초록

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.

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1. A system for monitoring glucose concentration in a host, the system comprising: a continuous glucose sensor configured to produce a signal indicative of a real-time glucose concentration in a host; andan electronic system comprising an alarm, wherein the electronic system is operably connected to

1. A system for monitoring glucose concentration in a host, the system comprising: a continuous glucose sensor configured to produce a signal indicative of a real-time glucose concentration in a host; andan electronic system comprising an alarm, wherein the electronic system is operably connected to the sensor, wherein the electronic system further comprises a processor configured to estimate a glucose concentration of the host for a future time by selectively applying one or more algorithms from a plurality of estimation algorithms, and wherein the processor is further configured to trigger the alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold. 2. The system of claim 1, wherein the alarm is at least one of a visual alarm, an audible alarm, and a tactile alarm. 3. The system of claim 1, wherein the predetermined threshold is user configurable. 4. The system of claim 1, wherein the future time is at least about 5 minutes in the future. 5. The system of claim 4, wherein the future time is at least about 10 minutes in the future. 6. The system of claim 5, wherein the future time is at least about 15 minutes in the future. 7. The system of claim 6, wherein the future time is at least about 20 minutes in the future. 8. The system of claim 1, wherein the processor is further configured to calibrate the signal using a conversion function, and wherein one of the plurality of estimation algorithms is based on the conversion function to extrapolate glucose data for the future time. 9. The system of claim 8, wherein the conversion function is calculated from a linear regression. 10. The system of claim 8, wherein the conversion function is calculated from a non-linear regression. 11. The system of claim 8, wherein the conversion function is calculated from reference data obtained from a single point glucose measuring device. 12. The system of claim 11, wherein the single point glucose measuring device is built into the electronic system. 13. The system of claim 1, wherein the processor is further configured to filter the signal, and wherein the estimated glucose data is calculated from the filtered signal. 14. The system of claim 1, wherein the processor is further configured to apply at least one boundary to the estimated glucose data for the future time. 15. The system of claim 14, wherein the boundary is a physiological boundary. 16. The system of claim 1, wherein the electronic system further comprises a user interface, wherein the processor is further configured to calibrate the signal, and wherein the processor is further configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface. 17. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data. 18. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data. 19. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms. 20. The system of claim 1, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data. 21. The system of claim 1, wherein the plurality of estimation algorithms programmed into the electronic system is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting. 22. The system of claim 1, wherein the processor is further configured to generate real-time or historical estimated glucose data based on the signal and wherein the processor is further configured to estimate a glucose concentration of a host for a future time is configured to use the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period. 23. A device comprising a processor and a computer readable memory, the computer readable memory comprising code configured to instruct the processor to process data from a continuous glucose measuring device, wherein the code comprises: instructions configured to process a signal received from a continuous glucose measuring device, the signal comprising data indicative of a measured glucose concentration of a host;instructions configured to estimate a future glucose concentration of the host by selectively applying one or more algorithms from a plurality of estimation algorithms; andinstructions configured to trigger an alarm when the estimated glucose data for the future time is above or below at least one predetermined threshold. 24. The device of claim 23, further comprising instructions configured to allow a user to modify the predetermined threshold. 25. The device of claim 23, wherein the future time is at least about 5 minutes in the future. 26. The device of claim 25, wherein the future time is at least about 15 minutes in the future. 27. The device of claim 23, further comprising instructions configured to calibrate the signal using a conversion function, and wherein one of the plurality of estimation algorithms the device is based on the conversion function. 28. The device of claim 27, wherein the conversion function is calculated from a linear regression. 29. The device of claim 27, wherein the conversion function is calculated from a non-linear regression. 30. The device of claim 27, wherein the conversion function is calculated from reference data obtained from a single point glucose measuring device. 31. The device of claim 30, wherein the single point glucose measuring device is integral with the device. 32. The device of claim 23, further comprising instructions configured to filter the signal, and wherein the instructions configured to estimate glucose data estimate glucose data from the filtered signal. 33. The device of claim 23, further comprising instructions configured to apply at least one boundary to the estimated glucose data for the future time. 34. The device of claim 33, wherein the boundary is a physiological boundary. 35. The device of claim 23, further comprising instructions configured to calibrate the signal and instructions configured to display a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on a user interface. 36. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data. 37. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data. 38. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms. 39. The device of claim 23, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data. 40. The device of claim 23, wherein the plurality of estimation algorithms programmed into the electronic device is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting. 41. The device of claim 23, wherein the code further comprises instructions configured to generate real-time or historical estimated glucose data based on the signal and wherein the instructions configured to estimate a glucose concentration of a host for a future time is configured to use the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period. 42. A method for monitoring a glucose concentration in a host, the method comprising: generating a signal from a continuous glucose measuring device indicative of a glucose concentration in a host;processing, using electronic circuitry, the signal to estimate a glucose concentration of the host for a future time by selectively applying one or more algorithms from a plurality of estimation algorithms; andalarming the host when the estimated glucose data for the future time is above or below at least one predetermined threshold. 43. The method of claim 42, wherein alarming comprises providing at least one of a visual signal, an audible signal, and a tactile signal. 44. The method of claim 42, further comprising calibrating the signal using a conversion function, wherein one of the plurality of estimation algorithms programmed into the electronic circuitry is based on the conversion function. 45. The method of claim 42, further comprising filtering the signal, wherein processing the signal to estimate glucose data estimates the glucose data from the filtered signal. 46. The method of claim 42, further comprising applying a boundary to the estimated glucose data for the future time. 47. The method of claim 42, further comprising calibrating the signal and displaying a graphical representation of the calibrated signal and a directional arrow indicative of a direction and a rate of change of the calibrated signal on the user interface. 48. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of a rate of change of the glucose data. 49. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an acceleration of the glucose data. 50. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to an evaluation of the estimated glucose data derived from the plurality predetermined estimation algorithms. 51. The method of claim 42, wherein the selective application of the one or more estimation algorithms is based on or responsive to a clinical risk associated with the estimated glucose data. 52. The method of claim 42, wherein the plurality of estimation algorithms programmed into the electronic circuitry is selected from the group of algorithms consisting of first order polynomial regression, second order polynomial regression, third order polynomial regression, an autoregressive algorithm, neural network-based mapping, fuzzy logic based pattern matching, genetic-algorithms based pattern matching, and time-series forecasting. 53. The method of claim 42, further comprising using electronic circuitry to generate real-time or historical estimated glucose data based on the signal and wherein the processing includes using the real-time or historical estimated glucose data in the applied one or more algorithms to estimate the glucose data for the future time period.