Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors
Method, system and computer program product for providing real time detection of analyte sensor sensitivity decline is continuous glucose monitoring systems are provided.
1. A computer implemented method, comprising:
receiving a set of analyte sensor data taken over a first time period after initialization of a sensor;
performing a sliding window analysis on the set of analyte sensor data, wherein performing the sliding window analysis comprises extracting a first sensor data characteristic for a first window of the set of analyte sensor data, wherein the first window starts at a first start time, ends at a first end time, and has a first duration less than the first time period;
determining a probability of existence of signal attenuation associated with a decline in analyte sensor response based on the first sensor data characteristics; and
comparing the determined probability of existence of signal attenuation to a predetermined threshold value.
2. The computer implemented method of claim 1 , further comprising the steps of:
extracting a second sensor data characteristic for a second window of the set of analyte sensor data, wherein the second window starts at a second start time after the first start time, ends at a second end time after the first end time, and has a second duration less than the first time period;
determining a second probability of existence of signal attenuation associated with a decline in analyte sensor response based on the first and second sensor data characteristics; and
comparing the determined second probability of existence of signal attenuation to a predetermined threshold value.
3. The computer implemented method of claim 1 , wherein the first sensor data characteristic is a mean value or a variance value.
4. The computer implemented method of claim 1 , wherein the first sensor data characteristic is an average slope of a sensor signal.
5. The computer implemented method of claim 1 , wherein the first sensor data characteristic is an average sensor life or a time elapsed since insertion of the sensor.
6. The computer implemented method of claim 1 , wherein the first time period includes a time in the first twelve to twenty four hours after insertion of the sensor.
7. The computer implemented method of claim 1 , wherein the analyte sensor data is glucose sensor data.
8. The computer implemented method of claim 1 , wherein the analyte sensor data is lactate sensor data.
9. The computer implemented method of claim 1 , wherein the analyte sensor data is a ketone sensor data.
10. The computer implemented method of claim 1 , further comprising the step of computing an estimated decline in analyte sensor response.
11. An apparatus, comprising:
a data storage unit; and
a processing unit operatively coupled to the data storage unit, the processing unit programmed to:
perform a sliding window analysis on a set of analyte sensor data, wherein the set of analyte sensor data is taken over a first time period after initialization of a sensor, and wherein in performance of the sliding window analysis, the processing unit is programmed to extract a first sensor data characteristic for a first window of the set of analyte sensor data, wherein the first window starts at a first start time, ends at a first end time, and has a first duration less than the first time period;
determine a probability of existence of signal attenuation associated with a decline in analyte sensor response based on the first sensor data characteristics; and
compare the determined probability of existence of signal attenuation to a predetermined threshold value.
12. The apparatus of claim 11 , wherein the processing unit is further programmed to:
extract a second sensor data characteristic for a second window of the set of analyte sensor data, wherein the second window starts at a second start time after the first start time, ends at a second end time after the first end time, and has a second duration less than the first time period;
determine a second probability of existence of signal attenuation associated with a decline in analyte sensor response based on the first and second sensor data characteristics; and
compare the determined second probability of existence of signal attenuation to a predetermined threshold value.
13. The apparatus of claim 11 , wherein the first sensor data characteristic is a mean value or a variance value.
14. The apparatus of claim 11 , wherein the first sensor data characteristic is an average slope of a sensor signal.
15. The apparatus of claim 11 , wherein the first sensor data characteristic is an average sensor life or a time elapsed since insertion of the sensor.
16. The apparatus of claim 11 , wherein the first time period includes a time in the first twelve to twenty four hours after insertion of the sensor.
17. The apparatus of claim 11 , wherein the analyte sensor data is glucose sensor data.
18. The apparatus of claim 11 , wherein the analyte sensor data is lactate sensor data.
19. The apparatus of claim 11 , wherein the analyte sensor data is a ketone sensor data.
20. The apparatus of claim 11 , wherein the processing unit is further programmed to compute an estimated decline in analyte sensor response.