CN102500013A - Fully automatic intelligent infusion method and device based on model predictive control for large doses of insulin - Google Patents

Abstract

The invention provides an intelligent method based on model predictive control for automatic infusion of large doses of insulin, which is characterized in that: a continuous glucose monitoring system (CGMS) and an insulin pump are used as the basis of hardware; online detection of food is carried out based on a strong tracking filter; when the food is detected, large doses of insulin is transfused immediately, and the amount of insulin depends on historical food; whether the dosage is needed to be increased is judged every 30 minutes based on the model predictive control, and the dosage is designed; when the blood glucose concentration decreases, whether the basic amount of insulin is needed to be maintained is determined according to the predicted blood glucose value; and finally the blood glucose concentration is controlled to be within the safe range. Compared with the existing corresponding technology, the invention has the advantages of high degree of intelligence and fully automatic operation, and the blood glucose control effects can be improved significantly in case that self-management cannot be carried out by patients.

Description

Bolus insulin fully-automatic intelligent infusion methods and device based on Model Predictive Control

Technical field

What the present invention relates to is a kind of intelligent method of automatic design bolus insulin, and particularly a kind of Intelligentized design method based on Model Predictive Control belongs to biomedical engineering field.

Background technology

Along with the raising of living standard and the deterioration of environment, diabetes become the important diseases of serious harm human health day by day.That diabetes become is blind, the main cause of renal failure and LEA, also is that cardiovascular complication causes dead principal element.Diabetes are human the fourth-largest causes of the death, and the death toll that diabetes cause accounts for 6.8% of total death toll, have every year nearly 4,000,000 people to die from diabetes and complication thereof.The health care expense in the annual whole world 11.6% is used to treat diabetes and complication thereof.

The main cause that develops complications is the hyperglycemia (blood sugar concentration is higher than 10mmol/L (every liter of mM)) that diabetes cause.For blood sugar lowering concentration, IDDM patient and the treatment of part type 2 diabetes mellitus needs of patients exogenous insulin.At present, state-of-the-art therapy is to wear insulin pump, one day 24 hours real-time infusions.

In order to simulate the insulin secretion rule of healthy human body, present insulin pump infusion therapy comprises two kinds of patterns: bolus insulin and basis amount insulin.Basis amount insulin is used for compensating the blood glucose that human body self produces.Bolus insulin mainly is used for compensating the influence of diet and proofreaies and correct hyperglycemia.Because diet is difficult to measured in real time, the infusion bolus insulin needs the participation of user, i.e. the self management of user: user front and back on the feed need input to insulin pump with the moment and the size of diet.The diet size here is meant the content of carbohydrate in the diet.

In order accurately to estimate the content of the carbohydrate in the diet, diabetics need be accepted strictness and the food nutrition knowledge education of system.Nonetheless, the estimated bias of diet size also is difficult to avoid.In order to overcome above-mentioned defective, many intelligent insulin pumps have all been introduced the diet data base.The patient only need tell insulin pump, has eaten what and weight separately.Insulin pump will call the diet data base, calculates the content of carbohydrate automatically.These advanced intelligent insulin pumps have partly alleviated patient's use burden.

Yet the heavy dose of all at present insulin pumps all needs patient's participation design, and this has brought very big inconvenience for they and household's thereof life.Particularly a lot of IDDM patients are child and teenager, and it is unpractical often to input to insulin pump to diet information before letting they are each and having meal.

Summary of the invention

The present invention is directed to the deficiency and the defective of prior art, a kind of heavy dose of method of fully-automatic intelligent design based on Model Predictive Control is provided.An important hardware foundation of the present invention is dynamic blood glucose monitoring system (CGMS or CGM), and the increasingly mature of it makes real-time measuring blood concentration become possibility.Based on real-time metrical information and feedback control principle, the application has designed a kind of fully-automatic intelligent algorithm and has come the time and the size of design bolus insulin automatically.

The hardware configuration sketch relevant with the present invention is as shown in Figure 1.The patient wear insulin pump, interior insulin-containing, and at the control injected insulin of pump line reason system (bolus insulin fully-automatic intelligent algorithm).The patient also wears CGMS, monitors patient's blood sugar concentration in real time.System can also comprise a hand-held pump manager, and the patient carries out the control and the setting of system through it.

