Patent ID: 11937920
Assignee: MICROTECH MEDICAL (HANGZHOU) CO., LTD.
Field: Medical technology (Instruments)
Classification: CPC A  G  H | IPC A  G  H

Claim 4:
5. The intelligent real-time dynamic blood glucose monitoring system based on the cloud big data according to claim 1, wherein the cloud big data server calculates the CFcomp as follows:
the CFcomp is a parameter associated with a measured value of the electrochemical impedance data Z by a correlation Fcomp(Z;Ω):

CFcomp=Fcomp(Z;Ω)=Ωa×M(Z/Z0)+Ωb 

wherein, Ω is a built-in parameter array specific to the sensor, Ω
   =
   
    [
    
     
      
       
        Ω
        ⁢
        a
       
      
     
     
      
       
        Ω
        ⁢
        b
       
      
     
    
    ]
   
  
  ,
  
   M
   ⁡
   (
   
    Z
    /
    Z
    ⁢
    0
   
   )
  
 

is a function related to the electrochemical impedance data Z, and Z0 is the impedance value measured by the sensor in a controlled experimental environment;
when the user uses the finger blood glucose meter to detect the blood glucose value BG, the BG, the current data of the dynamic glucose sensor Is and the CFcomp conform to following relationship with a smallest possible error:, BG
     =
     
      Is
      ×
      
       CF
       ⁢
       comp
      
     
    
   
  
  
   
    
     =
     
      
       Is
       ×
       
        M
        ⁡
        (
        
         Z
         /
         Z
         ⁢
         0
        
        )
       
       ×
       Ω
       ⁢
       a
      
      +
      
       Is
       ×
       Ω
       ⁢
       b
      
     
    
   
  
 

the cloud big data server is configured to: step A, near each measurement time Tb of the finger blood glucose meter, screen out the implantable dynamic glucose sensor data having a time interval |Tb−Tz| less than the threshold value, and extract the following data:
1. BG: measured data of the finger blood glucose meter
2. SG: blood glucose calculation data of the implantable dynamic glucose sensor
3. Is: current data of the implantable dynamic glucose sensor
4. Z: Impedance data of the implantable dynamic glucose sensor
5. Tz: time stamp data of the implantable dynamic glucose sensor
select the data with |SG−BG| less than the threshold value as valid samples to form the sample record package [BGn ISn Zn Tzn] for calculation;
use the data in the last three months for a regression, and arranging the subscript number n of the historical data variable in reverse order of Tz, that is, the closer to the current historical data, the smaller the subscript number, the data of dynamic glucose sensor come from multiple sensor probes of the same model;
step B, establish a sample matrix and a weight matrix:, G
  =
  
   [
   
    
     
      
       BG
       1
      
     
    
    
     
      
       BG
       2
      
     
    
    
     
      
       BG
       3
      
     
    
    
     
      ⋮
     
    
    
     
      
       BG
       n
      
     
    
   
   ]
  
 

 
  I
  =
  
   [
   
    
     
      
       
        Is
        1
       
       ×
       
        M
        ⁡
        (
        
         
          z
          1
         
         /
         z
         ⁢
         0
        
        )
       
      
     
     
      
       Is
       1
      
     
    
    
     
      
       
        Is
        2
       
       ×
       M
       ⁢
       
        (
        
         
          z
          2
         
         /
         z
         ⁢
         0
        
        )
       
      
     
     
      
       Is
       2
      
     
    
    
     
      
       
        Is
        3
       
       ×
       M
       ⁢
       
        (
        
         
          z
          3
         
         /
         z
         ⁢
         0
        
        )
       
      
     
     
      
       Is
       3
      
     
    
    
     
      ⋮
     
     
      ⋮
     
    
    
     
      
       
        Is
        n
       
       ×
       M
       ⁢
       
        (
        
         
          z
          n
         
         /
         z
         ⁢
         0
        
        )
       
      
     
     
      
       Is
       n
      
     
    
   
   ]
  
 

 
  W
  =
  
   [
   
    
     
      
       w
       ⁡
       (
       
        Tz
        1
        ′
       
       )
      
     
     
       
     
     
       
     
     
       
     
     
       
     
    
    
     
       
     
     
      
       w
       ⁡
       (
       
        Tz
        2
        ′
       
       )
      
     
     
       
     
     
       
     
     
       
     
    
    
     
       
     
     
       
     
     
      
       w
       ⁡
       (
       
        Tz
        3
        ′
       
       )
      
     
     
       
     
     
       
     
    
    
     
       
     
     
       
     
     
       
     
     
      ⋱
     
     
       
     
    
    
     
       
     
     
       
     
     
       
     
     
       
     
     
      
       w
       ⁡
       (
       
        Tz
        n
        ′
       
       )
      
     
    
   
   ]
  
 

wherein, Tz′n=Tcurrent−Tzn 
the closer Tzn is to the current time, the greater its weight is;
step C, solve an overdetermined equation G=IΩ;
using a weighted least square method to solve a matrix equation {circumflex over (Ω)}=(ITWI)−1ITWG;
step D, eliminate abnormal data: calculating a residual matrix {circumflex over (ε)}=G−I{circumflex over (Ω)}, removing data item with residuals greater than the threshold value, and then repeating steps B to C to get an optimal solution {circumflex over (Ω)};
step E, use the optimal solution {circumflex over (Ω)} to correct the current set Ω with a predetermined correction ratio γ:

Ω:=(1−γ)×Ω+γ×{circumflex over (Ω)}

a range of γ values is 0<γ<1.