Document Type
Article
Publication Date
12-15-2025
Original Citation
Sabnis G,
Hession L,
Mahoney J,
Mobley A,
Santos M,
Geuther B,
Kumar V.
Visual detection of seizures in mice using supervised machine learning. Cell Rep Methods. 2025;5(12):101242.
Keywords
JMG, Animals, Seizures, Supervised Machine Learning, Mice, Pentylenetetrazole, Disease Models, Animal, Male, Video Recording, Machine Learning, Mice, Inbred C57BL
JAX Source
Cell Rep Methods. 2025;5(12):101242.
ISSN
2667-2375
PMID
41308647
DOI
https://doi.org/10.1016/j.crmeth.2025.101242
Grant
This work was funded by The Jackson Laboratory Directors Innovation Fund, National Institutes of Health, National Institute on Drug Abuse DA051235 and DA048634 (V.K.), and National Institute on Aging AG078530 (V.K.).
Abstract
Seizures are caused by abnormal synchronous brain activity. The resulting changes in muscle tone, such as twitching, stiffness, or jerking, are used in visual scoring systems such as the Racine scale to quantify seizure intensity. However, visual inspection is time consuming, low throughput, and partially subjective, and there is a need for scalable and rigorous quantitative approaches. We used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from non-invasive video data. Using the pentylenetetrazole (PTZ)-induced seizure model in mice, we trained video-only classifiers to predict ictal events and combined these events to predict composite seizure intensity for a recording session, as well as time-localized seizure intensity scores. Our results show that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, non-invasive, and standardized seizure scoring for neurogenetics and therapeutic discovery.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.