READS-V: Real-time Automated Detection of Epileptic Seizures from
  Surveillance Videos via Skeleton-based Spatiotemporal ViG
Yankun Xu Jie Yang Wenjie Ming Shuang Wang Mohamad Sawan
An accurate and efficient epileptic seizure onset detection system 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
skeleton-based spatiotemporal vision graph neural network (STViG) for
efficient, accurate, and timely REal-time Automated Detection of epileptic
Seizures from surveillance Videos (READS-V). Our experimental results indicate
STViG outperforms previous state-of-the-art action recognition models on our
collected patients' video data with higher accuracy (5.9% error) and lower
FLOPs (0.4G). Furthermore, by integrating a decision-making rule that combines
output probabilities and an accumulative function, our READS-V system achieves
a 5.1 s EEG onset detection latency, a 13.1 s advance in clinical onset
detection, and zero false detection rate.