Health Monitoring System

Abstract

A machine for processing continuous glucose monitoring data and issuing an alert if hypoglycemia is imminent has three modules: (a) a pre-processing module that receives and modulates continuous glucose monitoring data by reducing noise and adjusting for missed data points and shifts due to calibration; (b) a core algorithm module that receives data from the pre-processing module and calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and (c) an alarm mode module that receives data from the core algorithm and issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device if hypoglycemia is imminent.

1 . A low glucose prediction signal generator that uses a set of constraints to predict an imminent occurrence of hypoglycemia, the generator comprising:
 (a) a pre-processing module that receives and modulates continuous glucose monitoring (CGM) data by reducing noise and adjusting for missed data points and shifts due to calibration;   (b) a core algorithm module that receives data from the pre-processing module and calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and   (c) an alarm mode module that receives data from the core algorithm and if hypoglycemia is imminent, issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device.   
     
     
         2 . The signal generator of  claim 1  wherein the preprocessing module the CGM data are filtered for noise using a noise spike filter to remove outliers and a low pass filter to damp electrical noise; to use most current information, recently missed data points are interpolated using a simple linear interpolation; to prevent erroneous estimation of the rate of change when the sensor is calibrated, a calibration detection module is used to detect a persistent offset in data and shifts the data from before the calibration; wherein the preprocessing module only operates when enough data is present to make a prediction and will operate during periods of sensor outage, up to two readings, by extrapolating previous estimates. 
     
     
         3 . The signal generator of  claim 1  wherein the core algorithm module the rate of change is estimated using the first derivative of the 3-point Lagrange interpolation polynomial, wherein a series of logical steps is taken to ensure that the subject is within a determined proximity of the hypoglycemia threshold, the glucose is decreasing at a physiologically probable rate, and that the time to crossing the hypoglycemia threshold is within a preset prediction horizon, and wherein if these checkpoints are all passed, the alarm mode module is activated. 
     
     
         4 . The signal generator of  claim 1  wherein the alarm mode module, when an imminent hypoglycemic event is predicted in the core algorithm module, the alarm mode references any previous alarms to ensure that it has been more than a pre-designated lockout period to ensure that any action taken during the previous alarm has time to take effect, wherein if this checkpoint is passed, an audible, electronic or visible alarm is issued, or a feedback signal is issued that results in insulin delivery suspension, insulin delivery attenuation, or consumption of rescue carbohydrates. 
     
     
         5 . The signal generator of  claim 1  wherein:
 (a) the preprocessing module the CGM data are filtered for noise using a noise spike filter to remove outliers and a low pass filter to damp electrical noise; to use most current information, recently missed data points are interpolated using a simple linear interpolation; to prevent erroneous estimation of the rate of change when the sensor is calibrated, a calibration detection module is used to detect a persistent offset in data and shifts the data from before the calibration; wherein the preprocessing module only operates when enough data is present to make a prediction and will operate during periods of sensor outage, up to two readings, by extrapolating previous estimates; 
 (b) the core algorithm module the rate of change is estimated using the first derivative of the 3-point Lagrange interpolation polynomial, wherein a series of logical steps is taken to ensure that the subject is within a determined proximity of the hypoglycemia threshold, the glucose is decreasing at a physiologically probable rate, and that the time to crossing the hypoglycemia threshold is within a preset prediction horizon, and wherein if these checkpoints are all passed, the alarm mode module is activated; and 
 (c) the alarm mode module, when an imminent hypoglycemic event is predicted in the core algorithm module, the alarm mode references any previous alarms to ensure that it has been more than a pre-designated lockout period to ensure that any action taken during the previous alarm has time to take effect, wherein if this checkpoint is passed, an audible, electronic or visible alarm is issued, or a feedback signal is issued that results in insulin delivery suspension, insulin delivery attenuation, or consumption of rescue carbohydrates. 
 
     
     
         6 . The signal generator of  claim 1  wherein the preprocessing module implements the steps of  FIGS. 1-1  and  1 - 2 . 
     
     
         7 . The signal generator of  claim 1  wherein the core algorithm module implements the steps of  FIG. 2 . 
     
     
         8 . The signal generator of  claim 1  wherein the alarm mode module implements the steps of  FIG. 3-3 . 
     
     
         9 . The signal generator of  claim 1  wherein the preprocessing module implements the steps of  FIGS. 1-1  and  1 - 2 , the core algorithm module implements the steps of  FIG. 2-1 , and the alarm mode module implements the steps of  FIG. 3-3 . 
     
     
         10 . A machine for processing continuous glucose monitoring (CGM) data and issuing an alert if hypoglycemia is imminent, the machine comprising a computer specifically programmed with:
 (a) a pre-processing module that receives and modulates continuous glucose monitoring (CGM) data by reducing noise and adjusting for missed data points and shifts due to calibration;   (b) a core algorithm module that receives data from the pre-processing module and calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and   (c) an alarm mode module that receives data from the core algorithm and issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device if hypoglycemia is imminent.   
     
     
         11 . The machine of  claim 10  operably-linked to the insulin delivery device. 
     
     
         12 . The machine of  claim 10 , operably-linked to a continuous glucose monitoring (CGM) device. 
     
     
         13 . The machine of  claim 10  operably-linked to a integrated continuous glucose monitoring (CGM) and insulin delivery device. 
     
     
         14 . A method of using a machine of  claim 10  for processing continuous glucose monitoring (CGM) data and issuing an alert if hypoglycemia is imminent, the method comprising the steps of:
 (a) receiving and modulating CGM data in a pre-processing module by reducing noise and adjusting for missed data points and shifts due to calibration; 
 (b) receiving data from the pre-processing module in a core algorithm module that then calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and 
 (c) receiving data from the core algorithm in an alarm mode module that then issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device if hypoglycemia is imminent. 
 
     
     
         15 . A low glucose predictor (LPG) core algorithm comprising a numerical logical algorithm that feeds a three-point calculated rate of change using backward difference approximation and the current glucose value into logical expressions to detect impending hypoglycemia, wherein the logical expressions verify that the rate of change is both negative and within a predetermined acceptable range as well as that the continuous glucose monitoring (CGM) glucose values are within predefined boundaries and that a pending hypoglycemic event is predicted within the threshold time window, and wherein the numerical logical algorithm provides tuning and insensitivity to sensor signal dropouts.