Antonoudiou, P.; Basu, T.; Maguire, J.

Numerous methods have been proposed for seizure detection automation, yet the tools to harness these methods and apply them in practice are limited. Here we compare four interpretable and widely-used machine learning models (decision tree, gaussian naive bayes, passive aggressive classifier, stochastic gradient descent classifier) on an extensive electrographic seizure dataset collected from chronically epileptic mice. We find that the gaussian naive bayes model achieved the highest precision and f1 score, while also detecting all seizures in our dataset and only requires a small amount of data to train the model and achieve good performance. We use this model to create an open-source python application SeizyML that couples model performance with manual curation allowing for efficient and accurate detection of electrographic seizures.