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Lightweight physiologic sensor performance during pre-hospital care delivered by ambulance clinicians

机译:救护车临床医生在院前护理过程中的轻型生理传感器性能

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摘要

The aim of this study was to explore the impact of motion generated by ambulance patient management on the performance of two lightweight physiologic sensors. Two physiologic sensors were applied to pre-hospital patients. The first was the Contec Medical Systems CMS50FW finger pulse oximeter, monitoring heart rate (HR) and blood oxygen saturation (SpO2). The second was the RESpeck respiratory rate (RR) sensor, which was wireless-enabled with a Bluetooth® Low Energy protocol. Sensor data were recorded from 16 pre-hospital patients, who were monitored for 21.2 ± 9.8 min, on average. Some form of error was identified on almost every HR and SpO2 trace. However, the mean proportion of each trace exhibiting error was <10 % (range <1–50 % for individual patients). There appeared to be no overt impact of the gross motion associated with road ambulance transit on the incidence of HR or SpO2 error. The RESpeck RR sensor delivered an average of 4.2 (±2.2) validated breaths per minute, but did not produce any validated breaths during the gross motion of ambulance transit as its pre-defined motion threshold was exceeded. However, this was many more data points than could be achieved using traditional manual assessment of RR. Error was identified on a majority of pre-hospital physiologic signals, which emphasised the need to ensure consistent sensor attachment in this unstable and unpredictable environment, and in developing intelligent methods of screening out such error.
机译:这项研究的目的是探讨救护车患者管理产生的运动对两个轻型生理传感器性能的影响。两个生理传感器应用于住院前患者。首先是Contec Medical Systems CMS50FW手指脉搏血氧仪,用于监测心率(HR)和血氧饱和度(SpO2)。第二个是RESpeck呼吸频率(RR)传感器,该传感器通过Bluetooth ®低能耗协议无线启用。记录了16位院前患者的传感器数据,平均监测时间为21.2±9.8分钟。几乎在所有HR和SpO2迹线上都发现了某种形式的错误。但是,每条迹线显示错误的平均比例为<10%(个别患者的范围​​为<1-5%)。公路救护车过境相关的总体运动似乎对HR或SpO2错误的发生率没有明显影响。 RESpeck RR传感器平均每分钟提供4.2(±2.2)次经过验证的呼吸,但由于超过了其预先定义的运动阈值,因此在救护车的总体运动过程中未产生任何经过验证的呼吸。但是,这比使用传统的RR手动评估所能获得的数据点要多得多。在大多数院前生理信号中都发现了错误,这强调了在这种不稳定且不可预测的环境中,需要确保传感器连接始终如一的必要性,并需要开发出智能方法来筛选出此类错误。

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