Feldman J M, Ebrahim M H, Bar-Kana I
Department of Anesthesiology, Allegheny University of the Health Sciences, Philadelphia, PA 19129, USA.
J Clin Monit. 1997 Nov;13(6):379-84. doi: 10.1023/a:1007476707284.
To determine if Robust Sensor Fusion (RSF), a method designed to fuse data from multiple sensors with redundant heart rate information can be used to improve the quality of heart rate data. To determine if the improved estimate of heart rate can reduce the number of false and missed heart rate alarms.
A total of 85 monitoring periods were investigated, 12 from the operating room, 60 from adult ICU and 13 from pediatric ICU. The operating room periods began with induction of anesthesia and ended at the completion of the anesthetic. For the ICU data, four hour blocks of time were studied. For each monitoring period, HR values were recorded at 5 second intervals or less from the ECG, SpO2 and IAC using a SpaceLabs Medical Gateway connected to a SpaceLabs Medical PC2. Fused estimates of HR were derived for every time point using RSF and all results accepted regardless of confidence value. Data were annotated manually to identify the "reference" HR (that HR value most likely to be correct) at all time points. All HR values from the sensors and the fused estimate that were different from the reference HR by more than +/- 5 beats/min were considered inaccurate. For each monitoring period, the total time per hour that data were either inaccurate or unavailable was calculated for each sensor as well as the fused estimates. The total time of false and missed HR alarms was found for all sensors and the fused estimate by comparing the data to thresholds for both high and low HR alarms at 150 bpm, 130 bpm, 110 bpm and 50 bpm, 40 bpm, 30 bpm respectively.
The fused estimate of HR was consistently as good or better than the estimate available from any individual sensor. The fused estimates also consistently reduced the incidence of false alarms compared with individual sensors without an unacceptable incidence of missed alarms.
Redundancy in sensor measurements can be used to improve HR estimation in the clinical setting. Methods like RSF which improve the quality of monitored data and reduce nuisance alarms will enhance the value of patient monitors to clinicians.
确定稳健传感器融合（RSF）方法，即一种旨在融合来自多个具有冗余心率信息的传感器数据的方法，是否可用于提高心率数据的质量。确定改进后的心率估计值是否能减少误报和漏报心率警报的数量。
共调查了85个监测时段，其中12个来自手术室，60个来自成人重症监护病房（ICU），13个来自儿科ICU。手术室时段从麻醉诱导开始，至麻醉结束。对于ICU数据，研究的是4小时时间段。对于每个监测时段，使用连接到SpaceLabs Medical PC2的SpaceLabs Medical网关，以5秒或更短的间隔记录来自心电图（ECG）、脉搏血氧饱和度（SpO2）和有创动脉血压（IAC）的心率（HR）值。使用RSF为每个时间点得出融合后的心率估计值，且无论置信度如何，所有结果均被接受。通过人工标注数据，以确定所有时间点的“参考”心率（即最可能正确的心率值）。将传感器的所有心率值以及与参考心率相差超过±5次/分钟的融合估计值视为不准确。对于每个监测时段，计算每个传感器以及融合估计值每小时数据不准确或不可用的总时间。通过将数据分别与150次/分钟、130次/分钟、110次/分钟以及50次/分钟、40次/分钟、30次/分钟的高、低心率警报阈值进行比较，得出所有传感器和融合估计值的误报和漏报心率警报的总时间。
融合后的心率估计值始终与任何单个传感器的估计值一样好或更好。与单个传感器相比，融合估计值还始终降低了误报发生率，且漏报发生率在可接受范围内。
传感器测量中的冗余可用于改善临床环境中的心率估计。像RSF这样能提高监测数据质量并减少干扰警报的方法，将提升患者监护仪对临床医生的价值。
