Comprehensive Summary
In this paper, Artificial Intelligence (AI) was used to identify different behavioral features during epileptic seizures. Using the mouse model of Angelman syndrome, 32 mice were exposed to flurothyl to induce seizures once daily for 8 consecutive days. The AI model was trained on three different data sets and then applied to the test group of 32 mice. Videos of seizures were recorded at 480p and 30fps. DeepLabCut (DLC) and Behavioral Segmentation of Open Field in DLC (B-SOiD) were used to identify 63 interpretable behavior groups. The behavioral groups can be used to identify seizure states, progression, and mortality. The system was shown top separate GS from preictal behavior. Future research should explore translation potential to other model organisms as well as epilepsy patients.
Outcomes and Implications
Analysis of motor and behavior characters of seizures can help identify seizure types and epileptic zones. Video monitoring is already used to monitor epilepsy and improved video analysis of motor control during seizures is useful for diagnosis and for preclinical studies in mice.