Micropressors, devices and methods for use in analyte monitoring systems
The present invention comprises one or more microprocessors programmed to execute methods for improving the performance of an analyte monitoring device including prediction of glucose levels in a subject by utilizing a predicted slower-time constant (1/k 2 ). In another aspect of the invention, pre-exponential terms (1/c 2 ) can be used to provide a correction for signal decay (e.g., a Gain Factor). In other aspects, the present invention relates to one or more microprocessors comprising programming to control execution of (i) methods for conditional screening of data points to reduce skipped measurements, (ii) methods for qualifying interpolated/extrapolated analyte measurement values, (iii) various integration methods to obtain maximum integrals of analyte-related signals, as well as analyte monitoring devices comprising such microprocessors. Further, the present invention relates to algorithms for improved optimization of parameters for use in prediction models that require optimization of adjustable parameters.
1. A method of obtaining an analyte value in biological fluid of a user with an analyte sensor having two electrode sets coupled to a microprocessor, the method comprising:
contacting the two electrode sets with biological fluid of a user, in which each electrode set includes an iontophoretic electrode and an electrochemical sensor element in contact with the biological fluid and each of the two iontophoretic electrodes function as either a cathode or anode while in contact with the fluid;
measuring a total signal from the sensor element associated with the iontophoretic electrodes;
extracting a signal from the total signal related to the analyte from the sensor element associated with the iontophoretic electrodes;
accepting said measurement value when at least a first conditional data integrity screening indicates a skin conductance reading falls within predetermined acceptable ranges or within predetermined threshold values and at least a second conditional data integrity screening indicates that the signals have an acceptable monotonic trend or the signals comprise one or more non-monotonic events as a percentage of the total signals being less than a threshold percentage;
integrating, via the microprocessor, the signal related to the analyte over time to obtain a charge signal as being proportional to a value of the analyte, the charge signal being representative of a measurement value of glucose in the biological fluid of the user;
displaying the measurement of the biological fluid of the user for diagnostic purposes; or
skipping said measurement value when two or more of said two or more conditional data integrity screenings falls outside of predetermined acceptable ranges or beyond predetermined threshold values.
2. The method of claim 1 , in which at least one of the first and second conditional data integrity screenings is selected from the group consisting of peak sensor current, sensor current, iontophoretic voltage, subject temperature, analyte monitoring device operating temperature, and background current.
3. The method of claim 1 , in which said measurement value comprises one or more data points related to a measurement value at a given time point.
4. The method of claim 1 , in which one of said at least first and second conditional data integrity screenings relates to non-monotonicity of the signal from the biosensor.
5. The method of claim 1 , in which said skin conductance value is represented as an absolute value.
6. The method of claim 1 , further comprising equal weighting of all of said first and second conditional data integrity screenings for determination of said accepting or skipping.
7. The method of claim 1 , further comprising different weighting of one or more of said at least first and second conditional data integrity screenings for determination of said accepting or skipping.
8. The method of claim 1 , in which at least one of said first and second conditional data integrity screenings are selected from the group consisting of temperature of the subject, peak sensor signal, and trend analysis of data.