Intelligent inflation algorithm applied to upper arm type upper air sphygmomanometer

Abstract

The invention relates to an intelligent inflation algorithm applied to an upper arm type upper-air sphygmomanometer, which comprises the following steps of: 1, after the sphygmomanometer enters a measurement state, acquiring and processing a pressure value signal by a central processing unit; 2, eliminating the interference of the pulse wave signal on the inflation slope through a filtering algorithm; 3, after the central processing unit obtains data from the pressure sensor, the increasing slope of the pressure value in the cuff is calculated at fixed time intervals; 4, after the slope is obtained through calculation, the slope is compared with a set target slope, and the inflation rate of the inflator pump is adjusted according to the slope difference degree. The low-pass filter is strong in denoising capability, can effectively filter high-frequency noise and a part with rapid data change, enables signals to be clear and reliable, and can reduce the influence and ensure the stability of inflation slope calculation due to the fact that pulse waves are coupled with noise signals generated by an inflation pump in the inflation process.

Description

Technical Field

[0001] The invention belongs to the technical field of medical equipment, and in particular relates to an intelligent inflation algorithm applied to an upper arm type sphygmomanometer. Background Art

[0002] Upper arm blood pressure measurement usually refers to upper arm blood pressure measurement, which plays an important role in family health management due to its convenience and speed. Its working principle is that at the beginning of the measurement, the cuff is inflated so that the subject's arterial blood cannot be transmitted through the upper arm. When the air is inflated to a certain degree, the arterial blood pressure is suppressed. At this time, the pressure sensor will detect this change and convert it into a digital signal. However, the accuracy of upper arm blood pressure measurement is greatly affected by the inflation of the upper air pump, and the accidental measurement of blood pressure is more accidental, resulting in failure to reflect the true blood pressure situation.

[0003] In summary, the prior art has the problem that the blood pressure measurement value is affected by the inflation of the upper inflation pump, resulting in poor stability and accuracy of the blood pressure measurement result. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of the present invention to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] Therefore, the purpose of the present invention is to provide an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer, which can solve the problem in the prior art that the blood pressure measurement value is affected by the inflation of the upper air pump, resulting in poor stability and accuracy of the blood pressure measurement result. The present invention provides an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer, and the intelligent inflation algorithm applied to the upper arm type upper air sphygmomanometer includes the following steps:

[0006] Step 1: After the sphygmomanometer enters the measurement state, the central processing unit collects and processes the pressure value signal, drives the air pump to work according to the set target slope, calculates the inflation slope of the pressure in the cuff at regular intervals, determines the degree of approach to the set target slope, controls the inflation efficiency of the air pump, and reduces the impact of the pressure oscillation in the cuff on the pulse wave;

[0007] Step 2: Eliminate the interference of the pulse wave signal on the inflation slope through filtering algorithm;

[0008] Step 3: After the central processing unit obtains data from the pressure sensor, it calculates the increasing slope of the pressure value in the cuff at a fixed interval;

[0009] Step 4: After calculating the slope, compare it with the set target slope, and adjust the inflation rate of the air pump according to the difference in slope.

[0010] Optionally, in step 1, the central processing unit collects and processes the pressure value signal to drive the air pump to work through the set target slope. Every once in a while, calculate the inflation slope of the pressure in the cuff, and judge the approaching degree with the set target slope to control the inflation efficiency of the air pump, reducing the influence of the pressure oscillation in the cuff on the pulse wave.

[0011] Optionally, when calculating the growth slope of the pressure value in the cuff in step 3, the filtering algorithm is carried out synchronously.

[0012] Optionally, the principle formula of the filtering algorithm in step 2 is as follows:

[0013] Y(n) = a * X(n) + (1 - a)Y(n - 1) (n ∈ (0, ∞))

[0014] Where, Y is the filtering result, X is the input quantity, that is, the pressure value, n is the number of sampling points, and a is the cut-off frequency of the low-pass filter.

[0015] Optionally, the following slope algorithm is used to calculate the growth slope of the pressure value in the cuff in step 3:

[0016]

[0017] Where, is a set of linearly independent functions, a k is the undetermined coefficient (k = 1, 2,..., m, m < n), and the fitting criterion is to make y i (i = 1, 2,..., n) and f(x i ) the sum of the squares of the distances δ i is the smallest.

[0018] Optionally, in step 4, the slope in step 3 is used as the input, and through the inflation algorithm, the algorithm PID system is a closed-loop control method.

[0019] Optionally, the inflation algorithm is as follows:

[0020]

[0021] Where, Kp, Ki, and Kd are the coefficients of the proportional term, integral term, and differential term of the system respectively, e(t) is used as the pressure value slope, and u(t) is the actual output of the system.

[0022] In summary, the present invention includes at least the following beneficial effects:

[0023] (1) The present invention adopts the PID control system first because the system is easy to use and does not require a specifically designed mathematical model; secondly, the system has good adaptability to model uncertainty and external disturbances and is very effective in practical applications; finally, the PID system parameter adjustment is relatively flexible, and the proportion of proportion, integral and differential can be flexibly changed according to the regulated object to achieve the best control effect.

