Ebrahim M H, Feldman J M, Bar-Kana I
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.
J Clin Monit. 1997 Nov;13(6):385-93. doi: 10.1023/a:1007438224122.
Physiologic data measured in the clinical environment is frequently corrupted causing erroneous data to be displayed, periods of missing information or nuisance alarms to be triggered. To date, the possibility of combining sensors with similar information to improve the quality of the extracted data has not been developed. The objective of this work is to develop a method for combining heart rate measurements from multiple sensors to obtain: (i) an estimate of heart rate that is free of artifact; (ii) a confidence value associated with every heart rate estimate which indicates the likelihood that an estimate is correct; (iii) a more accurate estimate of heart rate than is available from any individual sensor.
The essence of the method is to discriminate between good and bad sensor measurements and combine only the good readings to derive an optimal heart rate estimate. Past estimates of heart rate are used to derive a predicted value for the current heart rate that is also fused along with the sensor measurements. Consensus between sensor measurements, the predicted value and physiologic credibility of the readings are used to distinguish between good and bad readings. Three sensor measurements and the predicted value are evaluated yielding 16 possible hypotheses for the current state of the available data. A Kalman filter uses the most likely hypothesis to derive the fused estimate. Statistical measures of the sensor error and rate of change of heart rate are adaptively estimated when data are sufficiently reliable and used to enhance the hypothesis selection process.
The method of sensor fusion presented has been documented to perform well using clinical data. Limitations of the technique and the assumptions employed are discussed as well as directions for future research.
在临床环境中测量的生理数据经常受到干扰，导致显示错误数据、出现信息缺失期或触发不必要的警报。迄今为止，尚未开发出将具有相似信息的传感器组合起来以提高提取数据质量的方法。这项工作的目标是开发一种将来自多个传感器的心率测量值进行组合的方法，以获得：（i）无伪影的心率估计值；（ii）与每个心率估计值相关的置信度值，该值表明估计正确的可能性；（iii）比任何单个传感器提供的心率估计值更准确的估计值。
该方法的本质是区分传感器测量的好坏，并仅组合良好读数以得出最佳心率估计值。过去的心率估计值用于得出当前心率的预测值，该预测值也与传感器测量值融合在一起。传感器测量值、预测值和读数的生理可信度之间的一致性用于区分好坏读数。对三个传感器测量值和预测值进行评估，得出关于可用数据当前状态的16种可能假设。卡尔曼滤波器使用最可能的假设来得出融合估计值。当数据足够可靠时，自适应估计传感器误差和心率变化率的统计量，并将其用于改进假设选择过程。
所提出的传感器融合方法已被证明在使用临床数据时表现良好。讨论了该技术的局限性和所采用的假设以及未来研究的方向。