The present invention realizes through following technical scheme: at first, CGMS offers current blood glucose value the chip at algorithm for design place and deposits memorizer in; Secondly, utilize strong tracking filter to estimate the rate of change of blood sugar concentration in real time, when it surpasses a certain threshold value, detect diet, and conservative bolus insulin of infusion; Then, start Model Predictive Control Algorithm, whether per decision-making half an hour once appends bolus insulin; At last, after blood sugar concentration gets into decline passway, stop the infusion bolus insulin, judge whether to exist risk of hypoglycemia, thereby whether decision closes basis amount insulin based on Model Predictive Control Algorithm.Final expectation remains on blood sugar level in the safe scope.

The present invention mainly has the diet detection, heavy dose designs, suspends three functions of basis amount automatically.Wherein, diet detects to be used for judging whether the patient takes food, if feed is arranged, just infusion is initial heavy dose of and start Model Predictive Control Algorithm at once, judges whether append heavy dose per half an hour; Heavy dose of automatic design comprises two parts: initial heavy dose of with append heavy dose, be used for compensating the influence of diet; Suspending the basis amount is in order to prevent hypoglycemic generation.

Description of drawings

Fig. 1 is a bolus insulin fully-automatic intelligent infusion device sketch map according to the invention;

Fig. 2 is the strong tracking filter algorithm flow chart of estimating the blood glucose change rate of concentration in the bolus insulin fully-automatic intelligent infusion methods of stating according to the invention;

Fig. 3 is the bolus insulin fully-automatic intelligent infusion methods flow chart of steps of stating according to the invention;

Fig. 4 is a bolus insulin fully-automatic intelligent infusion device system construction drawing according to the invention.

The specific embodiment

Below in conjunction with accompanying drawing and specific embodiment the present invention is described further, but not as to qualification of the present invention.

(1) diet detection algorithm.

Note k blood glucose value constantly is G (k), the change of blood sugar rate be G ' (k), suppose that the change of blood sugar rate is more stable, then can obtain following second-order linearity dynamic model:

$\left\{\begin{array}{c}\left[\begin{array}{c}G(k+1)\\ G^{\′}(k+1)\end{array}\right]=\left[\begin{array}{cc}1& \mathrm{\Δt}\\ 0& 1\end{array}\right]\left[\begin{array}{c}G\left(k\right)\\ G^{\′}\left(k\right)\end{array}\right]+w\left(k\right)\\ y\left(k\right)=\left[\begin{array}{cc}1& 0\end{array}\right]\left[\begin{array}{c}G\left(k\right)\\ G^{\′}\left(k\right)\end{array}\right]+v\left(k\right)\end{array}\right.---\left(1\right)$

Wherein, Δ t is the sampling period, is generally 1 minute or 5 minutes, and is known; W (k) is modeling uncertainty or external disturbance, the unknown; V (k) is for measuring noise, the unknown; Y (k) is the output valve of CGMS, and is known; G (k) represents real blood glucose concentration value, the unknown, but y (k) is its measured value, so both are very approaching; G ' (k) represents the change of blood sugar rate, the unknown, and can not survey.Based on above-mentioned dynamic model and real-time measurement values y (k); Can estimate blood glucose rate of change G ' (k) with strong tracking filter, the strong tracking filter of its estimated value note work

has following advantage: 1) model uncertainty is had stronger robustness; 2) mutation status there is stronger trace ability, even when reaching poised state, still keeps trace ability soft phase and mutation status in system.Therefore, strong tracking filter is the effective tool that detects diet, is particularly suitable for detecting the change of blood sugar rate.

Below in conjunction with accompanying drawing specifically to how estimating that G ' (k) is described further.

It is strong that the tracking filter algorithm is estimated

1) order $X\left(k\right)=\left[\begin{array}{c}G\left(k\right)\\ G^{\′}\left(k\right)\end{array}\right],$ $F\left(k\right)=\left[\begin{array}{cc}1& \mathrm{\Δ\; t}\\ 0& 1\end{array}\right],$ H (k)=[1 0]

2) make k=0; (P (k|k) is an estimate variance to select initial value P (0|0); P (k+1|k) is the prediction variance); Select a suitable reduction factor-beta.