[0024] (2) The present invention has a strong denoising capability through the low-pass filter, which can effectively filter out high-frequency noise and the rapidly changing parts of the data, making the signal clear and reliable. Because the pulse wave will couple with the noise signal generated by the inflation pump during the inflation process, low-pass filtering can reduce this effect and ensure the stability of the inflation slope calculation.

[0025] (3) The present invention can filter out external factors such as the jitter of the measured person during the measurement process through a low-pass filtering algorithm.

[0026] The slope algorithm calculates the slope of different pulse waves based on the least squares method with higher accuracy, which can more truly reflect the pulse wave amplitude of the user in the current state, making the final blood pressure measurement result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a schematic diagram of the overall working process of an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer of the present invention;

[0029] Figure 2 It is an original signal waveform diagram of an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer of the present invention;

[0030] Figure 3 It is a waveform diagram after low-pass filtering of an intelligent inflation algorithm applied to an upper-arm type upper-arm sphygmomanometer of the present invention;

[0031] Figure 4 It is a schematic diagram of a slope calculation queue of an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer of the present invention;

[0032] Figure 5 It is a schematic diagram of slope calculation of an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer of the present invention;

[0033] Figure 6It is a schematic diagram of a PID system of an intelligent inflation algorithm applied to an upper arm type upper air sphygmomanometer of the present invention; DETAILED DESCRIPTION

[0034] The following is combined with Figure 1-6 The present invention is described in further detail.

[0035] Embodiment 1, refer to Figure 1-6 In order to solve the problem in the prior art that the blood pressure measurement value is affected by the inflation of the upper inflation pump, resulting in poor stability and accuracy of the blood pressure measurement result, the present invention discloses an intelligent inflation algorithm for an upper arm type upper air sphygmomanometer, comprising the following steps:

[0036] Step 1: After the sphygmomanometer enters the measurement state, the central processing unit collects and processes the pressure value signal, drives the inflation pump to work according to the set target slope, calculates the inflation slope of the pressure in the cuff at regular intervals, determines the degree of approach to the set target slope, controls the inflation efficiency of the inflation pump, and reduces the impact of the pressure oscillation in the cuff on the pulse wave.

[0037] Step 2: Eliminate the interference of the pulse wave signal on the inflation slope through the filtering algorithm, making the slope parameter more stable and reliable. The pressure value in the cuff is affected by the sampling rate and the air pump exhalation volume during the collection process. Without adding the algorithm, the data is irregular and disordered, which increases the difficulty of obtaining the pulse wave signal; at the same time, an unstable inflation scheme will also cause errors in the pulse wave collection, thus affecting the final measurement results.

[0038] Step 3: After the central processing unit obtains data from the pressure sensor, it calculates the increasing slope of the pressure value in the cuff at fixed intervals. At this time, the filtering algorithm is carried out synchronously to eliminate the influence of the pulse wave on the slope calculation.

[0039] Step 4: After calculating the slope, compare it with the set target slope, and adjust the inflation rate of the air pump according to the difference in the slope. If the slope is large, it means that the inflation is too fast at this time, and the duty cycle is reduced to slow down the pump rate. On the contrary, if the slope is small, it means that the target slope has not been reached, so the duty cycle is increased.

[0040] Specific implementation principle: 1. When the measurement starts, the central processor obtains the pressure value in the armband through the pressure acquisition module. After obtaining the value, the data is low-pass filtered. This application uses software low-pass filtering. The low-pass filtering principle formula is as follows:

[0041] Y(n)=a*X(n)+(1-a)Y(n-1)(n∈(0,∞))

[0042] According to the above formula, Y is the filtering result, X is the input quantity, which is the pressure value in the present invention, n is the number of sampling points, and a is the cut-off frequency of the low-pass filter. By Figure 2 The schematic diagram of the original signal waveform of Figure 3 and the data comparison of the waveform schematic diagram after low-pass filtering of

[0043] 2. Based on the filtered data, the inflation slope at the current time can be calculated every once in a while, which has certain reference significance. The data loading method before calculating the slope is as Figure 4 The schematic diagram of the slope calculation queue. Define a queue with a length of n. The inflation slope is calculated from the mean value in these queues, and the data in the queues are the pressure values in the cuff; this queue follows the principle of first in first out. The data that enters first is taken out first after calculation, and then the data is enqueued.

