Dosso Yasmina Souley, Aziz Samreen, Nizami Shermeen, Greenwood Kim, Harrold JoAnn, Green James R
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6135-6138. doi: 10.1109/EMBC44109.2020.9175354.
Newborns admitted to the neonatal intensive care unit (NICU) require a high level of care due to their precarious condition. Nurses typically monitor their vital signs continuously using wearable sensors such as electrocardiogram (ECG) electrodes placed on their chest and a pulse oximeter on a limb. When the patient moves, this can cause motion artifacts on one or more physiologic signals, potentially resulting in a false alarm on the patient monitor. We therefore propose a motion detection algorithm to mitigate these alarms by providing context. Using a camera positioned above the crib or overhead warming bed, we recorded videos from six patients and annotated all patient movements. These data were used to train and evaluate two different approaches for non-contact motion detection. Results were stronger for the optical flow technique than for the long short-term memory network approach. This represents a challenging problem due to variable lighting, patient clothing and bed coverings, and the complex clinical environment in the NICU.
因病情不稳定，入住新生儿重症监护病房（NICU）的新生儿需要高水平护理。护士通常使用可穿戴传感器持续监测他们的生命体征，比如放置在其胸部的心电图（ECG）电极以及肢体上的脉搏血氧仪。当患者移动时，这可能会在一个或多个生理信号上产生运动伪影，从而可能导致患者监护仪发出误报。因此，我们提出一种运动检测算法，通过提供相关背景信息来减少此类警报。我们使用放置在婴儿床或头顶暖床上方的摄像头，记录了六名患者的视频，并标注了所有患者的动作。这些数据被用于训练和评估两种不同的非接触式运动检测方法。光流技术的检测结果比长短期记忆网络方法的结果更好。由于光照变化、患者衣物和床罩，以及NICU复杂的临床环境，这是一个具有挑战性的问题。