3) by formula

$\hat{X}(k+1|k)=F\left(k\right)\hat{X}\left(k\right|k),\mathrm{\γ}(k+1)=y(k+1)-H\left(k\right)\hat{X}(k+1|k)$

Calculate

By formula

$S_0(k+1)=\left\{\begin{array}{cc}\mathrm{\γ}\left(1\right){\mathrm{\γ}}^T\left(1\right),& k=0\\ \frac{[\mathrm{\ρ}{S}_0\left(k\right)+\mathrm{\γ}(k+1){\mathrm{\γ}}^T(k+1)]}{1+\mathrm{\ρ}},& k\≥1\end{array}\right.$

Calculate S ₀(k+1),

By formula

N(k+1)＝S ₀(k+1)-H(k)Q(k)H ^T(k)-βR(k+1)，

M(k+1)＝H(k)F(k)P(k|k)F ^T(k)H ^T(k)，

${\mathrm{\λ}}_0=\frac{\mathrm{tr}\left[N(k+1)\right]}{\mathrm{tr}\left[M(k+1)\right]},,$

$\mathrm{\λ}(k+1)=\left\{\begin{array}{cc}{\mathrm{\λ}}_0,& {\mathrm{\λ}}_0\≥1\\ 1,& {\mathrm{\λ}}_0\≤1\end{array}\right.$

Calculate the suboptimum factor lambda (k+1) that fades.

4) according to formula

P(k+1|k)＝λ(k+1)F(k)P(k|k)F ^T(k)+Q(k)，

Calculate P (k+1|k);

By formula

K(k+1)＝P(k+1|k)H ^T(k)·[H(k)P(k+1|k)H ^T(k)+R(k+1)] ^-1

Calculate K (k+1);

Finally obtain by

$\hat{X}(k+1|k+1)=\hat{X}(k+1|k)+K(k+1)\mathrm{\γ}(k+1)$

State estimation value

5) upgrade P (k+1|k+1)

P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k)

6) k+1 → k turns to 3), continue circulation.

The detail flowchart of above-mentioned algorithm is seen Fig. 2.Through above-mentioned algorithm, can obtain the estimated value

of X (k) and then obtain G ' estimated value

(k)

Under the state, G ' value (k) is less on an empty stomach, and after the feed, G ' (k) will become very big.Therefore, system can design threshold value η＞0.During as

, detect diet.The value of threshold value η varies with each individual, and needs to carry out statistical analysis to different patients' historical data, finds best threshold value (usually, threshold value is between 1.5-3mg/dL/min).

(2) heavy dose designs automatically

The heavy dose of this part comprises two parts: detect after the diet heavy dose of infusion at once; After starting Model Predictive Control Algorithm, judge whether to append heavy dose per half an hour.

When first heavy dose of design,, therefore, can utilize concrete patient's historical diet information to design a conservative heavy dose because system is unknown to the diet size.For example, this patient's historical average diet size is M, can design heavy dose according to M/2 or M/3, thereby improves the safety of system.For further the safety of raising system can also increase a judging unit: if blood sugar level is higher than certain threshold value (for example 140mg/dL), Cai the infusion bolus insulin.

After detecting diet, Model Predictive Control Algorithm will be activated.The PREDICTIVE CONTROL that uses a model just needs a forecast model, and the present invention adopts as follows the forecast model based on discrete transfer function:

$G\left(k\right)=\frac{K_I{z}^{-d_I}}{1+a_I{z}^{-1}+b_I{z}^{-2}}I\left(k\right)+\frac{K_M{z}^{{-d}_M}}{1+a_M{z}^{-1}+b_M{z}^{-2}}M\left(k\right)---\left(2\right)$

Wherein, G (k), I (k) and M (k) represent k blood sugar concentration, infusion of insulin speed and diet size constantly respectively; z ^-1Be backward shift operator, for example z ^-3G (k)=G (k-3) is the back transposition of three steps.COEFFICIENT K _I, d _I, a _I, b _I, K _M, d _M, a _M, b _MObtain by the open loop identification, therefore known.K _IReflected the influence size of infusion of insulin speed to blood sugar level; d _IRepresent the onset time size of insulin; a _IReflected the influence degree of a last step blood sugar concentration G (k-1) to current blood sugar concentration G (k); b _IReflected the influence degree of G (k-2) to G (k); K _M, d _M, a _M, b _MPhysical significance similar.We know by formula (2), and the blood sugar concentration of current time can be predicted by infusion of insulin speed, diet size, the blood sugar concentration in a last step and the blood sugar concentration in last two steps.

