Blood glucose sensing system
A blood glucose sensing system includes a plurality of physiological sensors. The system can estimate blood glucose based on discrete invasive blood glucose estimates from a blood sample, discrete noninvasive blood glucose estimates derived from optical sensors, and continuously-calculated blood glucose estimates derived from a nonlinear state-space model of glucose and insulin reactions within a human body. The state-space model has user-entered values corresponding to their insulin and meal intake. The user's blood glucose is estimated from a combination of the discrete invasive blood glucose estimates, the discrete noninvasive blood glucose estimates and the continuously-calculated blood glucose estimate.
1. A blood glucose sensing system comprising:
a plurality of physiological sensors configured to provide sensor data associated with a user, the plurality of physiological sensors comprising an invasive sensor and an optical blood glucose sensor configured to generate blood glucose data of a user;
an input for receiving a plurality of blood glucose values derived from the invasive sensor data;
one or more hardware signal processors configured to:
receive blood glucose data from the optical blood glucose sensor;
receive a plurality of user-specific data, the user-specific data comprising food intake data and insulin intake data;
detect physiological events to generate a plurality of physiological event data based at least in part on the optical sensor data and independent of user input;
determine, using a glucose insulin estimator, a plurality of modeled blood glucose values over time between measurements by the blood glucose sensor, wherein the plurality of modeled blood glucose values are determined based on the blood glucose data and user-specific data, wherein the glucose insulin estimator comprises a nonlinear state-space model of glucose and insulin reactions within a human body and state variables associated with at least one of insulin secretion rate, plasma glucose, tissue glucose, insulin in interstitial fluid, insulin in liver, insulin in plasma, insulin in portal veins, delayed insulin signal, glucose in the stomach at solid phase, glucose in the stomach at liquid phase, glucose in the intestine, non-monomeric insulin in the subcutaneous space, monomeric insulin in the subcutaneous space, glucagon hormone, pro-glycogen, or glucagon rate affected endogenous factor, or parameters associated with at least one of glucose kinetics, insulin kinetics, rate of appearance of insulin, endogenous production of insulin, utilization of insulin, secretion of insulin, or renal excretion of insulin; and
dynamically optimize the nonlinear state-space model to minimize an error between the plurality of modeled blood glucose values of the user and measured values of blood glucose, wherein the measured values of blood glucose comprise blood glucose values derived from the invasive sensor data and blood glucose values derived from the optical sensor data,
wherein an input of the state-space model comprises the plurality of physiological event data of the user; and
determine a continuous estimate of blood glucose over a period of monitoring time based on a combination of the plurality of blood glucose values and the plurality of modeled blood glucose values; and
a display configured to display a glucose trend over time, the glucose trend based at least in part on the plurality of modeled blood glucose values.
2. The blood glucose sensing system of claim 1 , wherein the user-specific data further comprises one or more of biographical data and basal values.
3. The blood glucose sensing system of claim 1 , wherein user-specific data is manually inputted.
4. The blood glucose sensing system of claim 1 ,
wherein the one or more hardware signal processors are configured to generate a blood glucose estimate based at least in part on each of the plurality of modeled blood glucose values, a plurality of noninvasive sensor data and a plurality of invasive sensor data, and
wherein the one or more hardware signal processors are configured to recursively adjust parameters of the state-space model to minimize an error between the plurality of modeled blood glucose values of the user and measured values of blood glucose, the blood glucose estimate based at least in part on the state-space model having parameters resulting in minimal error between the plurality of modeled blood glucose values of the user and measured values of blood glucose.
5. The blood glucose sensing system of claim 4 , the one or more hardware processors configured to determine a continuous blood glucose estimate over a period of monitoring time.
6. The blood glucose sensing system of claim 4 , wherein the state-space model comprises:
an input vector comprising an insulin intake and food intake;
a state vector comprising the state variables;
a state equation comprising the parameters; and
the modeled blood glucose values.
