VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
It addresses the problem of cumbersome, non-portable EEG monitoring for epilepsy patients by enabling efficient, accurate remote video detection, though it appears incremental as it builds on existing skeleton-based action recognition methods.
The paper tackles real-time video-based seizure detection by proposing VSViG, a skeleton-based spatiotemporal Vision Graph neural network, which achieves higher accuracy (5.9% error), lower FLOPs (0.4G), smaller model size (1.4M), 5.1 s detection latency after EEG onset, 13.1 s advance before clinical onset, and zero false detection rate.
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/