부산대학교 도서관

Video recording has been extensively studied for seizure detection and classification due to its convenience of collection. Most existing vision-based studies generally followed a two-stage scheme of first object detection and then action recognition to detect seizures for better real-world application. However, all of these approaches are two-stage not end-to-end, which may make the model locally optimal. Besides, the object detection algorithms applied in existing methods often suffer heavy computational burden, leading to slow inference speed and high hardware support. All these issues can seriously hinder the practical application and deployment of the model. Therefore, we proposed a novel end-to-end model in this article, which could simultaneously achieve patient detection and seizure detection. The amount of parameters and computations in the conventional object detection branch can be reduced by innovatively exploring the idea of using a spatial attention module instead of object detection networks for patient detection. However, based on a toy example, we found that relying solely on a spatial attention module without guidance is not reliable, despite its high performance in seizure detection. Therefore, a guided spatial attention module (GSAM) is proposed in this article. An extra regression loss function is used for guiding the learning of GSAM. In addition, the hard shrinkage operation is applied on the generated spatial attention heatmap (SAH), making the generated SAH closer to the real object detection with a faster model convergence. Besides, a temporal attention module is used to reduce the amount of parameters and computations, as well as to fuse the temporal information well. Experiments show that our method has less parameters and faster running speed than competing methods, yet better performance on seizure detection. The proposed GSAM with high performance could well replace the object detection algorithm for patient detection.