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EP-4111965-B1 - GLUCOSE SENSOR IDENTIFICATION USING ELECTRICAL PARAMETERS

EP4111965B1EP 4111965 B1EP4111965 B1EP 4111965B1EP-4111965-B1

Inventors

  • GEE, Elaine
  • PICCININI, Francesca
  • ZHOU, LI
  • TRAN, Chi A
  • BATMANGHELICH, Farhad
  • NAVA-GUERRA, Leonardo
  • ARGUELLES MORALES, Juan Enrique
  • PATEL, ANUJ M.
  • AROYAN, SARKIS D.

Dates

Publication Date
20260506
Application Date
20220629

Claims (13)

  1. A method for calibrating a glucose sensor (12, 130), the method comprising: determining, by one or more processors, a set of electrical parameters for the glucose sensor, wherein the electrical parameters comprise a voltage at the glucose sensor, an electrical current for the glucose sensor, or an impedance for the glucose sensor; determining, by the one or more processors, a cluster for the glucose sensor (12, 130) from a plurality of clusters based on the set of electrical parameters, wherein each cluster of the plurality of clusters identifies respective configuration information; and configuring, by the one or more processors, the glucose sensor to determine a glucose level of a patient based on configuration information (142) identified by the determined cluster.
  2. The method of claim 1, wherein determining the cluster comprises applying a machine learning algorithm, wherein the machine learning algorithm has been trained using in vitro features and in vivo features for a training set of glucose sensors.
  3. The method of claim 2, wherein applying the machine learning algorithm comprises applying a reconstruction independent component analysis (RICA) algorithm to the set of electrical parameters, wherein the RICA algorithm has been trained using the in vitro features and the in vivo features for the training set of glucose sensors.
  4. The method of claim 2, wherein applying the machine learning algorithm comprises applying a principal components analysis (PCA) algorithm to the set of electrical parameters, wherein the PCA algorithm has been trained using the in vitro features and the in vivo features for the training set of glucose sensors.
  5. The method of claim 2, wherein applying the machine learning algorithm comprises applying both reconstruction independent component analysis (RICA) and principal components analysis (PCA) to the set of electrical parameters, wherein the RICA algorithm and the PCA algorithm have been trained using the in vitro features and the in vivo features for the training set of glucose sensors.
  6. The method of any of claims 2-5, further comprising training, by the one or more processors, the machine learning algorithm using in the vitro features and the in vivo features for the training set of sensor devices.
  7. The method of any of claims 2-6, wherein the configuration information comprises a correction factor determined based on a subset of sensor devices of the training set of glucose sensors that are assigned to the cluster.
  8. The method of claim 1, wherein the configuration information comprises a correction factor.
  9. The method of any preceding claim, wherein the glucose sensor is a first glucose sensor, wherein the cluster is a first cluster, and the set of electrical parameters is a first set of electrical parameters, the method further comprising: determining, by the one or more processors, a second set of electrical parameters for a second glucose sensor of the plurality of glucose sensors; determining, by the one or more processors, a second cluster of the plurality of clusters for the second glucose sensor based on the second set of electrical parameters; determining, by the one or more processors, that the second glucose sensor does not satisfy a quality metric in response to determining that the second glucose sensor is associated with the second cluster and that the second cluster is associated with quality value that does not satisfy the quality metric; and outputting, by the one or more processors, an indication that the second glucose sensor does not satisfy the quality metric.
  10. The method of any preceding claim, wherein configuring the glucose sensor (12, 130) comprises outputting an indication of the cluster.
  11. A device for calibrating a glucose sensor (12, 130), the device comprising: a memory; and one or more processors implemented in circuitry and in communication with the memory, the one or more processors configured to perform the method of any preceding claim.
  12. A computer-readable storage medium having stored thereon instructions that, when executed, configure a processor to perform the method of any of claims 1-10.
  13. A computer program comprising instructions which, when the program is executed by a processor, cause the processor to carry out the method of any of claims 1-10.

