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An accurate and efficient epileptic seizure onset detection system cansignificantly benefit patients. Traditional diagnostic methods, primarilyrelying on electroencephalograms (EEGs), often result in cumbersome andnon-portable solutions, making continuous patient monitoring challenging. Thevideo-based seizure detection system is expected to free patients from theconstraints of scalp or implanted EEG devices and enable remote monitoring inresidential settings. Previous video-based methods neither enable all-daymonitoring nor provide short detection latency due to insufficient resourcesand ineffective patient action recognition techniques. Additionally,skeleton-based action recognition approaches remain limitations in identifyingsubtle seizure-related actions. To address these challenges, we propose a novelskeleton-based spatiotemporal vision graph neural network (STViG) forefficient, accurate, and timely REal-time Automated Detection of epilepticSeizures from surveillance Videos (READS-V). Our experimental results indicateSTViG outperforms previous state-of-the-art action recognition models on ourcollected patients' video data with higher accuracy (5.9% error) and lowerFLOPs (0.4G). Furthermore, by integrating a decision-making rule that combinesoutput probabilities and an accumulative function, our READS-V system achievesa 5.1 s EEG onset detection latency, a 13.1 s advance in clinical onsetdetection, and zero false detection rate.

READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG