Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning
Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning
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DOI：
10.1038/s41598-019-44004-w
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发表时间：
2019-05-29
影响因子：
4.6
通讯作者：
Rashidi, Parisa
中科院分区：
文献类型：
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作者：
Davoudi, Anis;Malhotra, Kumar Rohit;Rashidi, Parisa
目前，许多重症监护指标没有自动捕获的粒度级别，而是重复评估负担过重的护士。在这项试点研究中，我们研究了在重症监护室（ICU）中使用普适传感技术和人工智能进行自主和粒度监测的可行性。作为一个典型的普遍条件，我们的特点是谵妄患者和他们的环境。我们使用可穿戴传感器、光和声音传感器以及摄像头来收集患者及其环境的数据。我们分析收集到的数据，以检测和识别病人的脸，他们的姿势，面部动作单位和表情，头部姿势的变化，肢体运动，声压级，光强度水平，和探视频率。我们发现，面部表情，功能状态，需要肢体运动和姿势，和环境因素，包括访问频率，光和声压水平在夜间之间的谵妄和非谵妄患者有显着差异。我们的研究结果表明，使用非侵入性系统对重症患者及其环境进行颗粒和自主监测是可行的，我们证明了其表征重症监护患者和环境因素的潜力。
Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient's face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.