Description

TECHNICAL FIELD This disclosure relates to glucose sensors. BACKGROUND Glucose sensors are configured to detect and/or quantify the amount of glucose in a patient's body (e.g., interstitial glucose or possibly blood glucose), which enables patients and medical personnel to monitor physiological conditions within the patient's body. In some examples, it may be beneficial to monitor glucose levels on a continuing basis (e.g., in a diabetic patient). Thus, glucose sensors have been developed for use in obtaining an indication of glucose levels in a diabetic patient. Such indications are useful in monitoring and/or adjusting a treatment regimen, which typically includes administration of insulin to the patient. A patient can measure their blood glucose (BG) using a BG measurement device (i.e., glucose meter), such as a test strip meter. A continuous glucose measurement system (or a continuous glucose monitor (CGM)) may be configured to determine interstitial glucose; although possible for CGM to determine blood glucose. A hospital hemacue may also be used to determine glucose level CGMs may be beneficial for patients who desire to take more frequent glucose measurements. Some example CGM systems include subcutaneous (or short-term) sensors and implantable (or long-term) sensors. A CGM system may execute an initialization sequence when the CGM is inserted into a patient. The initialization sequence may speed up sensor equilibration and may allow a CGM system to provide reliable glucose measurements earlier. US 2012/0262298 A1 relates to an advanced analyte sensor calibration and error detection. US 2004/0223155 A1 relates to a method of characterizing spectrometer instruments and providing calibration models to compensate for instrument variation. SUMMARY In general, this disclosure describes techniques for calibrating glucose sensors (e.g., a continuous glucose monitor or "CGM") based on one or more electrical parameters. More particularly, this disclosure describes techniques and devices for determining electrical parameters of a glucose sensor in vitro and/or in vivo and identifying a cluster for the glucose sensor that may be used for configuring the glucose sensor. A glucose sensor may comprise configuration information related to manufacturing the glucose sensor, which may be used to determine a glucose level of a patient. To set the configuration information, a process may comprise measuring the glucose level of a patient with the glucose sensor in vivo and measuring the glucose level of a patient with a pre-configured device. In this example, the configuration information may be set (e.g., by a clinician) based on a comparison of the glucose level measured by the glucose sensor and the glucose level measured by the pre-configured device. In some examples, the glucose level measured by the glucose sensor may be an interstitial glucose level, and the glucose level measured by the pre-configured device may be blood glucose level. In such examples, a processor may be configured to convert the interstitial glucose level to a blood glucose level (e.g., such as by scaling and offsetting), and compare the blood glucose level determined from the interstitial glucose level to the blood glucose level measured by the pre-configured device. In accordance with the techniques of the disclosure, configuration information of a glucose monitor may be set based on one or more electrical parameters measured in vitro. Electrical parameters include a voltage, an electrical current (e.g., iSig), or an impedance. In general, the electrical current (iSig) flowing through the sensing (e.g., working) electrode of a glucose sensor is indicative of the glucose level in the patient's interstitial fluid. For example, processing circuitry may measure the one or more electrical parameters after the glucose monitor is manufactured and before the glucose monitor is implanted into a patient. The processing circuitry identifies a cluster for the glucose sensor based on the one or more electrical parameters. Each cluster of the plurality of clusters identifies configuration information. The configuration information may include a correction factor for improving an accuracy of the glucose sensor. The glucose sensor may be calibrated with configuration information (e.g., the correction factor) based on a cluster identified for the glucose sensor. For example, in response to identifying a particular cluster for the glucose device, the processing circuitry may calibrate the glucose sensor with a respective set of configuration information that are assigned to the particular cluster. In this way, a glucose sensor may be calibrated without relying on in vivo calibration techniques, which may improve an accuracy of the glucose device. Moreover, glucose sensors that are assigned to a cluster associated with inaccurate glucose sensors may be pruned, which may help to improve an accuracy and/or reliability of a resulting set of glucose monitors. A mor