DynamoSort: Using machine learning approaches for the automatic classification of seizure dynamotypes.

ABSTRACT:

Objective

Epilepsy is characterised by unprovoked and recurring seizures, which can be electrically measured using electroencephalograms (EEG). To better understand the underlying mechanisms of seizures, researchers are exploring their temporal dynamics through the lens of dynamical systems modelling. Seizure initiation and termination patterns of spiking amplitude and frequency can be sorted into "dynamotypes", which may be able to serve as biomarkers for intervention. However, manual classification of these dynamotypes requires trained raters and is prone to variability. To address this, we developed DynamoSort, a machine-learning algorithm for automatic seizure onset and offset classification.

Methods

We used approximately 2100 seizures from an intra-amygdala kainic acid

SUBMITTER: Wooley J 

PROVIDER: S-EPMC11870418 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature