# US 11,896,371 B2 · Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,896,371 B2 - Document ID: US11896371B2 - Status: Granted - Kind: B2 - Application number: 17/073,852 - Filed: 2020-10-19 - Published: 2021-02-04 - Granted: 2024-02-13 - Earliest claimed: 2012-09-26 - Assignees: Abbott Diabetes Care Inc. - Applicants: Abbott Diabetes Care Inc. - Inventors: Erwin Satrya Budiman - Web page: https://patentgrep.com/patent/US11896371B2 ## CPC - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/14503: {invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors (A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence)} - A61B 5/14546: {for measuring analytes not otherwise provided for, e.g. ions, cytochromes} - A61B 5/7203: {for noise prevention, reduction or removal} - A61B 5/7235: {Details of waveform analysis (detecting specific parameters of the electrocardiograph cycle A61B5/349)} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 2560/0475: Special features of memory means, e.g. removable memory cards - G06F 2218/10: by analysing the shape of a waveform, e.g. extracting parameters relating to peaks - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, devices, and systems are provided for correcting lag in measurements of analyte concentration level in interstitial fluid. The invention includes receiving a signal representative of sensor data from an analyte monitoring system related to an analyte level measured over time, computing rates of change of the sensor data for a time period of the sensor data, computing a rate distribution of the rates of change, transforming the rate distribution into a linear arrangement, determining a best-fit line for the transformed rate distribution, computing a slope of the best-fit line; and using the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data. Numerous additional features are disclosed. ## Claims ### Claim 1 A method for lag correction of sensor data from a glucose sensor, comprising: receiving sensor data from a glucose sensor, the sensor data including historical glucose sensor data over a plurality of intervals; determining a variability of the historical glucose sensor data over each of the plurality of intervals; comparing the variability of the historical glucose sensor data of a recent interval of the plurality of intervals to at least one prior variability determined from prior intervals of the plurality of intervals; calculating a noise metric of the historical glucose sensor data of the recent interval of the plurality of intervals; setting an amount of lag correction based on the noise metric and at least one of the variability of the recent interval and the at least one prior variability; and computing lag corrected values based on the amount of lag correction. ### Claim 2 (depends on 1) The method of claim 1, further comprising: defining a probability density function of the historical glucose sensor data over at least a portion of one of the plurality of intervals; and setting the amount of lag correction based further on the probability density function of the historical glucose sensor data. ### Claim 3 (depends on 1) The method of claim 1, wherein the variability of the historical glucose sensor data is measured using a normalized glucose variability ratio. ### Claim 4 (depends on 1) The method of claim 1, wherein the variability is determined based on at least one of standard deviation or absolute distance between an upper and a lower quartile of the historical glucose sensor data over one or more of the plurality of intervals. ### Claim 5 (depends on 1) The method of claim 1, further comprising: comparing the variability of the historical glucose sensor data of the recent interval to a variability determined from population data. ### Claim 6 (depends on 1) The method of claim 1, wherein setting the amount of lag correction further includes relating the lag corrected values to a scaling factor based on an equation: Glag(t)=G latest(t)+(K G c(t)) wherein K represents the scaling factor, G latest(t) represents a non-lag corrected glucose value at time t, G c(t) represents a nominal lag correction amount at time t, and G lag(t) represents the lag corrected value at time t. ### Claim 7 (depends on 1) The method of claim 1, wherein the amount of lag correction is within a range from zero to one, and wherein zero corresponds to no lag correction and one corresponds to full lag correction. ### Claim 8 (depends on 1) The method of claim 1, wherein each of the plurality of intervals is at least two days. ### Claim 9 (depends on 1) The method of claim 1, wherein at least a portion of one of the plurality of intervals is three hours or less. ### Claim 10 A system for lag correction of sensor data from a glucose sensor, comprising: a processor; and a memory coupled to the processor, the memory storing processor executable instructions to: receive sensor data from the glucose sensor, the sensor data including historical glucose sensor data over a plurality of intervals; determine a variability of the historical glucose sensor data over each of the plurality of intervals; compare the variability of the historical glucose sensor data of a recent interval of the plurality of intervals to at least one prior variability determined from prior intervals of the plurality of intervals; calculate a noise metric of the historical glucose sensor data of the recent interval of the plurality of intervals; set an amount of lag correction based on the noise metric and at least one of the variability of the recent interval and the at least one prior variability; and compute lag corrected values based on the amount of lag correction. ### Claim 11 (depends on 10) The system of claim 10, further comprising instructions to: define a probability density function of the historical glucose sensor data over at least a portion of one of the plurality of intervals; and set the amount of lag correction based further on the probability density function of the historical glucose sensor data. ### Claim 12 (depends on 10) The system of claim 10, wherein the variability of the historical glucose sensor data is measured using a normalized glucose variability ratio. ### Claim 13 (depends on 10) The system of claim 10, wherein the variability is determined based on at least one of standard deviation or absolute distance between an upper and a lower quartile of the historical glucose sensor data over one or more of the plurality of intervals. ### Claim 14 (depends on 10) The system of claim 10, further comprising instructions to: compare the variability of the historical glucose sensor data of the recent interval to a variability determined from population data. ### Claim 15 (depends on 10) The system of claim 10, wherein setting the amount of lag correction further includes relating the lag corrected values to a scaling factor based on an equation: Glag(t)=G latest(t)+(K G c(t)) wherein K represents the scaling factor, G latest(t) represents a non-lag corrected glucose value at time t, G c(t) represents a nominal lag correction amount at time t, and G lag(t) represents the lag corrected value at time t. ### Claim 16 (depends on 10) The system of claim 10, wherein the amount of lag correction is within a range from zero to one, and wherein zero corresponds to no lag correction and one corresponds to full lag correction. ### Claim 17 (depends on 10) The system of claim 10, wherein each of the plurality of intervals is at least two days. ### Claim 18 (depends on 10) The system of claim 10, wherein at least a portion of one of the plurality of intervals is three hours or less. ### Claim 19 A computer program product stored on a computer-readable medium comprising executable instructions to perform operations comprising: receiving sensor data from a glucose sensor, the glucose sensor including historical glucose sensor data over a plurality of intervals; determine a variability of the historical glucose sensor data over each of the plurality of intervals; compare the variability of the historical glucose sensor data of a recent interval of the plurality of intervals to at least one prior variability determined from prior intervals of the plurality of intervals; calculating a noise metric of the historical glucose sensor data of the recent interval of the plurality of intervals; set an amount of lag correction based on the noise metric and at least one of the variability of the recent interval and the at least one prior variability; and compute lag corrected values based on the amount of lag correction. ### Claim 20 (depends on 19) The computer program product of claim 19, further comprising instructions to: define a probability density function of the historical glucose sensor data over at least a portion of one of the plurality of intervals; and set the amount of lag correction based further on the probability density function of the historical glucose sensor data. ### Claim 21 (depends on 19) The computer program product of claim 19, wherein the variability of the historical glucose sensor data is measured using a normalized glucose variability ratio. ### Claim 22 (depends on 19) The computer program product of claim 19, wherein the variability is determined based on at least one of standard deviation or absolute distance between an upper and a lower quartile of the historical glucose sensor data over one or more of the plurality of intervals. ### Claim 23 (depends on 19) The computer program product of claim 19, further comprising instructions to perform operations further comprising: comparing the variability of the historical glucose sensor data of the recent interval to a variability determined from population data.