proposed a bad channel removal model and patient detection method to improve the robustness of vEpiNetV2 for multi-center validation. Performance is ver

SUBMITTER: Lin N 

PROVIDER: S-EPMC12357389 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature