# US 12,697,049 B2 · Continuous glucose monitors and related sensors utilizing mixed model and bayesian calibration algorithms > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,697,049 B2 - Document ID: US12697049B2 - Status: Granted - Kind: B2 - Application number: 16/779,503 - Filed: 2020-01-31 - Published: 2020-07-30 - Granted: 2026-08-04 - Earliest claimed: 2017-08-21 - Assignees: DexCom, Inc. - Applicants: DexCom, Inc. - Inventors: Stephen J. Vanslyke; Giada Acciaroli; Martina Vettoretti; Andrea Facchinetti; Giovanni Sparacino - Web page: https://patentgrep.com/patent/US12697049B2 ## CPC - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/1451: {for interstitial fluid} - A61B 5/14546: {for measuring analytes not otherwise provided for, e.g. ions, cytochromes} - A61B 5/1495: Calibrating or testing of in-vivo probes - A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)} - A61B 5/7235: {Details of waveform analysis (detecting specific parameters of the electrocardiograph cycle A61B5/349)} - A61B 2560/0223: of calibration, e.g. protocols for calibrating sensors - A61B 2560/0252: using ambient temperature - A61B 2560/0257: using atmospheric pressure - A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A method for monitoring a blood glucose level of a user is provided. The method includes receiving a time-varying electrical signal from an analyte sensor during a temporal phase of a monitoring session. The method includes selecting a calibration model from a plurality of calibration models, wherein the selected calibration model comprises one or more calibration model parameters. The method includes estimating at least one of the one or more calibration model parameters of the selected calibration model based on at least the time-varying electrical signal during the temporal phase of the monitoring session. The method includes estimating the blood glucose level of the user based on the selected calibration model and using the at least one estimated parameter. An apparatus and non-transitory computer readable medium having similar functionality are also provided. ## Claims ### Claim 1 A method for monitoring a blood glucose level of a user, the method comprising: receiving a time-varying electrical signal from an analyte sensor; detecting a start of a temporal phase of a monitoring session, the detecting being based at least in part on an evaluation of a noise component of the time-varying electrical signal, wherein the evaluation of the noise component corresponds to the start of the temporal phase of the monitoring session; selecting a calibration model from a plurality of candidate calibration models, the selected calibration model comprising one or more calibration model parameters, the selecting of the calibration model being based at least in part on the temporal phase of the monitoring session; estimating at least one calibration model parameters of the selected calibration model based on at least the time-varying electrical signal during the temporal phase of the monitoring session; estimating the blood glucose level of the user based on the selected calibration model and using the estimated at least one of the one or more calibration model parameters of the selected calibration model; and displaying the blood glucose level of the user on a display. ### Claim 2 (depends on 1) The method of claim 1, further comprising receiving a reference input. ### Claim 3 (depends on 2) The method of claim 2, wherein the reference input comprises at least one of a blood glucose reference, a noise metric of the time-varying electrical signal, an impedance of the analyte sensor, an input from a sensor configured to measure at least one of an acceleration of the user, a temperature and an atmospheric pressure. ### Claim 4 (depends on 1) The method of claim 1, wherein selecting the calibration model is based at least in part on the selected calibration model having a highest probability, of the plurality of candidate calibration models, of predicting an actual blood glucose level of the user utilizing the time-varying electrical signal. ### Claim 5 (depends on 4) The method of claim 4, wherein the probability is a Bayesian probability. ### Claim 6 (depends on 1) The method of claim 1, wherein selecting the calibration model is based at least in part on detecting a pattern corresponding to the selected calibration model in the time-varying electrical signal. ### Claim 7 (depends on 1) The method of claim 1, wherein estimating at least one of the one or more calibration model parameters of the selected calibration model comprises: setting the one or more calibration model parameters to an initial value; transforming the time-varying electrical signal into an estimated interstitial glucose level of the user utilizing the selected calibration model and the initial value of the one or more calibration model parameters; estimating the blood glucose level based on the estimated interstitial glucose level; updating the one or more calibration model parameters based on a difference between the estimated blood glucose level and a reference input of the blood glucose level of the user; and recursively re-estimating the interstitial glucose level and the blood glucose level based on the selected calibration model and the one or more updated calibration model parameters until a predefined relationship between the reference input of the blood glucose level of the user and at least one of the estimated interstitial glucose level and the estimated blood glucose level is present. ### Claim 8 (depends on 7) The method of claim 7, wherein the predefined relationship comprises at least one of the estimated interstitial glucose level and the estimated blood glucose level being within a predetermined accuracy of the reference input of the blood glucose level. ### Claim 9 (depends on 7) The method of claim 7, wherein the initial value of the one or more calibration model parameters is a prior average value for the one or more calibration model parameters. ### Claim 10 (depends on 1) The method of claim 1, wherein the plurality of candidate calibration models comprise a common global calibration model, each utilizing one or more unique calibration model parameters. ### Claim 11 (depends on 10) The method of claim 10, wherein the global calibration model comprises a first portion corresponding to a baseline behavior of the analyte sensor and a second portion corresponding to a sensitivity of the analyte sensor. ### Claim 12 (depends on 1) The method of claim 1, wherein the time-varying electrical signal comprises a plurality of sensor data points. ### Claim 13 An apparatus configured to monitor a blood glucose level of a user, the apparatus comprising: a memory; a display; and a processor configured to perform operations comprising: receiving a time-varying electrical signal from an analyte sensor, detecting a start of a temporal phase of a monitoring session, the detecting being based at least in part on an evaluation of a noise component of the time-varying electrical signal, wherein the evaluation of the noise component corresponds to the start of the temporal phase of the monitoring session, selecting a calibration model from a plurality of candidate calibration models, the selected calibration model comprising one or more calibration model parameters, the selecting of the calibration model being based at least in part on the temporal phase of the monitoring session, estimating at least one of the one or more calibration model parameters of the selected calibration model based on at least the time-varying electrical signal and a reference input during the temporal phase of the monitoring session, estimating the blood glucose level of the user based on the selected calibration model and using the estimated at least one of the one or more calibration model parameters of the selected calibration model, and displaying the blood glucose level of the user on the display. ### Claim 14 (depends on 13) The apparatus of claim 13, further comprising the analyte sensor. ### Claim 15 (depends on 13) The apparatus of claim 13, the operations further comprising receiving the reference input. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the reference input comprises at least one of a blood glucose reference, a noise metric of the time-varying electrical signal, an impedance of the analyte sensor, an input from a sensor configured to measure at least one of an acceleration of the user, a temperature and an atmospheric pressure. ### Claim 17 (depends on 13) The apparatus of claim 13, the selecting of the calibration model being based at least in part on the selected calibration model having a highest probability, of the plurality of candidate calibration models, of predicting an actual blood glucose level of the user utilizing the time-varying electrical signal. ### Claim 18 (depends on 17) The apparatus of claim 17, wherein the probability is a Bayesian probability. ### Claim 19 (depends on 13) The apparatus of claim 13, the selecting of the calibration model being based at least in part on detecting a pattern corresponding to the selected calibration model in the time-varying electrical signal. ### Claim 20 (depends on 13) The apparatus of claim 13, the operations further comprising estimating at least one of the one or more calibration model parameters of the selected calibration model by: setting the one or more calibration model parameters to an initial value; transforming the time-varying electrical signal into an estimated interstitial glucose level of the user utilizing the selected calibration model and the initial value of the one or more calibration model parameters; estimating the blood glucose level based on the estimated interstitial glucose level; updating the one or more calibration model parameters based on a difference between the estimated blood glucose level and the reference input of the blood glucose level of the user; and recursively re-estimating the interstitial glucose level and the blood glucose level based on the selected calibration model and the one or more updated calibration model parameters until a predefined relationship between the reference input of the blood glucose level of the user and at least one of the estimated interstitial glucose level and the estimated blood glucose level is present. ### Claim 21 (depends on 20) The apparatus of claim 20, wherein the predefined relationship comprises at least one of the estimated interstitial glucose level and the estimated blood glucose level being within a predetermined accuracy of the reference input of the blood glucose level. ### Claim 22 (depends on 20) The apparatus of claim 20, wherein the initial value of the one or more calibration model parameters is a prior average value for the one or more calibration model parameters. ### Claim 23 (depends on 13) The apparatus of claim 13, wherein the plurality of candidate calibration models comprise a common global calibration model, each utilizing one or more unique calibration model parameters. ### Claim 24 (depends on 23) The apparatus of claim 23, wherein the global calibration model comprises a first portion corresponding to a baseline behavior of the analyte sensor and a second portion corresponding to a sensitivity of the analyte sensor. ### Claim 25 (depends on 13) The apparatus of claim 13, wherein the time-varying electrical signal comprises a plurality of sensor data points. ### Claim 26 A non-transitory, computer-readable medium comprising code that, when executed, causes a processor of an apparatus configured to monitor a blood glucose level of a user to perform operations comprising: receiving a time-varying electrical signal from an analyte sensor; detecting a start of a temporal phase of a monitoring session, the detecting being based at least in part on an evaluation of a noise component of the time-varying electrical signal, wherein the evaluation of the noise component corresponds to the start of the temporal phase of the monitoring session; selecting a calibration model from a plurality of candidate calibration models, the selected calibration model comprising one or more calibration model parameters, the selecting of the calibration model being based at least in part on the temporal phase of the monitoring session; estimating at least one of the one or more calibration model parameters of the selected calibration model based on at least the time-varying electrical signal and a reference input during the temporal phase of the monitoring session; estimating the blood glucose level of the user based on the selected calibration model and using the estimated at least one of the one or more calibration model parameters of the selected calibration model; and displaying the blood glucose level of the user on a display. ### Claim 27 (depends on 26) The non-transitory, computer-readable medium of claim 26, further comprising code that, when executed, the operations further comprising receiving the reference input. ### Claim 28 (depends on 27) The non-transitory, computer-readable medium of claim 27, wherein the reference input comprises at least one of a blood glucose reference, a noise metric of the time-varying electrical signal, an impedance of the analyte sensor, an input from a sensor configured to measure at least one of an acceleration of the user, a temperature and an atmospheric pressure. ### Claim 29 (depends on 26) The non-transitory, computer-readable medium of claim 26, wherein selecting the calibration model is based at least in part on the selected calibration model having a highest probability, of the plurality of candidate calibration models, of predicting an actual blood glucose level of the user utilizing the time-varying electrical signal. ### Claim 30 (depends on 29) The non-transitory, computer-readable medium of claim 29, wherein the probability is a Bayesian probability. ### Claim 31 (depends on 26) The non-transitory, computer-readable medium of claim 26, wherein selecting the calibration model is further based at least in part on detecting a pattern corresponding to the selected calibration model in the time-varying electrical signal. ### Claim 32 (depends on 26) The non-transitory, computer-readable medium of claim 26, wherein estimating at least one of the one or more calibration model parameters of the selected calibration model comprises: setting the one or more calibration model parameters to an initial value; transforming the time-varying electrical signal into an estimated interstitial glucose level of the user utilizing the selected calibration model and the initial value of the one or more calibration model parameters; estimating the blood glucose level based on the estimated interstitial glucose level; updating the one or more calibration model parameters based on a difference between the estimated blood glucose level and the reference input of the blood glucose level of the user; and recursively re-estimating the interstitial glucose level and the blood glucose level based on the selected calibration model and the one or more updated calibration model parameters until a predefined relationship between the reference input of the blood glucose level of the user and at least one of the estimated interstitial glucose level and the estimated blood glucose level is present. ### Claim 33 (depends on 32) The non-transitory, computer-readable medium of claim 32, wherein the predefined relationship comprises at least one of the estimated interstitial glucose level and the estimated blood glucose level being within a predetermined accuracy of the reference input of the blood glucose level. ### Claim 34 (depends on 32) The non-transitory, computer-readable medium of claim 32, wherein the initial value of the one or more calibration model parameters is a prior average value for the one or more calibration model parameters. ### Claim 35 (depends on 32) The non-transitory, computer-readable medium of claim 32, wherein the plurality of candidate calibration models comprise a common global calibration model, each utilizing one or more unique calibration model parameters. ### Claim 36 (depends on 35) The non-transitory, computer-readable medium of claim 35, wherein the global calibration model comprises a first portion corresponding to a baseline behavior of the analyte sensor and a second portion corresponding to a sensitivity of the analyte sensor. ### Claim 37 (depends on 32) The non-transitory, computer-readable medium of claim 32, wherein the time-varying electrical signal comprises a plurality of sensor data points. ### Claim 38 A method for monitoring a blood glucose level of a user, the method comprising: receiving time-varying electrical signals from an analyte sensor during at least first and second temporal phases of a monitoring session, each of the first and second temporal phases being different phases of the monitoring session; selecting a first calibration model from a plurality of candidate calibration models for the first temporal phase, the first calibration model comprising one or more calibration model parameters; selecting a second calibration model for the second temporal phase, the second calibration model comprising one or more calibration model parameters, and the second calibration model being different than the first calibration model; estimating at least one of the one or more calibration model parameters of the first calibration model based at least in part on a portion of the time-varying electrical signals received during the first temporal phase; estimating at least one of the one or more calibration model parameters of the second calibration model based at least in part on a portion of the time-varying electrical signals received