Pattern recognition engine for blood glucose measurements

1. A computer implemented blood glucose analysis method comprising:

receiving a plurality of blood glucose measurements relating to an individual taken over a plurality of temporal frames;

identifying hypoglycaemic and/or hyperglycaemic blood glucose measurements in the plurality of blood glucose measurements;

identifying a plurality of sub-intervals of the temporal frames in which there are a number of hypoglycaemic and/or hyperglycaemic blood glucose measurements that satisfies a threshold condition;

inferring a temporal relationship between blood glucose measurements in a first sub-interval of the identified sub-intervals and blood glucose measurements in a second sub-interval of the identified sub-intervals, wherein inferring the temporal relationships comprises:

determining a plurality of matching pairs of blood glucose measurements, wherein a matching pair comprises a blood glucose measurement in the first sub-interval and a blood glucose measurement in the second sub-interval and wherein determining matching pairs of blood glucose measurements comprises:

comparing time stamps associated with the blood glucose measurements in the first sub-interval to time stamps associated with the blood glucose measurements in the second sub-interval; and

determining a matching pair if a time stamp associated with a blood glucose measurement in the first sub-interval correspond to a time stamp associated with a blood glucose measurement in the second sub-interval,

determining if the total number of matching pairs exceeds a threshold number of pairs, wherein the threshold number of pairs is equal to or more than two; and

in the event of a positive determination, inferring a temporal relationship between the first sub-interval and the second sub-interval;

outputting the identified one or more sub-intervals of the temporal frames; and

outputting a suggested causal driver for the inferred temporal relationship, wherein the causal driver is selected from a database based on the inferred temporal relationship.

2. The method of claim 1 , wherein the plurality of temporal frames are consecutive.

3. The method of claim 1 , wherein the plurality of temporal frames each have a duration of a day and wherein the sub-interval is at most a three hour period.

4. The method of claim 1 , wherein the plurality of temporal frames each have a duration of a week and wherein the sub-interval is at most a day.

5. The method of claim 1 , further comprising plotting the blood glucose measurements from each of the plurality of temporal frames to generate a blood glucose measurement graph.

6. The method of claim 5 , wherein outputting the identified one or more sub-intervals of the temporal frames comprises outputting the blood glucose measurement graph, wherein the identified one or more sub-intervals are highlighted in the blood glucose measurement graph.

7. The method of claim 5 , wherein plotting the blood glucose measurements from each of the plurality of temporal frames comprises overlaying the blood glucose measurements from each of the plurality of temporal frames to generate a modal time period.

8. The method of claim 1 , wherein the threshold condition is a number and/or density of hypoglycaemic and/or hyperglycaemic blood glucose measurements within a sub-interval.

9. The method of claim 1 , further comprising:

determining a time lag between the first sub-interval and the second sub-interval; and

if the time lag is above a threshold time period, indicating that there is no temporal relationship between the first sub-interval and the second sub-interval.

10. The method of claim 1 , further comprising:

receiving additional data relating to the individual taken over the plurality of temporal frames; and

determining the suggested causal driver for the identified one or more sub-intervals of the temporal frames based at least in part on the additional data.

11. The method of claim 10 , wherein the additional data comprises one or more of: insulin dose data; data relating to exercise performed by the user; food intake data; and/or physiological measurements/data.

12. An apparatus comprising:

one or more processors; and

a memory, wherein the memory comprises computer readable instructions that, when executed by the one or more processors, cause the apparatus to:

receive a plurality of blood glucose measurements relating to an individual taken over a plurality of temporal frames;

identify hypoglycaemic and/or hyperglycaemic blood glucose measurements in the plurality of blood glucose measurements;

identify a plurality of sub-intervals of the temporal frames in which there are a number of hypoglycaemic and/or hyperglycaemic blood glucose measurements that satisfies a threshold condition;

infer a temporal relationship between blood glucose measurements in a first sub-interval of the identified sub-intervals and blood glucose measurements in a second sub-interval of the identified sub-intervals, wherein inferring the temporal relationships comprises:

determining matching pairs of blood glucose measurements, wherein a matching pair comprises a blood glucose measurement in the first sub-interval and a blood glucose measurement in the second sub-interval and wherein determining matching pairs of blood glucose measurements comprises:

comparing time stamps associated with the blood glucose measurements in the first sub-interval to time stamps associated with the blood glucose measurements in the second sub-interval; and

determining a matching pair if a time stamp associated with a blood glucose measurement in the first sub-interval correspond to a time stamp associated with a blood glucose measurement in the second sub-interval,

determining if the total number of matching pairs exceeds a threshold condition, wherein the threshold number of pairs is equal to or more than two; and

in the event of a positive determination, inferring a temporal relationship between the first sub-interval and the second sub-interval;

output the identified one or more sub-intervals of the temporal frames; and

output a suggested causal driver for the inferred temporal relationship, wherein the causal driver is selected from a database based on the inferred temporal relationship.

13. The apparatus of claim 12 , wherein the memory further comprises computer readable instructions that, when executed by the one or more processors, cause the apparatus to plot the blood glucose measurements from each of the plurality of temporal frames to generate a blood glucose measurement graph.

14. The apparatus of claim 12 , wherein the memory further comprises computer readable instructions that, when executed by the one or more processors, cause the apparatus to:

determine a time lag between the first sub-interval and the second sub-interval; and

if the time lag is above a threshold time period, indicate that there is no temporal relationship between the first sub-interval and the second sub-interval.

15. The apparatus of claim 12 , wherein the memory further comprises computer readable instructions that, when executed by the one or more processors, cause the apparatus to:

receive additional data relating to the individual taken over the plurality of temporal frames; and

determine the suggested causal driver for the identified one or more sub-intervals of the temporal frames based at least in part on the additional data.

16. The apparatus of claim 15 , wherein the additional data comprises one or more of: insulin dose data; data relating to exercise performed by the user; food intake data; and/or physiological measurements/data.

17. A non-transitory computer program product comprising computer readable code that, when executed by a computer, causes the computer to:

receive a plurality of blood glucose measurements relating to an individual taken over a plurality of temporal frames;

identify hypoglycaemic and/or hyperglycaemic blood glucose measurements in the plurality of blood glucose measurements;

identify a plurality of sub-intervals of the temporal frames in which there are a number of hypoglycaemic and/or hyperglycaemic blood glucose measurements that satisfies a threshold condition;

infer a temporal relationship between blood glucose measurements in a first sub-interval of the identified sub-intervals and blood glucose measurements in a second sub-interval of the identified sub-intervals, wherein inferring the temporal relationships comprises:

determining a plurality of matching pairs of blood glucose measurements, wherein a matching pair comprises a blood glucose measurement in the first sub-interval and a blood glucose measurement in the second sub-interval and wherein determining matching pairs of blood glucose measurements comprises:

comparing time stamps associated with the blood glucose measurements in the first sub-interval to time stamps associated with the blood glucose measurements in the second sub-interval; and

determining a matching pair if a time stamp associated with a blood glucose measurement in the first sub-interval correspond to a time stamp associated with a blood glucose measurement in the second sub-interval,

determining if the total number of matching pairs exceeds a threshold number of pairs, wherein the threshold number of pairs is equal to or more than two; and

in the event of a positive determination, inferring a temporal relationship between the first sub-interval and the second sub-interval;

output the identified one or more sub-intervals of the temporal frames; and

output a suggested causal driver for the inferred temporal relationship, wherein the causal driver is selected from a database based on the inferred temporal relationship.