Calibration of glucose monitoring sensor and/or insulin delivery system

Abstract

Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, a blood-glucose concentration in a patient may be determined based, at least in part, on one or more probability models, one or more functions for estimating blood-glucose concentrations, and/or blood glucose reference sample-sensor measurement pairs.

What is claimed is: 
     
         1 . An apparatus comprising:
 a filter unit to receive one or more signals based on blood glucose sensor measurements, the filter unit comprising one or more processors to:   determine one or more composite sensor glucose concentration values for a patient based, at least in part, on a combination of a plurality of estimated sensor glucose concentration values;   wherein the plurality of estimated sensor glucose concentration values are based, at least in part, on a plurality of functions for estimating a blood glucose concentration in the patient from sensor measurements based, at least in part, on a plurality of probability models; and   wherein the plurality of estimated sensor glucose concentration values are also based, at least in part, on a plurality of blood glucose reference sample-sensor measurement pairs.   
     
     
         2 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 take sensor measurements for the plurality of blood glucose reference sample-sensor measurement pairs via at least one subcutaneous current sensor.   
     
     
         3 . The apparatus of  claim 1 , wherein at least one of the plurality of functions is to be defined at least partly by a slope and an offset. 
     
     
         4 . The apparatus of  claim 1 , wherein at least one of the plurality of functions is to be defined, at least in part, by a calfactor variable and an offset variable. 
     
     
         5 . The apparatus of  claim 4 , wherein a relationship between current sensor measurements corresponding to the plurality of blood glucose reference sample-sensor measurement pairs and the plurality of estimated sensor glucose concentration values is to be represented by a linear model, the linear model to be associated with a slope and an offset; and wherein the slope and the offset of the linear model are determinable from the calfactor variable, the offset variable, or a combination thereof. 
     
     
         6 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 estimate one or more parameters of the plurality of probability models based at least in part on a linear Kalman filter estimator to model process noise and measurement noise as being constant.   
     
     
         7 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 estimate one or more parameters of the plurality of probability models based at least in part on a linear Kalman filter estimator to adapt process noise as a function of model performance.   
     
     
         8 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 estimate one or more parameters of the plurality of probability models based at least in part on a linear Kalman filter estimator to adapt measurement noise as a function of model performance.   
     
     
         9 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 estimate one or more parameters of the plurality of probability models based at least in part on a Bayesian estimator in which sensor measurements corresponding to the blood glucose reference sample-sensor measurement pairs comprise an independent variable and blood glucose reference samples corresponding to the blood glucose reference sample-sensor measurement pairs comprise a measured variable.   
     
     
         10 . The apparatus of  claim 1 , wherein the one or more processors are further to:
 weight the plurality of sensor glucose concentration values based, at least in part, on a plurality of quality indicators indicative of an accuracy of the plurality of probability models.   
     
     
         11 . The apparatus of  claim 10 , wherein the plurality of quality indicators are to comprise a plurality of likelihood values; and wherein the one or more processors are further to:
 calculate the plurality of likelihood values based, at least partly, on error differences between one or more of the plurality of estimated sensor glucose concentration values and one or more reference blood glucose values from blood glucose reference samples.   
     
     
         12 . The apparatus of  claim 1 , further comprising:
 one or more blood-glucose sensors adapted to be coupled to the patient to obtain blood-glucose sensor measurements and adapted to provide one or more signals based on the blood-glucose sensor measurements, and   further comprising instructions executable by the one or more processors to: obtain the plurality of blood glucose reference sample-sensor measurement pairs via the one or more blood-glucose sensors based at least in part on the one or more signals.   
     
     
         13 . A system comprising:
 means for determining one or more composite sensor glucose concentration values for a patient based, at least in part, on a combination of a plurality of estimated sensor glucose concentration values;   wherein the plurality of estimated sensor glucose concentration values are based, at least in part, on a plurality of functions for estimating a blood glucose concentration in the patient from sensor measurements based, at least in part, on a plurality of probability models; and   wherein the plurality of estimated sensor glucose concentration values are also based, at least in part, on a plurality of blood glucose reference sample-sensor measurement pairs.   
     
     
         14 . The system of  claim 13 , comprising:
 means for taking sensor measurements for the plurality of blood glucose reference sample-sensor measurement pairs via at least one subcutaneous current sensor.   
     
     
         15 . The system of  claim 13 , further comprising means for defining at least one of the plurality of functions based, at least partly, by a slope and an offset. 
     
     
         16 . The system of  claim 13 , further comprising means for defining at least one of the plurality of functions based, at least in part, on a calfactor variable and an offset variable. 
     
     
         17 . The system of  claim 13 , further comprising:
 means for weighting the plurality of sensor glucose concentration values based, at least in part, on a plurality of quality indicators indicative of an accuracy of the plurality of probability models.   
     
     
         18 . The system of  claim 17 , wherein the plurality of quality indicators comprise a plurality of likelihood values; and further comprising:
 means for calculating the plurality of likelihood values based, at least partly, on error differences between one or more of the plurality of estimated sensor glucose concentration values and one or more reference blood glucose values from blood glucose reference samples.   
     
     
         19 . The system of  claim 13 , further comprising:
 means for subcutaneous sensing of blood glucose in a patient being adapted to be coupled to the patient to obtain blood-glucose sensor measurements and being adapted to provide one or more signals based on the blood-glucose sensor measurements, and   means for obtaining the plurality of blood glucose reference sample-sensor measurement pairs via the means for subcutaneous sensing of blood glucose based at least in part on the one or more signals.   
     
     
         20 . An article comprising:
 at least one non-transitory storage medium having stored thereon instructions executable by one or more processors to:   determine one or more composite sensor glucose concentration values for a patient based, at least in part, on a combination of a plurality of estimated sensor glucose concentration values;   wherein the plurality of estimated sensor glucose concentration values are based, at least in part, on a plurality of functions for estimating a blood glucose concentration in the patient from sensor measurements based, at least in part, on a plurality of probability models; and   wherein the plurality of estimated sensor glucose concentration values are also based, at least in part, on a plurality of blood glucose reference sample-sensor measurement pairs.