VSViG：通过基于骨架的时空 ViG 实现实时视频癫痫发作检测
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
AI总结：
提出基于骨架时空 Vision Graph 神经网络的 VSViG，结合概率累积决策规则，实现低延迟、低算力、零误检的实时视频癫痫发作检测。
摘要：
准确且高效的癫痫发作起始检测能够显著惠及患者。传统诊断方法主要依赖脑电图（EEG），往往导致设备笨重且不便携，使对患者的持续监测面临困难。基于视频的癫痫检测系统有望使患者摆脱头皮或植入式 EEG 设备的束缚，并支持在居住环境中进行远程监测。以往的基于视频的方法由于资源不足以及患者动作识别技术效果不佳，既无法实现全天监测，也不能提供较短的检测延迟。此外，基于骨架的动作识别方法在识别与癫痫发作相关的细微动作方面仍存在局限。为应对这些挑战，我们提出一种新颖的基于视频的癫痫检测模型，即通过基于骨架的时空 Vision Graph 神经网络（VSViG）实现，以满足实时场景中高效、准确和及时的目的。实验结果表明，在我们收集的患者视频数据上，VSViG 优于以往最先进的动作识别模型，具有更高准确率（5.9% 错误率）、更低 FLOPs（0.4G）和更小模型尺寸（1.4M）。此外，通过整合结合输出概率与累积函数的决策规则，我们实现了 EEG 起始后 5.1 秒的检测延迟、临床起始前 13.1 秒的检测提前量以及零误检率。项目主页位于：https://github.com/xuyankun/VSViG/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
