Robust sensor fusion improves heart rate estimation : Clinical evaluation

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.