# US 10,478,556 B2 · Probability based controller gain

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- Number: US 10,478,556 B2
- Document ID: US10478556B2
- Status: Granted
- Kind: B2
- Application number: 15/061,202
- Filed: 2016-03-04
- Published: 2017-09-07
- Granted: 2019-11-19
- Earliest claimed: 2016-03-04
- Assignees: Roche Diabetes Care, Inc.
- Applicants: Roche Diabetes Care, Inc.
- Inventors: Harvey B. Buck, Jr.; David L. Duke
- Web page: https://patentgrep.com/patent/US10478556B2

## CPC

- A61M 5/1723: {using feedback of body parameters, e.g. blood sugar or pressure}
- A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement}
- 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)}
- G05B 13/024: {in which a parameter or coefficient is automatically adjusted to optimise the performance}
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- A61M 2205/3584: using modem, internet or Bluetooth®
- A61M 2205/3592: using telemetric means, e.g. radio or optical transmission
- A61M 2205/52: with memories providing a history of measured variating parameters of apparatus or patient
- A61M 2230/005: Parameter used as control input for the apparatus
- A61M 2230/201: Glucose concentration
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Methods and systems are disclosed for estimating a glucose level of a person having diabetes and selecting automatically open-loop and closed-loop control for a connected therapy delivery device. The method may comprise analyzing measured glucose results and corresponding impedance values received from a glucose sensor coupled to the person with a probability analysis tool implemented by a microcontroller to determine a total quality score that is based on the minimum constraint of a probability of glucose sensor accuracy determined measured glucose results and a probability of sensing quality determined from the impedance values. The microcontroller may estimate the glucose level of the person with a recursive filter based on the plurality of measured glucose results weighted with the total quality score and select automatically either open-loop control or closed-loop control for the connected therapy delivery device based on the value of the total quality score.

## Claims

### Claim 1

A method for estimating glucose level of a person having diabetes and selecting automatically open-loop and closed-loop control for a connected therapy delivery device, the method comprising:
receiving into a blood glucose management device having a microcontroller a plurality of measured glucose results and corresponding impedance values from a glucose sensor coupled to the person;
using the microcontroller to analyze the plurality of measured glucose results and corresponding impedance values with a probability analysis tool configured to determine a total quality score Qtotal that is based on the minimum constraint of a set of quality metrics which comprises a probability of glucose sensor accuracy PA based on the plurality of measured glucose results and a probability of sensing quality Qac based on the impedance values;
using the microcontroller to estimate the glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the total quality score Qtotal; and
switching, via the microcontroller, the connected therapy delivery device automatically between open-loop control and closed-loop control based on the value of the total quality score Qtotal .

### Claim 2 (depends on 1)

The method of claim 1, wherein the blood glucose management device comprises a blood glucose meter, and the microcontroller is coupled to the glucose sensor, a cellular phone, a smart phone, a personal digital assistant, a personal computer, or a computer server.

### Claim 3 (depends on 1)

The method of claim 1, wherein the glucose sensor comprises a continuous glucose monitoring system physically coupled to the person having diabetes and configured to automatically measure the glucose level of the person, and the connected therapy delivery device is an infusion pump, wherein the infusion pump is wired or wireless connected to the blood glucose management device.

### Claim 4 (depends on 1)

The method of claim 1, wherein the plurality of measured glucose results comprises periodic glucose measurements taken in period selected from the range of every 1 to 10 minutes.

### Claim 5 (depends on 1)

The method of claim 1, wherein the probability analysis tool is configured to determine the total quality score Qtotal further based on at least one of:
when the person eats a meal;
when the person exercises; and
when insulin is delivered to the person.

### Claim 6 (depends on 1)

The method of claim 1, wherein the probability analysis tool comprises a hidden Markov model, wherein:
the hidden Markov model has two states:
a first state SA indicating the glucose sensor is accurate, and
a second state SI indicating the glucose sensor is inaccurate; and
the hidden Markov model is configured to determine the probability of glucose sensor accuracy PA, based on a state of the hidden Markov model and the plurality of measured glucose results.

### Claim 7 (depends on 6)

The method of claim 6, wherein probability of the glucose sensor being in the second state SI is based on a most-recent measured glucose result, a most-recent change in the plurality of measured glucose results, or a combination thereof.

### Claim 8 (depends on 6)

The method of claim 6, wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is
P
A
→
I
=
min
⁡
[
(
1
-
1
1
+
e
-
(
Δ
⁢
⁢
CG
+
α
1
⁢
/
⁢
α
2
)
+
(
1
-
1
1
+
e
-
(
CG
+
α
3
)
⁢
/
⁢
α
4
)
,
1
]
,
where CG is a most-recent measured glucose result, ΔCG is a most-recent change in the plurality of measured glucose results, and α1,α2, α3, and α4 are constants related to characteristics of the glucose sensor.

