Methods and systems for processing glucose data measured from a person having diabetes
First Claim
1. A method for estimating a glucose level of a person having diabetes, the method comprising:
- receiving into a computing device a plurality of measured glucose results from a glucose sensor coupled to the person;
using the computing device to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy PA based on the plurality of measured glucose results;
using the computing device 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 probability of glucose sensor accuracy PA; and
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, anda 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; and
wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is
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Abstract
Methods and systems are disclosed for estimating a glucose level of a person having diabetes comprises. One method may comprise: receiving into a computing device a plurality of measured glucose results from a glucose sensor coupled to the person; using the computing device to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy based on the plurality of measured glucose results; and using the computing device to estimate a 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 probability of glucose sensor accuracy.
19 Citations
58 Claims
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1. A method for estimating a glucose level of a person having diabetes, the method comprising:
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receiving into a computing device a plurality of measured glucose results from a glucose sensor coupled to the person; using the computing device to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy PA based on the plurality of measured glucose results; using the computing device 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 probability of glucose sensor accuracy PA; and 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; and wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory computer-readable medium having a computer-executable instructions for performing a method for estimating a glucose level of a person having diabetes, the method comprising:
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receiving into a computer a plurality of measured glucose results from a glucose sensor coupled to the person; using the computer to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy PA based on the measured glucose results; using the computer 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 probability of glucose sensor accuracy PA; and using the computer to predict 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; 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; and
wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39)
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40. An apparatus for estimating a glucose level of a person having diabetes, the apparatus comprising a microcontroller, an input device, and a display, wherein:
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the microcontroller is electrically coupled to the input device configured to receive a plurality of measured glucose results from a glucose sensor coupled to the person, wherein the microcontroller is configured to receive the plurality of measured glucose results from the input device; the microcontroller is configured to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy PA based on the plurality of measured glucose results; the microcontroller is configured to estimate a 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 probability of glucose sensor accuracy PA ; and the microcontroller is electrically coupled to the display such that the microcontroller is configured to transmit to the display information related to the estimate of the glucose level of the person; and the microcontroller is further configured 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 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; and
wherein probability of the hidden Markov model transitioning from the first state SA to second the state SI is - View Dependent Claims (41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58)
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Specification