7. The blood glucose sensing system of claim 6 , wherein the state equation comprises state variables associated with insulin secretion rate, plasma glucose, tissue glucose, insulin in interstitial fluid, insulin in liver, insulin in plasma, insulin in portal vein, delayed insulin signal, glucose in stomach at solid phase, glucose in stomach at liquid phase, glucose in intestine, non-monomeric insulin in subcutaneous space, monomeric insulin in subcutaneous space, glucagon hormone, pro-glycogen, and glucagon rate affected endogenous factor.
8. The blood glucose sensing system of claim 1 , wherein the one or more hardware signal processors is configured to detect physiological events to generate a plurality of physiological event data based at least in part on the sensor data and independent of user input.
9. A method for blood glucose monitoring, the method comprising:
generating, using an invasive blood glucose sensor and an optical blood glucose sensor, blood glucose data of a user;
receiving, using one or more hardware processors, the blood glucose data;
receiving, using the one or more hardware processors, a plurality of user-specific data, the user-specific data comprising food intake data and insulin intake data;
determining, using the one or more hardware processors implementing a glucose insulin model, a plurality of modeled blood glucose values over time between measurements by the blood glucose sensor, wherein the plurality of modeled blood glucose values are determined based on the blood glucose data and user-specific data, wherein the glucose insulin model is a nonlinear state-space model of glucose and insulin reactions within a human body, and wherein the glucose insulin model comprises:
state variables associated with at least one of insulin secretion rate, plasma glucose, tissue glucose, insulin in interstitial fluid, insulin in liver, insulin in plasma, insulin in portal vein, delayed insulin signal, glucose in stomach at solid phase, glucose in stomach at liquid phase, glucose in intestine, non-monomeric insulin in subcutaneous space, monomeric insulin in subcutaneous space, glucagon hormone, pro-glycogen, or glucagon rate affected endogenous factor, or
parameters associated with at least one of glucose kinetics, insulin kinetics, rate of appearance of insulin, endogenous production of insulin, utilization of insulin, secretion of insulin, or renal excretion of insulin;
dynamically optimizing the glucose insulin model to minimize an error between the plurality of modeled blood glucose values of the user and measured values of blood glucose based on the blood glucose data of the user; and
displaying a glucose trend over time, the glucose trend based at least in part on the plurality of modeled blood glucose values.
10. The method of claim 9 , wherein the user-specific data comprises one or more of biographical data and basal values.
11. The method of claim 9 , comprising inputting a plurality of user-specific data.
12. The method of claim 9 , comprising:
generating, using the one or more hardware processors, a blood glucose estimate based at least in part on each of the plurality of modeled blood glucose values, a plurality of noninvasive sensor data and a plurality of invasive sensor data,
wherein, using the one or more hardware processors, the blood glucose estimate is generated at least in part by recursively adjusting parameters of a state-space model to minimize an error between the plurality of modeled blood glucose values of the user and measured values of blood glucose.
13. The method of claim 12 , generating the blood glucose estimate comprising generating a continuous blood glucose estimate over a period of monitoring time.
14. The method of claim 12 , wherein the nonlinear state-space model comprises:
an input vector comprising an insulin intake and food intake;
a state vector comprising the state variables;
a state equation comprising the parameters; and
the modeled blood glucose values.
15. The method of claim 14 , wherein the nonlinear state-space model comprises state variables associated with insulin secretion rate, plasma glucose, tissue glucose, insulin in interstitial fluid, insulin in liver, insulin in plasma, insulin in portal vein, delayed insulin signal, glucose in stomach at solid phase, glucose in stomach at liquid phase, glucose in intestine, non-monomeric insulin in subcutaneous space, monomeric insulin in subcutaneous space, glucagon hormone, pro-glycogen, and glucagon rate affected endogenous factor.
16. The method of claim 9 , wherein the blood glucose sensor is a non-invasive blood glucose sensor.
17. The method of claim 16 , wherein the non-invasive blood glucose sensor is an optical sensor.
18. The method of claim 9 , comprising detecting physiological events to generate a plurality of physiological event data based at least in part on the sensor data and independent of user input.