### Claim 9 (depends on 6)

The method of claim 6, wherein probability of the hidden Markov model remaining in the second state SI is
P
I
→
I
=
max
[
γP
I
k
-
1
-
(
1
1
+
e
-
(
Δ
⁢
⁢
CG
+
α
5
)
⁢
/
⁢
α
6
)
,
0
]
,
where ΔCG is a most-recent change in the plurality of measured glucose results, PIk-1 is a previous probability of transitioning to or being in the second state SI, and γ, α5, and α6 are constants related to characteristics of the glucose sensor.

### Claim 10 (depends on 6)

The method of claim 6, wherein the probability of glucose sensor accuracy PA is
1−[(PA →I×SA )+(PI→I×SI)], wherein
SA=1 when the hidden Markov model is in the first state SA and SA=0 otherwise,
SI =1 when the hidden Markov model is in the second state SI and SI=0 otherwise,
PA→I is a probability of transitioning from the first state SA to the second state SI, and
PI→I is a probability of remaining in the second state SI when in the second state SI.

### Claim 11 (depends on 1)

The method of claim 1, wherein the recursive filter is a Kalman filter or an Extended Kalman filter.

### Claim 12 (depends on 11)

The method of claim 11 further comprising using the microcontroller to predict a future glucose level of the person with the Kalman filter or the Extended Kalman filter, wherein:
the Kalman filter or the Extended Kalman filter comprises a prediction step and a measurement step; and
the prediction step is performed one or more times in order to predict the future glucose level of the person.

### Claim 13 (depends on 11)

The method of claim 11, wherein the Kalman filter or the Extended Kalman filter comprises a state vector, xk=[g ġ {umlaut over (g)}]T, where k represents a kth sample of the state vector, g represents the estimated glucose level of the person, ġ represents a first derivative of g, and {umlaut over (g)} resents a second derivative of g.

### Claim 14 (depends on 13)

The method of claim 13, wherein using the microcontroller to estimate the glucose level of the person comprises determining the state vector
xk={circumflex over (x)}k+Kk(zk−h({circumflex over (x)}k))Qtotal, where {circumflex over (x)}
x
^
k
=
[
1
1
0
0
β
1
1
0
0
β
2
]
⁢
x
k
-
1
,
z
k
=
[
CG
⁢
⁢
Δ
⁢
⁢
CG
]
T
,
H
=
[
1
0
0
0
1
0
]
,
CG is a most-recent measured glucose result at time k, ΔCG is a most-recent change in the plurality of measured glucose results, Kk is a Kalman gain, Qtotal is the total quality score, and β1 and β2 are constants related to characteristics of the glucose sensor.

### Claim 15 (depends on 14)

The method of claim 14, wherein the Kalman gain Kk is based on a measurement uncertainty Rk such that the measurement uncertainty Rk is variable and is based on the probability of sensor accuracy.

### Claim 16 (depends on 15)

The method of claim 15, wherein the measurement uncertainty Rk is:
Rk=max[min((σz−x2)α, σmax2(1−Qtotal)σmax2]+σcgm2,
where (σz−x2)α, represents is the standard deviation of the difference between raw CGM measurements (z) and filtered CGM measurements (x) over a last sampling period, σmax2 represents a maximum physiological variance for glucose in a person with poorly controlled diabetes, Qtotal is the total quality score, and σcgm2 represents a minimum variance for the raw CGM measurements.

### Claim 17 (depends on 16)

The method of claim 16, wherein the recursive filter is configured to estimate the glucose level of the person further based on at least one of:
when the person eats a meal;
when the person exercises; and
when insulin is delivered to the person.

### Claim 18 (depends on 1)

The method of claim 1 further comprising using the microcontroller to predict a future glucose level of the person with a regression analysis tool configured to predict the future glucose level based on the estimated glucose level of the person from the recursive filter.

### Claim 19 (depends on 18)

The method of claim 18, wherein the regression analysis tool comprises a Gaussian process regression analysis.

### Claim 20 (depends on 19)

The method of claim 19, wherein the Gaussian process regression analysis comprises a training algorithm configured to learn one or more characteristics of the person related to the glucose level of the person.

### Claim 21

An apparatus for estimating a glucose level of a person having diabetes and selecting automatically open-loop and closed-loop control for a connected therapy delivery device, the apparatus comprising a microcontroller and a display, wherein:
the microcontroller is configured to:
receive a plurality of measured glucose results and corresponding impedance values from a glucose sensor coupled to the person,
analyze the plurality of measured glucose results and corresponding impedance values with a probability analysis tool configured to determine a total quality score Qtotal that is based on the minimum constraint of a set of quality metrics which comprises a probability of glucose sensor accuracy PA based on the plurality of measured glucose results and a probability of sensing quality Qac based on the impedance values,
estimate the glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the total quality score Qtotal,
switch the connected therapy delivery device automatically between open-loop control and closed-loop control based on the value of the total quality score Qtotal, and
transmit to the display information related to the estimate of the glucose level of the person and the control switch of the therapy delivery device.

### Claim 22 (depends on 21)

The apparatus of claim 21, wherein the microcontroller is coupled to the glucose sensor, a cellular phone, a smart phone, a personal digital assistant, a personal computer, or a computer server.

### Claim 23 (depends on 21)

The apparatus of claim 21, wherein the glucose sensor comprises a continuous glucose monitoring system physically coupled to the person having diabetes and configured to automatically measure the glucose level of the person, and the connected therapy delivery device is a infusion pump, wherein the infusion pump is wired or wireless connected to the blood glucose management device.

### Claim 24 (depends on 21)

The apparatus of claim 21, wherein the plurality of measured glucose results comprises periodic glucose measurements taken in period selected from the range of every 1, 5 or 10 minutes.

### Claim 25 (depends on 21)

The apparatus of claim 21, wherein the probability analysis tool is configured to determine the total quality score Qtotal further based on at least one of:
when the person eats a meal;
when the person exercises; and
when insulin is delivered to the person.

### Claim 26 (depends on 21)

The apparatus of claim 21, wherein the probability analysis tool comprises a hidden Markov model, wherein:
the hidden Markov model has two states:
a first state SA indicating the glucose sensor is accurate, and
a second state SI indicating the glucose sensor is inaccurate; and
the hidden Markov model is configured to determine the probability of glucose sensor accuracy PA , based on a state of the hidden Markov model and the plurality of measured glucose results.

### Claim 27 (depends on 26)

The apparatus of claim 26, wherein probability of the glucose sensor being in the second state SI is based on a most-recent measured glucose result, a most-recent change in the plurality of measured glucose results, or a combination thereof.

### Claim 28 (depends on 26)

The apparatus of claim 26, wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is
P
A
→
I
=
min
⁡
[
(
1
-
1
1
+
e
-
(
Δ
⁢
⁢
CG
+
α
1
⁢
/
⁢
α
2
)
+
(
1
-
1
1
+
e
-
(
CG
+
α
3
)
⁢
/
⁢
α
4
)
,
1
]
,
where CG is a most-recent measured glucose result, ΔCG is a most-recent change in the plurality of measured glucose results, and αl, α2, α3 , and α4 are constants related to characteristics of the glucose sensor.

### Claim 29 (depends on 26)

The apparatus of claim 26, wherein probability of the hidden Markov model remaining in the second state SI is
P
I
→
I
=
max
[
γP
I
k
-
1
-
(
1
1
+
e
-
(
Δ
⁢
⁢
CG
+
α
5
)
⁢
/
⁢
α
6
)
,
0
]
,
where ΔCG is a most-recent change in the plurality of measured glucose results, PIk-1 is a previous probability of transitioning to or being in the second state SI, and γ, α5, and α6 are constants related to characteristics of the glucose sensor.

### Claim 30 (depends on 26)

The apparatus of claim 26, wherein the probability of glucose sensor accuracy PA is
1−[(PA→I×SA)+(PI→I×SI)], wherein
SA=1 when the hidden Markov model is in the first state SA and SA=0 otherwise,
SI=1 when the hidden Markov model is in the second state SI and SI=0 otherwise,
PA→I is a probability of transitioning from the first state SA to the second state SI, and
PI→I is a probability of remaining in the second state SI when in the second state SI.

### Claim 31 (depends on 21)

The apparatus of claim 21, wherein the recursive filter is a Kalman filter or an Extended Kalman filter.

### Claim 32 (depends on 31)

The apparatus of claim 31, wherein the microcontroller predicts a future glucose level of the person with the Kalman filter or the Extended Kalman filter, wherein:
the Kalman filter or the Extended Kalman filter comprises a prediction step and a measurement step; and
the prediction step is performed one or more times in order to predict the future glucose level of the person.

### Claim 33 (depends on 31)

The apparatus of claim 31, wherein the Kalman filter or the Extended Kalman filter comprises a state vector, xk=[g ġ {umlaut over (g)}]T, where k represents a kth sample of the state vector, g represents the estimated glucose level of the person, ġ represents a first derivative of g, and {umlaut over (g)} represents a second derivative of g.

### Claim 34 (depends on 33)

The apparatus of claim 33, wherein the microcontroller estimates the glucose level of the person via state vector
xk={circumflex over (x)}k+Kk(zk−h({circumflex over (x)}k))Qtotal,
where
x
^
k
=
[
1
1
0
0
β
1
1
0
0
β
2
]
⁢
x
k
-
1
,
z
k
=
[
CG
⁢
⁢
Δ
⁢
⁢
CG
]
T
,
H
=
[
1
0
0
0
1
0
]
,
CG is a most-recent measured glucose result, ΔCG is a most-recent change in the plurality of measured glucose results, Kk is a Kalman gain, Qtotal is the total quality score, and β1 and β2 are constants related to characteristics of the glucose sensor.

### Claim 35 (depends on 34)

The apparatus of claim 34, wherein the Kalman gain Kk is based on a measurement uncertainty Rk such that the measurement uncertainty Rk is variable and is based on the probability of sensor accuracy.

### Claim 36 (depends on 35)

The apparatus of claim 35, wherein the measurement uncertainty Rk is:
Rk=max[min((σz−x2)α, σmax2), (1−Qtotal)σmax2]+σcgm2,
where σz−x2)α, represents is the standard deviation of the difference between raw CGM measurements (z) and filtered CGM measurements (x) over a last sampling period, σmax2 represents a maximum physiological variance for glucose in a person with poorly controlled diabetes, Qtotal is the total quality score, and σcgm2 represents a minimum variance for the raw CGM measurements.

### Claim 37 (depends on 36)

The apparatus of claim 36, wherein the recursive filter is configured to estimate the glucose level of the person further based on at least one of:
when the person eats a meal;
when the person exercises; and
when insulin is delivered to the person.

### Claim 38 (depends on 21)

The apparatus of claim 21, wherein the microcontroller predicts a future glucose level of the person with a regression analysis tool configured to predict the future glucose level based on the estimated glucose level of the person from the recursive filter.

### Claim 39 (depends on 38)

The apparatus of claim 38, wherein the regression analysis tool comprises a Gaussian process regression analysis.

### Claim 40 (depends on 39)

The apparatus of claim 39, wherein the Gaussian process regression analysis comprises a training algorithm configured to learn one or more characteristics of the person related to the glucose level of the person.

### Claim 41

A method for estimating glucose level of a person having diabetes, the method comprising:
collecting, via a blood glucose management device having a microcontroller, a plurality of measured glucose results and corresponding impedance values from a glucose sensor coupled to the person;
using the microcontroller to analyze the plurality of measured glucose results and corresponding impedance values with a probability analysis tool configured to determine a total quality score Qtotal that is based on the minimum constraint of a set of quality metrics which comprises a probability of glucose sensor accuracy PA based on the plurality of measured glucose results and a probability of sensing quality Qac based on the impedance values;
using the microcontroller to estimate the glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the total quality score Qtotal; and
alerting via the microcontroller updating a display of the management device with a warning upon the estimated glucose level of the person being one of hypoglycemic and hyperglycemic.

### Claim 42

An apparatus for estimating a glucose level of a person having diabetes, the apparatus comprising a microcontroller and a display, wherein:
the microcontroller is configured to:
collect a plurality of measured glucose results and corresponding impedance values from a glucose sensor coupled to the person,
analyze the plurality of measured glucose results and corresponding impedance values with a probability analysis tool configured to determine a total quality score Qtotal that is based on the minimum constraint of a set of quality metrics which comprises a probability of glucose sensor accuracy PA based on the plurality of measured glucose results and a probability of sensing quality Qac based on the impedance values,
estimate the glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the total quality score Qtotal,
transmit to the display information related to the estimate of the glucose level of the person, and
alert via the display with a warning upon the estimated glucose level of the person being one of hypoglycemic and hyperglycemic.

### Claim 43 (depends on 1)

The method of claim 1 further comprises determining the probability of sensing quality Qac based on the impedance values via utilizing the equation:
QAC=0.5*(1−tan h(a*log(b*E)),
where E is a deviation sum from predetermined values, and parameters a and b are preselected to choose a location of a 0.5 value (1/b) and a rate of change at a 0.5 value (a).

### Claim 44 (depends on 21)

The apparatus of claim 21, wherein the microcontroller is configured to determine the probability of sensing quality Qac based on the impedance values via the equation:
QAC=0.5*(1−tan h(a*log(b*E)),
where E is a deviation sum from predetermined values, and parameters a and b are preselected to choose a location of a 0.5 value (1/b) and a rate of change at a 0.5 value (a).