In the said system, I (k) and G (k) can measure in real time, and diet size M (k) is difficult to measured in real time, therefore can be counted as a unknown input.Yet feed was accomplished in 10 minutes usually; Sampling period is elected 5 minutes usually as, d _MFor diet influences the hysteresis step-length of blood sugar concentration, be generally 2, promptly be 2*5=10 minute lag time.Therefore, general after 10+2*5=20 minute, diet just can directly not influence the blood glucose predictive value.In other words, based on forecast model (2), the blood glucose predictive value after 20 minutes does not rely on the diet size, so the present invention just can avoid falling the unknown difficult point of diet size, the accurately automatic prediction of realization.

Select following evaluation index (cost function), evaluation index provides the standard of judging that a control strategy is good and bad:

$\mathrm{\Ω}\widehat{=}\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\{{[\hat{G}(k+d_I+i|k)-G_R(k+d_I+i)]}^2+\mathrm{\λ}{\left[\mathrm{\Δ}\hat{I}(k+i|k)\right]}^2\}---\left(3\right)$

Wherein, N is a prediction step, Be predictive value in the k moment to j blood sugar level constantly, G _RBe the blood glucose setting value,

It is the difference value of infusion of insulin speed in the future of design.Above-mentioned evaluation index comprises two parts in fact: the gap between following blood sugar level and the setting value has been reflected in first; Second portion has reflected the intensity of variation of infusion of insulin speed.Parameter lambda is used for adjusting weight between the two, is designed and is defined by system.

Optimum infusion of insulin velocity series need make evaluation index minimum, that is, make blood glucose in following a period of time follow the tracks of square sum minimum of residual error square and the rate of change of infusion of insulin speed:

$\mathrm{\Δ}{\hat{I}}^{*}{(k+i|k)}_{i=0,L,N-1}=\underset{\mathrm{\Δ}\hat{I}}{\arg \min }\mathrm{\Ω}---\left(4\right)$

After obtaining

, be easy to obtain optimum infusion of insulin velocity series Here it is the control strategy of the optimum under evaluation index Ω.Suppose the blood glucose predictive value At moment k+d _I+ j reaches maximum.So, bolus insulin can be designed as:

$B\left(k\right)=\underset{i=0}{\overset{j}{\mathrm{\Σ}}}{\hat{I}}^{*}(k+i|k)---\left(5\right)$

(3) suspend the basis amount

After blood glucose gets into decline passway, will stop the infusion bolus insulin, this is for fear of the hypoglycemia incident.Because the harm of the short-term of hypoglycemia incident is very big, can cause apoplexy even death.

In order further to reduce hypoglycemic probability of happening, need on-line prediction hypoglycemia incident, in time close basis amount insulin.Making the I (k) in the formula (2) is zero for basis amount, M (k), and the blood sugar concentration that can predict future if the blood glucose predictive value behind the prediction duration (being generally 30-60 minute) is lower than certain hypoglycemia threshold value (like 70mg/dL), was just closed the basis amount 60-90 minute.

The present invention has substantive distinguishing features and marked improvement.Bolus insulin automatic design method based on Model Predictive Control is on the basis of well behaved strong tracking filter technology, model predictive control technique, Automatic Measurement Technique etc., to research and develop.The detail flowchart of this inventive method is seen Fig. 3.

