SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.

Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source software tools in the scientific community for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient and can be error prone and heavily biased. Here we have developed - SeizyML - an open-source software that combines machine learning models with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning classifiers (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes model detected all seizures in our dataset, had the lowest false detection rate, was robust to misclassifications, and only required a small amount of data to train. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress.