Estimation of confidence levels for physiology variables measured by a vital signs detection system

Quantifying the accuracy of physiological data measured by a Vital Signs Detection System (VSDS) plays a key role in making trustworthy decisions about the physiological status of a soldier. We developed an algorithm to report VSDS-measured heart and respiratory rates and their associated confidence levels. Heart and respiratory rates were measured about every 2 seconds for about 4 hours, while subjects engaged in low (e.g., sitting), medium (e.g., sit-ups), and high intensity (e.g., running) activities. The mean heart and median respiratory rates are calculated every 15 seconds by an in-house developed algorithm, and associated confidence levels for each variable are estimated simultaneously using a fuzzy-logic-based algorithm. Inputs into the algorithm are features that represent two types of information; the quality of each variable, and the relationship between the variables. Faulty data points are separated from good measures by setting a threshold. When data with pre-classified faults are tested with the confidence level threshold set at 0.5, the sensitivity and specificity of the algorithm for heart rate are 91% and 97%, respectively. For respiratory rate, because of the intrinsically noisy property of the data, the sensitivity and specificity are 87% and 93%, respectively. These preliminary results demonstrate that the fuzzy logic algorithm can accurately qualify heart and respiratory rates measured by a VSDS.