An algorithm for seizure detection in rodents

Epilepsy animal research often relies on long-term intracranial electroencephalographic (iEEG) recordings. Here, we describe an artificial neural network (ANN) algorithm for automatic detection of seizures.

The algorithm was trained on iEEG recordings of three mouse models of chronic epilepsy: (1) the pilocarpine model of epilepsy induced by status epilepticus; (2) the albumin model of seizures induced by blood-brain barrier opening; and (3) the synapsin triple knockout (STKO) model of genetic epilepsy. The iEEG signals were filtered, segmented, and underwent feature extraction to be classified by ANN. For classifier training, a dataset of seizure and non-seizure recordings was comprised and represented by 22 extracted features. Forward selection analysis was applied for the identification of an optimal feature subset. A graphical user interface was created for the simple execution of data analysis and seizure detection. System performance was assessed by analyzing over 2800 h of iEEG recordings from 15 animals. The developed system achieved a sensitivity and positive predictive value of above 98%.

Since the development of this system in 2010, it has been used to study seizure frequency in multiple mouse and rat models of status epilepticus and post-traumatic epilepsy (compared to sham controls), as well as in young and old controls.

We conclude that the proposed approach is a reliable and efficient method for the automatic detection of seizures in mice and rats.

Epilepsy research often relies on rodent experiments involving EEG (electroencephalography) recordings. Today, most researchers still rely on manual inspection of these recordings in order to find and count seizures. This paper describes AI software for automated seizure detection in mice and rats. Over the past 15 years, this algorithm has been used in multiple studies of conditions that cause seizures.