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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.

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.