Advanced calibration for analyte sensors

Abstract

Systems and methods for processing sensor data and calibration of the sensors are provided. In some embodiments, the method for calibrating at least one sensor data point from an analyte sensor comprises receiving a priori calibration distribution information; receiving one or more real-time inputs that may influence calibration of the analyte sensor; forming a posteriori calibration distribution information based on the one or more real-time inputs; and converting, in real-time, at least one sensor data point calibrated sensor data based on the a posteriori calibration distribution information.

What is claimed is: 
     
         1 . A method for improving glucose sensing using a glucose sensor, the method comprising:
 receiving a priori calibration distribution information obtained prior to a sensor session, the a priori calibration distribution information including probability information about at least a distribution of sensitivities;   receiving glucose sensor data from the glucose sensor during a sensing session for the glucose sensor, the glucose sensor data including at least one sensor data point; and   forming a posteriori calibration distribution information using a Bayesian approach and based on the a priori calibration distribution information and further based on one or more real-time inputs, wherein the one or more real-time inputs include the received sensor data;   converting, in real-time during the sensor session, the at least one sensor data point from the glucose sensor to calibrated sensor data based on the a posteriori calibration distribution information; and   displaying an indication of the converted sensor data for the glucose sensor.   
     
     
         2 . The method of  claim 1 , wherein the a priori calibration distribution information comprises information from previous calibrations of a particular sensor session and/or information obtained prior to sensor insertion. 
     
     
         3 . The method of  claim 1 , wherein the a priori calibration distribution information further comprises probability distributions for sensitivity (m), sensitivity-related information, baseline (b), or baseline-related information. 
     
     
         4 . The method of  claim 1 , wherein the a priori calibration distribution information comprises a priori guidance or validation ranges. 
     
     
         5 . The method of  claim 1 , wherein the one or more real-time inputs comprise data received or determined since a previous calibration process. 
     
     
         6 . The method of  claim 5 , wherein the one or more real-time inputs comprises at least one of: internally-derived real-time data, externally-derived real-time data, and combinations of internally- and externally-derived real-time data. 
     
     
         7 . The method of  claim 1 , wherein the one or more real-time inputs include at least one type of information selected from the group consisting of: stimulus signal output of sensor;
 sensor data measured by the sensor indicative of a glucose concentration; sensor data indicative of glucose rate-of-change; temperature measurements; sensor data from multi-electrode sensors; sensor data generated by redundant sensors; sensor data generated by one or more auxiliary sensors; data representative of a pressure on sensor; data generated by an accelerometer; sensor diagnostic information; impedance; and certainty level.   
     
     
         8 . The method of  claim 1 , wherein the one or more real-time inputs includes at least one type of information selected from the group consisting of: glucose concentration information obtained from a reference monitor; information related to meal; insulin dosing time and amounts; insulin estimates; exercise; sleep; illness; stress; hydration; and hormonal conditions. 
     
     
         9 . The method of  claim 1 , wherein the one or more real-time inputs includes combinations of internally- and externally-derived real-time data including at least one type of information selected from the group consisting of: information gathered from population based data; glucose concentration of the host; error at calibration or error in matched data pair; site of sensor implantation specific relationships; time since sensor manufacture; exposure of sensor, while on shelf, to temperature, humidity, or external factors; a measure of noise in a glucose concentration signal; and a level of certainty. 
     
     
         10 . The method of  claim 1 , further comprising determining a level of certainty associated with calibration information and/or calibrated sensor data. 
     
     
         11 . The method of  claim 1 , wherein forming a posteriori calibration distribution information comprises at least one of: an adjustment of the a priori calibration distribution information or a creation of a new range or distribution information based on the one or more real-time inputs. 
     
     
         12 . The method of  claim 11 , wherein an adjustment of the a priori calibration distribution information comprises shifting, tightening, or loosening the a priori calibration distribution. 
     
     
         13 . The method of  claim 1 , wherein the calibration distribution information is selected from the group consisting of: sensitivity; change in sensitivity; rate of change of sensitivity; baseline; change in baseline, rate of change of baseline, baseline profile associated with the sensor; sensitivity profile associated with the sensor; linearity; response time; relationships between properties of the sensor; relationships between particular stimulus signal output; and patient specific relationships between sensor and sensitivity, baseline, drift, impedance, impedance/temperature relationship, site of sensor implantation. 
     
     
         14 . The method of  claim 1 , further comprising providing output of calibrated sensor data. 
     
     
         15 . The method of  claim 1 , wherein the method is implemented using a processor and a memory coupled to the processor. 
     
     
         16 . A system for improving blood glucose sensing using a continuous glucose sensor, the system comprising sensor electronics configured to be operably connected to the continuous glucose sensor, the sensor electronics configured to:
 receive a priori calibration distribution information obtained prior to a sensor session, the a priori calibration distribution information including probability information about at least a distribution of sensitivities;   receive glucose sensor data from the glucose sensor during a sensing session for the glucose sensor, the glucose sensor data including at least one sensor data point; and   form a posteriori calibration distribution information using a Bayesian approach and based on the a priori calibration distribution information and further based on one or more real-time inputs, wherein the one or more real-time inputs include a value based on the received sensor data;   convert, in real-time during the sensor session, the at least one sensor data point from the glucose sensor to calibrated sensor data based at least in part on the a posteriori calibration distribution information; and   transmit an indication of the converted sensor data for the glucose sensor to a receiver or smart phone.   
     
     
         17 . The system of  claim 16 , wherein the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to form the a posteriori calibration distribution information. 
     
     
         18 . A system for improving glucose sensing using a glucose sensor, the system comprising:
 means for receiving a priori calibration distribution information obtained prior to a sensor session, the a priori calibration distribution information including probability information about combinations of sensitivity and baseline;   means for receiving glucose sensor data from the glucose sensor during the sensing session for the glucose sensor, the glucose sensor data including at least one sensor data point; and   means for calibrating, during the sensor session, wherein the means for calibrating includes:
 means for receiving a priori calibration distribution information obtained prior to a sensor session, the a priori calibration distribution information including probability information about at least a distribution of sensitivities; and 
 means for forming a posteriori calibration distribution information using a Bayesian approach and based on the a priori calibration distribution information and further based on one or more real-time inputs, wherein the one or more real-time inputs include the received glucose sensor data; 
   means for converting, in real-time during the sensor session, the at least one sensor data point from the glucose sensor to calibrated sensor data based on the a posteriori calibration distribution information; and   means for transmitting or displaying an indication of the converted sensor data for the glucose sensor.   
     
     
         19 . The system of  claim 18 , wherein the instructions, when executed by the processor module, cause the sensor electronics to adjust the a priori calibration distribution information to form the a posteriori calibration distribution information. 
     
     
         20 . The system of  claim 18 , wherein the instructions, when executed by the processor module, cause the sensor electronics to form the a posteriori calibration distribution information by creating a new range or distribution information based on the one or more real-time inputs.