Treffer: Semi-automated seizure detection using interpretable machine learning models.

A preprint abstract from biorxiv.org discusses the development of a semi-automated seizure detection system using interpretable machine learning models. The study compares four widely-used models and finds that the gaussian naive bayes model achieved the highest precision and f1 score, while also detecting all seizures in the dataset. The researchers have created an open-source python application called SeizyML that combines the model's performance with manual curation to enable efficient and accurate detection of electrographic seizures. It is important to note that this preprint has not yet undergone peer review. [Extracted from the article]