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.

我们提出了一种基于骨骼的时空视觉图神经网络 (STViG)，用于快速、准确、及时从监控视频中自动检测癫痫发作 (READS-V)。通过与以往动作识别模型的实验结果对比，STViG 在我们收集的患者视频数据上具有更高的准确性 (误差为 5.9%) 和较低的 FLOPs (0.4G)。此外，通过结合输出概率和累积函数的决策规则，我们的 READS-V 系统实现了 5.1 秒的 EEG 起始检测延迟，比临床起始检测提前 13.1 秒，并且无误报率。