APPARATUS AND METHOD FOR GENERATING A CONDITION INDICATION

• US 20120209099A1
• Filed: 02/10/2012
• Published: 08/16/2012
• Est. Priority Date: 08/10/2009
• Status: Active Grant

• Current Assignee: Diabetes Tools Sweden AB
• Original Assignee: Diabetes Tools Sweden AB
• Inventors: Liljeryd, Lars Gustaf, LJUHS, Michael Kjell, Riback, Jacob Lars Fredrik
• Granted Patent: US 9,336,355 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61M 2230/005 Parameter used as control input for the apparatus
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

An apparatus for generating a condition indication using a time sequence of data values, each data value representing a physiological measure of a condition of a subject at a time, includes: a transformer for transforming the time sequence of data values into a transformed sequence of data values using a transform rule, wherein the transform rule is such that a certain characteristic in a time course of the physiological measure is represented by the transformed sequence of data values is more linear than the time course before the transform; a rate of change calculator for calculating an estimated rate of change for the transformed sequence of data values; and a processor for processing the estimated rate of change to output the condition indication.

Claims

1. Apparatus for generating a condition indication using a time sequence of data values, each data value representing a physiological measure of a condition of a subject at a time, comprising:

a transformer for transforming the time sequence of data values into a transformed sequence of data values using a transform rule, wherein the transform rule is such that a certain characteristic in a time course of the physiological measure is represented by the transformed sequence of data values is more linear than the time course before the transform;

a rate of change calculator for calculating an estimated rate of change for the transformed sequence of data values; and

a processor for processing the estimated rate of change to output the condition indication.

2. Apparatus in accordance with claim 1, in which the processor is configured for generating a graphic, audible or tactile display of the estimated rate of change or for generating an electrical, magnetic or electro-magnetic signal representing the estimated rate of change.

3. Apparatus in accordance with claim 1, in which the transformed sequence of data values has a transformed value for a current time instant, and wherein the processor is configured for predicting a data value for a later time instance from the estimated rate of change,

in which the processor is configured for evaluating, whether the predicted data value exceeds a high alarm threshold or is below a low alarm threshold, and

in which the processor is configured for generating a visual, audible, tactile, mechanical, electro or magnetic alarm indication.

4. Apparatus in accordance with claim 1, in which the transformer comprises a memory for storing the transform rule as parameters for a parameterized function, or as a look-up table in which non-transform data values have associated transformed data values.

5. Apparatus in accordance with claim 1,

in which the transformer comprises an estimated probability function calculator for calculating an estimated probability function associated with a set of data values;

and

a transform calculator for calculating the transform rule using a predetermined target probability function being different from the estimated probability function so that the probability function of a set of transformed data values is closer to the target probability function than the estimated probability function.

6. Apparatus in accordance with claim 5, in which the estimated probability function is a cumulative distribution function associated with the set of data values, and

in which the predetermined target probability function is a cumulative distribution function.

7. Apparatus in accordance with claim 6, in which the target a cumulative distribution function is a linearly increasing a cumulative distribution function corresponding to a constant probability density function indicating the same probability for a certain data value within the certain characteristic.

8. Apparatus in accordance with claim 1, in which the time sequence of data values comprises glycemic data values indicating a glycemic condition for a time instance of the subject.

9. Apparatus in accordance with claim 1, in which the certain characteristic is represented by a characteristic data record, the characteristic data record comprising value data derived by average rates of change for different value ranges in a measurement range, where the value data have associated time instants, and

in which the rate of change calculator is configured for calculating a difference between two transformed values of the sequence of transformed values and for dividing the difference by a time difference calculated between the time instances associated with the two transformed values.

10. Apparatus in accordance with claim 3, in which the processor is configured for calculating the predicted data value using a linear function defined by a data value of the time sequence of data values and the calculated rate of change.

11. Apparatus in accordance with claim 10, in which the processor is configured for calculating a data value defined by the linear function at a later time being a predetermined time period later than the data value defining the linear function, or

in which the processor is configured for calculating a later time value of an intersection of the linear function and the high or low alarm value and for outputting an indication, when the condition of the subject approaches an alarm state based on the later time value.

12. Apparatus in accordance with claim 5, in which the estimated probability function calculator is configured for updating the estimated probability function by averaging, for a plurality of data level regions, the rate of change values represented by the time sequence of data values to obtain an updated average rate of change for each data value region, and

in which a transform calculator is configured for calculating the non-linear transform rule using the updated average rates of change for the data value regions, wherein the same target probability function is used.

13. Apparatus in accordance with claim 12, in which the estimated probability function calculator is configured for calculating a plurality of k normal distributions for a plurality of k regions, where each region represents a value range, and wherein adjacent regions overlap each other, so that, for each region, a Gaussian normal probability distribution is estimated using values in the corresponding regions, and wherein the estimated probability function calculator is configured for calculating weights for each region, so that an integral over the sum of the k weighted normal probability distributions results in unity.

14. Apparatus in accordance with claim 1, further comprising a transform rule calculator being configured for calculating a time course of the certain characteristic, as an initial calculation or as a re-calculation in response to a certain re-calculation event,

by selecting from measurement data of an individual or a plurality of individuals portions comprising the characteristic,

by dividing a whole value range into a plurality of different regions,

by calculating, for each region, an average rate of change, and

by building a characteristic dynamic behavior curve from the average rate of change values for the regions, wherein the time course of the certain characteristic is based on the characteristic dynamic behavior curve.

15. Apparatus in accordance with claim 1,

in which the transformer comprises an estimated characteristic function calculator for calculating a time course of the certain characteristic and a target function calculator for calculating a target function as a linear function determined by two points of the time course of the certain characteristic;

and

a transform calculator for calculating the transform rule using the target function and the estimated characteristic function so that transformed data is closer to the target function than to the estimated characteristic function.

16. Apparatus in accordance with claim 1, wherein the certain characteristic is a fall or a rise of the physiological measure.

17. Apparatus in accordance with claim 16, in which the certain characteristic is a blood glucose rise or a blood glucose fall.

18. Method for generating a condition indication using a time sequence of data values, each data value representing a physiological measure of a condition of a subject at a time, comprising:

transforming the time sequence of data values into a transformed sequence of data values using a transform rule, wherein the transform rule is such that a certain characteristic in a time course of the physiological measure is represented by the transformed sequence of data values is more linear than the time course before the transform;

calculating an estimated rate of change for the transformed sequence of data values; and

processing the estimated rate of change to output the condition indication.

19. Computer program having a program code for performing, when running on a computer or processor, the method for generating a condition indication using a time sequence of data values, each data value representing a physiological measure of a condition of a subject at a time, the method comprising:

transforming the time sequence of data values into a transformed sequence of data values using a transform rule, wherein the transform rule is such that a certain characteristic in a time course of the physiological measure is represented by the transformed sequence of data values is more linear than the time course before the transform;

calculating an estimated rate of change for the transformed sequence of data values; and

processing the estimated rate of change to output the condition indication.