Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals. Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% [email protected] while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future.

翻译：重症监护病房中的患者监测，尽管有生物传感器的辅助，仍需要医护人员的持续监督。为减轻医护人员负担，医院建立信息技术基础设施记录监测数据，并开发临床决策支持系统。然而，这些系统易受伪影干扰（例如治疗过程中肌肉运动产生的干扰），这些伪影往往与真实且可能危险的心电信号难以区分。利用视频记录，通过目标检测方法定位伪影来源，可促进生物信号的可靠分类。由于隐私限制，只能存储模糊化视频，这严重削弱了使用标准目标检测方法检测临床相关事件（如医疗干预或患者状态变化）的能力。因此，需要开发新型方法，既要充分利用模糊视频中因信息量减少而可用的各类信息，又要能便捷地在普通医院的信息技术基础设施中实施。本文提出了一种利用视频帧时间序列信息的新方法。为在给定硬件约束条件下高效集成现成目标检测器，我们重新设计图像颜色通道以表征时间一致性，从而提升了目标类别的检测率。该方法在自有数据集上较标准YOLOv5基线模型[email protected]提升+1.7%，同时训练速度提高十倍以上。初步实验表明该方法行之有效，并有望在未来推广至通用视频目标检测领域。