during the second temporal phase; estimating the blood glucose level of the user during the first temporal phase based at least in part on the first calibration model using the at least one estimated parameter of the first calibration model; estimating the blood glucose level of the user during the second temporal phase based at least in part on the second calibration model using the at least one estimated parameter of the second calibration model; determining at least one of a start or an end of the first temporal phase or the second temporal phase based on an evaluation of a noise component of the time-varying electrical signal satisfying a threshold; and displaying the blood glucose level of the user during the first temporal phase or the blood glucose level of the user during the second temporal phase on a display. ### Claim 39 (depends on 38) The method of claim 38, further comprising receiving a reference input. ### Claim 40 (depends on 39) The method of claim 39, wherein the reference input comprises at least one of a blood glucose reference, a noise metric of the time-varying electrical signal, an impedance of the analyte sensor, an input from a sensor configured to measure at least one of an acceleration of the user, a temperature and an atmospheric pressure. ### Claim 41 (depends on 38) The method of claim 38, wherein selecting the first and second calibration models is based at least in part on the selected first and second calibration models having a highest probability, of the plurality of candidate calibration models, of predicting during the first and second temporal phases, respectively, an actual blood glucose level of the user utilizing the time-varying electrical signals. ### Claim 42 (depends on 41) The method of claim 41, wherein the probability is a Bayesian probability. ### Claim 43 (depends on 38) The method of claim 38, wherein selecting at least one of the first and second calibration models is further based at least in part on detecting a pattern corresponding to the selected calibration model in the time-varying electrical signal. ### Claim 44 (depends on 38) The method of claim 38, wherein estimating at least one of the one or more calibration model parameters of the selected first or second calibration model comprises: setting the one or more calibration model parameters to an initial value; transforming the time-varying electrical signal into an estimated interstitial glucose level of the user utilizing the selected calibration model and the initial value of the one or more calibration model parameters; estimating the blood glucose level based on the estimated interstitial glucose level; updating the one or more calibration model parameters based on a difference between the estimated blood glucose level and a reference input of the blood glucose level of the user; and recursively re-estimating the interstitial glucose level and the blood glucose level based on the selected calibration model and the one or more updated calibration model parameters until a predefined relationship between the reference input of the blood glucose level of the user and at least one of the estimated interstitial glucose level and the estimated blood glucose level is present. ### Claim 45 (depends on 44) The method of claim 44, wherein the predefined relationship comprises at least one of the estimated interstitial glucose level and the estimated blood glucose level being within a predetermined accuracy of the reference input of the blood glucose level. ### Claim 46 (depends on 44) The method of claim 44, wherein the initial value of the one or more calibration model parameters is a prior average value for the one or more calibration model parameters. ### Claim 47 (depends on 38) The method of claim 38, wherein the time-varying electrical signals each comprises a plurality of sensor data points. ### Claim 48 An apparatus configured to monitor a blood glucose level of a user, the apparatus comprising: a memory; a display; and a processor configured to perform operations comprising: receiving time-varying electrical signals from an analyte sensor during at least first and second temporal phases of a monitoring session, each of the first and second temporal phases being different phases of the monitoring session, selecting a first calibration model from a plurality of calibration models for the first temporal phase, the first calibration model comprising one or more calibration model parameters, selecting a second calibration model for the second temporal phase, the second calibration model comprising one or more calibration model parameters, and the second calibration model being different than the first calibration model, estimating at least one of the one or more calibration model parameters of the first calibration model based at least in part on a portion of the time-varying electrical signals received during the first temporal phase, estimating at least one of the one or more calibration model parameters of the second calibration model based at least in part on the time-varying electrical signals received during the second temporal phase, estimating the blood glucose level of the user during the first temporal phase based at least in part on the first calibration model using the at least one estimated parameter of the first calibration model, estimating the blood glucose level of the user during the second temporal phase based at least in part on the second calibration model using the at least one estimated parameter of the second calibration model, determining at least one of a start or an end of the first temporal phase or the second temporal phase based on an evaluation of a noise component of the time-varying electrical signal satisfying a threshold, and displaying the blood glucose level of the user during the first temporal phase or the blood glucose level of the user during the second temporal phase on the display.