An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework

Résumé fourni par la source

Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.