Adaptive signal processing

Patent metadata

Field	Value
Publication number	US-2022262475-A1
Application number	US-202217661674-A
Country	US
Kind code	A1
Filing date	May 2, 2022
Priority date	May 19, 2014
Publication date	Aug 18, 2022
Grant date	—

Abstract

Disclosed herein are techniques related to adaptive signal processing. The techniques may involve: obtaining a plurality of unfiltered measurement values based on signals generated by a sensor; determining a plurality of filtered measurement values based on the plurality of unfiltered measurement values; determining, based on the plurality of filtered measurements, a first derivative metric for a current filtered measurement of the plurality of filtered measurements and a second derivative metric for the current filtered measurement; determining an output filtered measurement indicative of a physiological condition of a user based at least in part on the current filtered measurement, the first derivative metric, the second derivative metric, and a previous output measurement; and outputting the output filtered measurement.

First claim

What is claimed is:

1 . A system for controlling insulin delivery, the system comprising:
one or more processors; and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of:
obtaining a plurality of unfiltered measurement values based on signals generated by a sensor;
determining a plurality of filtered measurement values based on the plurality of unfiltered measurement values;
determining, based on the plurality of filtered measurements, a first derivative metric for a current filtered measurement of the plurality of filtered measurements and a second derivative metric for the current filtered measurement;
determining an output filtered measurement indicative of a physiological condition of a user based at least in part on the current filtered measurement, the first derivative metric, the second derivative metric, and a previous output measurement; and
outputting the output filtered measurement.

2 . The system of claim 1 , wherein outputting the output filtered measurement comprises outputting the output filtered measurement for transmission to an infusion device for delivery of insulin based on the output filtered measurement.

3 . The system of claim 1 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
determining a process variance metric based at least in part on the first derivative metric and the second derivative metric, wherein determining the output filtered measurement comprises determining the output filtered measurement indicative of the physiological condition of the user based at least in part on the current filtered measurement, the process variance, and the previous output measurement.

4 . The system of claim 1 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
determining a frequency estimate associated with the current filtered measurement based on the first derivative metric; and determining a noise estimate associated with the current filtered measurement based on the second derivative metric, wherein determining the output filtered measurement comprises determining the output filtered measurement indicative of the physiological condition of the user based at least in part on the current filtered measurement, the frequency estimate, the noise estimate, and the previous output measurement.

5 . The system of claim 4 ,
wherein determining the frequency estimate comprises scaling the first derivative metric by a calibration factor for converting the output filtered measurement to a second value, the first derivative metric comprising an average of first derivative values associated with the current filtered measurement and one or more preceding filtered measurements, wherein determining the noise estimate comprises scaling the second derivative metric by the calibration factor, the second derivative metric comprising an average of second derivative values associated with eh current filtered measurement and the one or more preceding filtered measurements.

6 . The system of claim 4 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
determining a rate of change metric associated with the current filtered measurement based at least in part on the first derivative metric; scaling the rate of change metric based on the noise estimate to generate a scaled rate of change metric; and adding the scaled rate of change metric to the current filtered measurement to generate an adjusted filtered measurement, wherein determining the output filtered measurement comprises determining the output filtered measurement based at least in part on the adjusted filtered measurement, the frequency estimate, the noise estimate, and the previous output measurement.

7 . The system of claim 1 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
identifying a dropout condition based at least in part on one or more of the first derivative metric and the second derivative metric associated with the current filtered measurement; and determining an adjusted filtered measurement in response to identifying the dropout condition, wherein determining the output filtered measurement comprises determining the output filtered measurement based at least in part on the adjusted filtered measurement, the first derivative metric, the second derivative metric, and the previous output measurement.

8 . The system of claim 7 , wherein identifying the dropout condition comprises identifying the dropout condition when the first derivative metric associated with the current filtered measurement is greater than a first derivative dropout threshold value and the second derivative metric associated with the current filtered measurement is less than a second derivative dropout threshold value.

9 . The system of claim 1 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
adjusting the current filtered measurement to compensate for delay based at least in part on one or more of the first derivative metric and the second derivative metric associated with the current filtered measurement, resulting in an adjusted filtered measurement, wherein determining the output filtered measurement comprises determining the output filtered measurement based on the adjusted filtered measurement, the first derivative metric, the second derivative metric, and the previous output measurement.

10 . The system of claim 9 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:
determining a noise estimate associated with the current filtered measurement based on the second derivative metric associated with the current filtered measurement, wherein adjusting the current filtered measurement comprises:
scaling a rate of change metric for the current filtered measurement based on the noise estimate, resulting in a scaled rate of change metric; and
adding the scaled rate of change metric to the current filtered measurement to obtain the adjusted filtered measurement.

11 . The system of claim 1 , wherein determining the output filtered measurement comprises:
determining an intermediate error estimate based on a preceding output error estimate, the first derivative metric, and the second derivative metric; determining a filter gain value based on the intermediate error estimate and a measurement error value; and determining the output filtered measurement based on the current filtered measurement, the filter gain value, and the previous output measurement.

12 . The system of claim 1 , wherein determining the output filtered measurement comprises:
implementing a Kalman filter to filter the current filtered measurement using the previous output measurement, the first derivative metric, and the second derivative metric.

13 . The system of claim 12 , wherein implementing the Kalman filter comprises:
determining an intermediate error estimate based on a preceding output error estimate, the first derivative metric, and the second derivative metric; determining a Kalman filter gain value based on the intermediate error estimate and a measurement e

Assignees

• Medtronic Minimed Inc

Inventors

• Nogueira Keith
• Agrawal Pratik J
• Kannard Brian T
• Li Xiaolong
• Liang Bradley C
• Shah Rajiv
• Zhong Yuxiang

Classifications

• A61M5/14244

  adapted to be carried by the patient, e.g. portable on the body · CPC title

• A61M2205/3303

  Using a biosensor · CPC title

• G05B15/02

  electric · CPC title

• G16H20/17 Primary

  delivered via infusion or injection · CPC title

• A61B5/4839

  combined with drug delivery · CPC title