  An accurate and efficient epileptic seizure onset detection can significantly
benefit patients. Traditional diagnostic methods, primarily relying on
electroencephalograms (EEGs), often result in cumbersome and non-portable
solutions, making continuous patient monitoring challenging. The video-based
seizure detection system is expected to free patients from the constraints of
scalp or implanted EEG devices and enable remote monitoring in residential
settings. Previous video-based methods neither enable all-day monitoring nor
provide short detection latency due to insufficient resources and ineffective
patient action recognition techniques. Additionally, skeleton-based action
recognition approaches remain limitations in identifying subtle seizure-related
actions. To address these challenges, we propose a novel Video-based Seizure
detection model via a skeleton-based spatiotemporal Vision Graph neural network
(VSViG) for its efficient, accurate and timely purpose in real-time scenarios.
Our experimental results indicate VSViG outperforms previous state-of-the-art
action recognition models on our collected patients' video data with higher
accuracy (5.9% error), lower FLOPs (0.4G), and smaller model size (1.4M).
Furthermore, by integrating a decision-making rule that combines output
probabilities and an accumulative function, we achieve a 5.1 s detection
latency after EEG onset, a 13.1 s detection advance before clinical onset, and
a zero false detection rate. The project homepage is available at:
https://github.com/xuyankun/VSViG/