Algorithm among Fig. 3 is described below:

Heavy dose of algorithm for design automatically based on Model Predictive Control:

1) system initialization;

2) receive a new CGMS reading after, deposit memorizer in and import strong tracking filter and calculate up-to-date change of blood sugar rate;

3) if the change of blood sugar rate is lower than threshold value (being generally between the 1.5-3mg/dL/min), waits for next CGMS reading, and get back to step 2); If surpass threshold value, get into next step;

4) the conservative initial bolus insulin of injection;

5) per 30 minutes, based on bolus insulin of Model Predictive Control Algorithm design;

6) whether the real-time judge blood sugar concentration begins to descend: not, then get back to step 5); Be then to get into next step;

7) whether real-time judge will have risk of hypoglycemia future: deny repeating step 7); Be then to suspend basis amount insulin;

8) after blood glucose gets into safety range, finish said process.

Compare with existing open loop infusion of insulin method, this invention has high intelligence, full automatic characteristics, can under the situation that lacks patient self management, significantly improve the glycemic control effect.

Fig. 4 is a preferred embodiment of the present invention hardware block diagram, combines Fig. 4 further to introduce in detail at present.

On patient body, be provided with insulin pump 41, include the insulin of doses, can under the control that designs the bolus insulin system automatically, accurately measure injection.Patient body also is provided with the dynamic blood glucose monitoring system 42 of CGMS, is used for measuring the diabetics blood sugar concentration in real time, and sends the data to the controller of automatic design bolus insulin system.

The external parts of patient are automatic design bolus insulin system control module, comprise with lower component: communication module 43, memorizer 44, controller 45,, user's input module 46, display module 47, supply module (in Fig. 4, not showing).Above-mentioned external control module is designed to be easy to carry like the mobile phone size, and is easy to operate.Wherein, communication module 43 is used for the transfer of data between insulin pump 41, the dynamic blood glucose monitoring system 42 of CGMS and the controller 45, and controller 45 sends instruction and returns to controller 45 for insulin pump 41, insulin pump 41 transmission injection of insulin data; CGMS42 gives controller 45 through its real-time transmission data.Memorizer 44 is used for the storage system routine data, also has patient data such as patient's historical dietary amount data, historical blood sugar concentration.Through user's input module 46, the patient can carry out hand control to insulin injection amount to system's input control information.Display module 47 is used for showing to the patient data such as injection of insulin information, blood sugar concentration information, the too high or too low warning message of blood glucose.Controller 45 control whole systems; Estimate the blood glucose rate of change according to the CGMS reading; Judge that the patient has the initial bolus insulin of feed back input; Whenever at a distance from bolus insulin of injection in 30 minutes, get into safety range up to blood glucose value, it comprises diet monitoring modular 451, blood glucose prediction module 452 and heavy dose of design module 453 again.Diet monitoring modular 451 is used for judging according to patient's change of blood sugar rate whether the patient has feed; Blood glucose prediction module 452 is used to predict Future Time patient's blood glucose concentration value; Following blood sugar level that heavy dose of design module 453 is used for calculating according to blood glucose prediction module 452 and the gap between the setting value, and the intensity of variation of infusion of insulin speed are calculated the bolus insulin quantity that whenever should inject at a distance from 30 minutes.

In addition,, on MATLAB, developed the diabetes emulation platform, contained 100 virtual patients in this emulation platform based on standard diabetes metabolism model.The inventive method is all being tested on the virtual patient.

Emulation testing begins from 0:00, and all the initial blood sugar level of virtual patient is elected 110mg/dL as; At 1:00, all patient's feeds, difference to some extent is distributed between the 45-85 gram content of carbohydrate according to patient body weight is different in the diet; In 10-25 minute, strong tracking filter has detected diet, and infusion initial heavy dose; Start Model Predictive Control design then and append heavy dose, append heavy dose of number and be 0-3 time and do not wait; Blood glucose stops heavy dose after getting into decline passway, and predicts the outcome according to blood glucose and to determine whether suspending the basis amount; L-G simulation test finishes at 12:00.In whole test, average blood sugar concentration is about 140mg/dL, hypoglycemic percentage of time takes place be lower than 1%.

The inventive method is through emulation testing, and effect is more satisfactory.The introducing of intelligent algorithm makes that the glycemic control effect has very big change under the situation of self management disappearance.In addition, because this method is simple, amount of calculation is lower, can be embedded at an easy rate in the present insulin pump, improves its effect.

