ANALYTE MONITORING SYSTEM AND METHODS OF USE
First Claim
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1. A method for monitoring an analyte, comprising:
- monitoring a data stream including a set of contiguous source data points related to the concentration of an analyte;
providing one or more sets of maximum lag corrected signals from the one or more sets of monitored data stream, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical monitored data stream, and wherein parameters for the maximum lag correction minimize the correlation between the expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical monitored data stream;
providing one or more sets of maximum smoothing signals from the set of monitored data stream, wherein each set of maximum smoothing signals is generated utilizing a smoothing algorithm;
determining analyte concentration utilizing the one or more sets of maximum lag corrected signals; and
determining a rate of change in the analyte concentration utilizing the one or more sets of maximum smoothing signals.
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Abstract
The present disclosure provides methods of processing data provided by a transcutaneous or subcutaneous analyte sensor utilizing different algorithms to strike a balance between signal responsiveness accompanied by signal noise and the introduction of error associated with that noise. The methods utilize the strengths of a lag correction algorithm and a smoothing algorithm to optimize the quality and value of the resulting data (glucose concentrations and the rates of change in glucose concentrations) to a continuous glucose monitoring system. Also provided are systems and kits.
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10 Claims
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1. A method for monitoring an analyte, comprising:
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monitoring a data stream including a set of contiguous source data points related to the concentration of an analyte; providing one or more sets of maximum lag corrected signals from the one or more sets of monitored data stream, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical monitored data stream, and wherein parameters for the maximum lag correction minimize the correlation between the expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical monitored data stream; providing one or more sets of maximum smoothing signals from the set of monitored data stream, wherein each set of maximum smoothing signals is generated utilizing a smoothing algorithm; determining analyte concentration utilizing the one or more sets of maximum lag corrected signals; and determining a rate of change in the analyte concentration utilizing the one or more sets of maximum smoothing signals. - View Dependent Claims (2, 3, 4, 5)
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6. A method for monitoring an analyte, comprising:
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monitoring a data stream including a set of contiguous source data points related to analyte concentration; providing one or more sets of maximum lag corrected signals from the set of contiguous source data points, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points, and wherein parameters for the maximum lag correction minimize the correlation between the expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical source data points; providing one or more sets of maximum smoothing signals from the set of contiguous source data points, wherein each set of maximum smoothing signals is generated utilizing a smoothing algorithm; determining analyte concentration utilizing the one or more sets of maximum lag corrected signals; and determining a rate of change in the analyte concentration utilizing the one or more sets of maximum smoothing signals.
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7. A method for monitoring an analyte, comprising:
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monitoring a data stream including a set of contiguous source data points related to analyte concentration; providing one or more sets of maximum lag corrected signals from the set of contiguous source data points, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points, and wherein parameters for the maximum lag correction minimize the correlation between the expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical source data points; providing one or more sets of maximum smoothing signals from the set of contiguous source data points, wherein each set of maximum smoothing signals is generated utilizing a smoothing algorithm; determining analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the one or more sets of maximum smoothing signals, wherein more weight is placed on the one or more sets of maximum lag corrected signals to determine the analyte concentration; and determining a rate of change in the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the one or more sets of maximum smoothing signals, where more weight is placed on the one or more sets of maximum smoothing signals to determine the rate of change.
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8. A method for monitoring an analyte, comprising:
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monitoring a data stream including a set of contiguous source data points related to analyte concentration; providing one or more sets of maximum lag corrected signals from the set of contiguous source data points, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points, and wherein parameters for the maximum lag correction minimize the correlation between the expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical source data points; providing a first one or more sets of maximum smoothing signals from the set of contiguous source data points, wherein each set of maximum smoothing signals is generated utilizing a smoothing algorithm; providing a second one or more sets of maximum smoothing signals from the set of contiguous source data points, wherein each of the second set of maximum smoothing signals is generated utilizing a second smoothing algorithm; determining analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothing signals, wherein more weight is placed on the one or more sets of maximum lag corrected signals to determine the analyte concentration; and determining a rate of change in the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothing signals, where more weight is placed on the first and second one or more sets of maximum smoothing signals to determine the rate of change. - View Dependent Claims (9, 10)
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Specification