Visual detection of seizures in mice using supervised machine learning

作者：Gautam Sabnis, Leinani Hession, J. Matthew Mahoney, Arie S. Mobley, Marina dos Santos, Brian Q. Geuther, Vivek Kumar · 发表于：Cell Reports Methods · 年份：2025 · DOI：10.1016/j.crmeth.2025.101242 · 被引用次数：3 · 研究领域：EEG and Brain-Computer Interfaces、Zebrafish Biomedical Research Applications、Neuroscience and Neuropharmacology Research

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.