Above-described embodiment is the more preferably specific embodiment of the present invention, and common variation that those skilled in the art carries out in technical scheme scope of the present invention and replacement all should be included in protection scope of the present invention.

Claims (9)

1. bolus insulin design and infusion methods based on a Model Predictive Control is characterized in that this method comprises the steps:

(1) blood glucose concentration value of utilizing dynamic blood glucose monitoring system (CGMS) to export is estimated the blood glucose rate of change, according to the change of blood sugar rate, judges whether the patient has feed;

(2) if judging the patient takes food really, the initial bolus insulin of infusion at once then, and carry out following blood glucose level prediction, per half an hour, judge once whether append bolus insulin;

Whether begin descend: if do not descend, then return step (2) if (3) monitoring blood sugar concentration in real time; If begin to descend, then judge future whether risk of hypoglycemia will be arranged, if risk of hypoglycemia is arranged, then suspend the basal insulin injection.

(4) after blood glucose gets into safety range, stop insulin injection.

2. bolus insulin method for designing according to claim 1; It is characterized in that: in the step (1); Use second-order linearity dynamic model and strong tracking filter to estimate the blood glucose rate of change, whether detect greater than certain threshold value whether feed is arranged according to the change of blood sugar rate.

3. bolus insulin method for designing according to claim 2; It is characterized in that: the change of blood sugar rate threshold value that judges whether feed obtains according to patient's historical data statistical analysis, can select usually between the 1.5-3mg/dL/min (the every Liter Per Minute of milligram).

4. bolus insulin method for designing according to claim 1 is characterized in that: in the step (2), in the moment that diet just has been detected, initial heavy dose is confirmed by the historical average diet size of patient; In order to improve safety, utilize 1/3 to 1/2 of historical average diet size to calculate initial heavy dose.

5. bolus insulin method for designing according to claim 1 is characterized in that: in the step (2), predict following blood sugar level by the single prediction of output model of two inputs based on discrete transfer function.

6. injection of insulin method according to claim 5 is characterized in that: append heavy dose and designed by model predictive control method, determined by following two factors: the gap between following blood sugar level and the setting value; And the intensity of variation of infusion of insulin speed.

7. bolus insulin method for designing according to claim 1 is characterized in that: in the step (3), blood glucose based on the blood glucose predictive value, judges whether to suspend insulin injection after getting into decline passway; Prediction step is elected 30-60 minute as; Suspending duration is 60-90 minute.

8. the bolus insulin based on Model Predictive Control designs and infusion device, it is characterized in that:

On patient body, be provided with:

Insulin pump 41 includes the insulin of doses, can under the control of design bolus insulin system automatically, accurately measure injection;

With the dynamic blood glucose monitoring system 42 of CGMS, be used for monitoring the diabetics blood sugar concentration in real time, and send the data to the controller of automatic design bolus insulin system;

The patient is external to be provided with automatic design bolus insulin system control module, comprises with lower component:

Communication module 43 is used for the transfer of data between insulin pump 41, the dynamic blood glucose monitoring system 42 of CGMS and the controller 45,

Memorizer 44 is used for the storage system routine data, also has patient data such as patient's historical dietary amount data, historical blood sugar concentration;

User's input module 46 is used for the patient to system's input control information, controls;

Display module 47 is used for showing to the patient data such as injection of insulin information, blood sugar concentration information, the too high or too low warning message of blood glucose;

Controller 45, the control whole system is estimated the blood glucose rate of change according to the CGMS reading, judges that the patient has the initial bolus insulin of feed back input, whenever at a distance from bolus insulin of injection in 30 minutes, gets into safety range up to blood glucose value.

9. bolus insulin design according to claim 8 and infusion device is characterized in that controller 45 comprises like lower module:

Diet detection module 451 is used for judging according to patient's change of blood sugar rate whether the patient has feed;

Blood glucose prediction module 452 is used to predict Future Time patient's blood glucose concentration value;

Heavy dose of design module 453, following blood sugar level that is used for calculating according to blood glucose prediction module 452 and the gap between the setting value, and the intensity of variation of infusion of insulin speed are calculated the bolus insulin quantity that whenever should inject at a distance from 30 minutes.

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