Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

ABSTRACT: Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. However, a large gap is observed between initial accuracies and those observed in clinical practice. In this work, we reproduced and assessed the accuracy of a large number of models, including deep learning networks, for detection of seizures from EEGs. Benchmarking included three different datasets for training and initial testing, and a manually annotated EEG from a local patient for further testing. Random forest and a convolutional neural network achieved the best resul

SUBMITTER: Carvajal-Dossman JP 

PROVIDER: S-EPMC12048661 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature