Method and device for monitoring an analyte concentration in the living body of a human or animal

• US 7,188,034 B2
• Filed: 04/25/2005
• Issued: 03/06/2007
• Est. Priority Date: 04/24/2004
• Status: Active Grant

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Roche Diagnostics Operations Incorporated (Roche Holding AG)
• Inventors: Staib, Arnulf, Hegger, Rainer
• Primary Examiner(s): Bui; Bryan
• Assistant Examiner(s): Sun; Xiuqin
• Application Number:
  US11/113,606
• Publication Number:
  US 20050240356A1
• Time in Patent Office: 680 Days
• Field of Search: 702/22, 702/19, 702/25, 702/117, 600/345, 600/310, 706/44, 436/518, 436/95, 250/573, 250/287, 435/14
• US Class Current: 702/22
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement

Abstract

The present invention generally relates to a method and a device for monitoring an analyte concentration in the living body of a human or animal. In particular to a method and device for determining analyte values y(t_n) correlating with the concentration to be determined are determined for consecutive points in time t_n. The analyte values y(t_n) is used to predict a prediction value for an analyte y(t_n0+Δt) over a prediction period Δt.

Claims

1. A method for continuous monitoring of an analyte concentration in the living body of a human or animal, the method comprising:

determining an analyte values y(t_n) correlating with the concentration of the analyte for consecutive points in time t_n;

calculating a prediction value y(t_n0+Δ

t) over a prediction period Δ

t based on the analyte value y(t_n) using the following equation;

y(t_n0+Δ

t)=F(t_n0, t<sub>n0−

Δ

n</sub>, . . . , t<sub>(n0−

(m−

2)Δ

n)</sub>, t<sub>(n0−

(m−

1)Δ

n)</sub>); and

generating a warning signal if the prediction value y(t_n0+Δ

t) deviates from a predetermined value by more than a threshold value,

wherein function F(t_k, t<sub>k−

Δ

n</sub>, t<sub>k−

2Δ

n</sub>, . . . , t<sub>k−

(m−

2)Δ

n</sub>, t<sub>k−

(m−

1)Δ

n</sub>) depends on the analyte values y(t_k), y(t<sub>k−

Δ

n</sub>), y(t<sub>k−

2Δ

n</sub>), . . . , y(t<sub>k−

(m−

2)Δ

n</sub>), y(t<sub>k−

(m−

1)Δ

n</sub>) and the function F is used to approximate the progression of the analyte values y(t_n) at time t_n0 in vicinity U of an analyte value y(t_n0) with a pre-determined accuracy σ

, such that

2. The method according to claim 1, wherein the function F depends not only on analyte values y(t_k), y(t_k−

Δ

n), y(t<sub>k−

2Δ

n</sub>), . . . , y(t<sub>k−

(m−

2)Δ

n</sub>), y(t<sub>k−

(m−

1)Δ

n</sub>), but in addition on their first time derivatives.

3. The method according to claim 1, wherein the function F depends not only on analyte values y(t_k), y(t_k−

Δ

n), y(t<sub>k−

2Δ

n</sub>), . . . , y(t<sub>k−

(m−

2)Δ

n</sub>), y(t<sub>k−

(m−

1)Δ

n</sub>), but in addition on their first and second time derivatives.

4. The method according to claim 1, wherein the function F contains linear or square terms.

5. The method according to claim 1, wherein the function F is a linear function.

6. The method according to claim 1, wherein the function F can be represented by coefficients a₀ to a_m as follows:

7. The method according to claim 6, wherein the coefficients, a₀ to a_m, are determined by minimizing the sum

8. The method according to claim 6, wherein the coefficients, a₀ to a_m, are determined by solving a system of linear equations which contains one equation each of the type

9. The method according to claim 8, wherein the system of equations contains more than m+1 equations and is solved numerically by approximation to determine the coefficients, a₀ to a_m.

10. The method according to claim 1, wherein the analyte values y(t_n) used to determine the function F are obtained by numerical processing, in particular by filtering, of measured values.

11. The method according to claim 1, wherein the analyte values y(t_n) used to calculate the prediction value are determined from consecutive points in time t_n which are separated by intervals of 30 seconds to 5 minutes, preferably by 1 to 3 minutes.

12. The method according to claim 1, wherein the analyte values y(t_n) used to calculate the prediction value or one of the prediction values are determined for times t_n which are separated by an interval corresponding to the prediction period Δ

t.

13. The method according to claim 1, wherein the number of the coefficients, a₀ to a_m, is 4 to 11, preferably 6 to 9.

14. The method according to claim 1, wherein the function F is determined as a function of transformed coordinate values Ty(t_k), Ty(t_k−

Δ

n), Ty(t<sub>k−

2Δ

n</sub>), . . . , Ty(t<sub>k−

(m−

2)Δ

n</sub>), Ty(t<sub>k−

(m−

1)Δ

n</sub>), which were determined by means of a transformation, preferably a linear transformation, from analyte values y(t_k), y(t<sub>k−

Δ

n</sub>), y(t<sub>k−

2Δ

n</sub>), . . . , y(t<sub>k−

(m−

2)Δ

n</sub>), y(t<sub>k−

(m−

1)Δ

n</sub>), whereby the transformation is selected such that at least one of the values Ty(t_k), Ty(t<sub>k−

Δ

n</sub>), Ty(t<sub>k−

2Δ

n</sub>), . . . , Ty(t<sub>k−

(m−

2)Δ

n</sub>), Ty(t<sub>k−

(m−

1)Δ

n</sub>) has a negligible influence on the function F at the predetermined accuracy σ

.

15. A method for continuous monitoring of an analyte concentration in the living body of a human or animal, the method comprising:

determining an analyte values y(t_n) correlating with the concentration of the analyte for consecutive points in time t_n;

calculating a prediction value y(t_n0+Δ

t) over a prediction period Δ

t based on the analyte value y(t_n) using the following equation;

y(t_n0+Δ

t)=F(t_n0, t<sub>n0−

Δ

n</sub>, . . . , t<sub>(n0−

(m−

2)Δ

n)</sub>, t<sub>(n0−

(m−

1)Δ

n)</sub>)

generating a warning signal if the prediction value y(t_n0+Δ

t) deviates from a predetermined value by more than a threshold value,

wherein function F(t_k, t<sub>k−

Δ

n</sub>, t<sub>k−

2Δ

n</sub>, . . . t<sub>k−

(m−

2)Δ

n</sub>, t<sub>k−

(m−

1)Δ

n</sub>) depends on the analyte values y(t_k), y(t<sub>k−

Δ

n</sub>), y(t<sub>k−

2Δ

n</sub>), . . . , y(t<sub>k−

(m−

2Δ

n</sub>), y(t<sub>k−

(m−

1)Δ

n</sub>); and

adapting the function F(t_k, t<sub>k−

Δ

n</sub>, t<sub>k−

2Δ

n</sub>, . . . , t<sub>k−

(m−

2)Δ

n</sub>, t<sub>k−

(m−

1)Δ

n</sub>) in a vicinity U of an analyte value y(t_n0+Δ

t) at a point in time t_n=t_n0+Δ

t for a calculated prediction value y(t_n0+Δ

t), such that the adapted function F approximates the progression of analyte values y(t_n) in the vicinity U with a predetermined accuracy and an additional prediction value y(t_n0+2Δ

t) is calculated therefrom,

whereby n₀, m, and Δ

n are integers.