论文标题
对重症患者的休息中断的自动检测
Automated Detection of Rest Disruptions in Critically Ill Patients
论文作者
论文摘要
睡眠已被证明是患者康复过程中必不可少且重要的组成部分。但是，由于噪声，疼痛和频繁的护理活动等因素，重症监护病房（ICU）患者的睡眠质量通常很低。医务人员和/或访客在某些时候频繁的睡眠中断可能会导致患者睡眠效果周期中断，也可能影响疼痛的严重程度。由于缺乏访问检测方法，检查睡眠质量与频繁探访之间的关联非常困难。在这项研究中，我们招募了38名患者，以自动评估捕获的视频帧的访问频率。我们使用密集的R-CNN（RESNET-101）模型来计算视频框架中房间中的人数。我们检查了何时中断患者，并检查了频繁的干扰与疼痛和住院时间的患者结局之间的关联。
Sleep has been shown to be an indispensable and important component of patients recovery process. Nonetheless, sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care activities. Frequent sleep disruptions by the medical staff and/or visitors at certain times might lead to disruption of patient sleep-wake cycle and can also impact the severity of pain. Examining the association between sleep quality and frequent visitation has been difficult, due to lack of automated methods for visitation detection. In this study, we recruited 38 patients to automatically assess visitation frequency from captured video frames. We used the DensePose R-CNN (ResNet-101) model to calculate the number of people in the room in a video frame. We examined when patients are interrupted the most, and we examined the association between frequent disruptions and patient outcomes on pain and length of stay.