[0044] 3. After the data is loaded by the above method, based on the filtered data, the inflation slope at the current time can be calculated every once in a while, kp(n) = (P(n + T) - P(n)) / T, but this calculation method often causes errors for people with different pulse amplitudes, resulting in unstable slope calculation. This algorithm provides a stable slope calculation algorithm in slope calculation, calculating the slope size of different pulse waves based on the least squares method:

[0045] (1) Basic idea:

[0046] As a method to solve the curve fitting problem, its basic idea is:

[0047]

[0048] Among them, is a set of linearly independent functions, a k is the undetermined coefficient (k = 1, 2,..., m, m < n), and the fitting criterion is to make y i (i = 1, 2,..., n) and f(x i ) the sum of the squares of the distances ∈ i is the smallest, which is called the least squares method criterion.

[0049] (2) Basic principle:

[0050] The least squares method, also known as the least square method, is a mathematical optimization technique. It uses the principle of minimizing the sum of squared errors between the processed data and the actual set of data to find the best function match with the metadata. The core idea of ​​the least squares method is to minimize the sum of squared distances between the observation points and the estimated points. The square here refers to the use of squares to measure the distance between the observation points and the estimated points, and the minimum refers to the estimated value of the parameter to ensure that the sum of squared distances between each observation point and the estimated point is minimized. Specifically, for a set of data points (x i ,y i ), we hope to get a straight line y = β0 + β1x, and find the sum of the squares of the vertical distances from all data points to this straight line, Q, that is:

[0051]

[0052] Among them, e i is the residual of the ith data point, i.e. the actual value y i and the predicted value β0+β1x i difference.

[0053] (3) Derivation process:

[0054] Suppose we have a set of data points (x i ,y i ), and we want to fit these data points with a linear equation y=ax+b. The cost function (objective function) is the sum of the residual squares, that is:

[0055]

[0056] In order to find the parameters a and b that minimize S(a,b), we take the partial derivatives of S(a,b) with respect to a and b respectively and set them equal to zero.

[0057] Find the partial derivative of a:

[0058]

[0059] Find the partial derivative with respect to b:

[0060]

[0061] Combining the above two equations, we get a linear system of equations:

[0062]

[0063] After simplifying the above formula, we get:

[0064]

[0065] This is a linear equation system about parameters a and b, which can be solved by linear algebra. Solving this system of equations can get the optimal parameters a and b. Convert the linear equation into matrix form:

[0066]

[0067] By inverting the matrix or using other linear equation solving methods, the values ​​of a and b can be obtained, and then y=ax+b can be obtained.

[0068] When we use the least squares fitting method mentioned above to continuously calculate the slope of the sampling points through the central processor every T (the number of sampling points), the calculated slope is as follows Figure 5 The slope calculation is described by the black line segment in the schematic diagram and is stored in a variable for subsequent calculations.

[0069] 4. The inflation slope obtained in the above steps is used as the input of the inflation algorithm. The inflation algorithm is improved and optimized by the PID system. The PID system used in the present invention is a closed-loop control method. The main operation is as follows: Figure 6 PID system diagram, r(t) is the given target value, e(t) is the input of the PID system, and in this invention, it is the pressure value slope. According to the target difference, the output of the PID controller is calculated and finally converted into the actual output u(t) of the system. The formula is as follows:

[0070]

[0071] Kp, Ki and Kd are the coefficients of the system proportional term, integral term and differential term respectively. The output value of the system is determined by the allocation of these three parameters.

[0072] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made according to the structure, shape, principle, and method of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent inflation algorithm for an upper arm sphygmomanometer, characterized in that: The steps include: Step 1: After the sphygmomanometer enters the measurement state, the central processing unit collects and processes the pressure value signal; Step 2: Eliminate the interference of the pulse wave signal on the inflation slope through filtering algorithm; Step 3: After the central processing unit obtains data from the pressure sensor, it calculates the increasing slope of the pressure value in the cuff at a fixed interval; Step 4: After calculating the slope, compare it with the set target slope and adjust the inflation rate of the air pump according to the difference in the slope.

2. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 1, characterized in that: In step 1, the central processing unit collects and processes the pressure value signal to drive the inflation pump to work through the set target slope. At regular intervals, the inflation slope of the pressure in the cuff is calculated, and the degree of approach to the set target slope is determined to control the inflation efficiency of the inflation pump and reduce the impact of the pressure oscillation in the cuff on the pulse wave.

3. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 1, characterized in that: When calculating the increasing slope of the pressure value in the cuff in step 3, the filtering algorithm is performed synchronously.

4. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 1, characterized in that: The filtering algorithm principle formula in step 2 is as follows: Y(n)=a*X(n)+(1-a)Y(n-1)(n∈(0,∞)) Among them, Y is the filtering result, X is the input quantity, that is, the pressure value, n is the number of sampling points, and a is the cutoff frequency of the low-pass filter.

5. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 4, characterized in that: The calculation of the increase slope of the pressure value in the cuff in step 3 adopts the following slope algorithm: Among them, is a set of linearly independent functions, and a k are undetermined coefficients (k = 1, 2,..., m, m < n). The fitting criterion is to minimize the sum of the squares of the distances δ i (i = 1, 2,..., n) between y i and f(x i ).

6. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 5, characterized in that: In step 4, the slope in step 3 is used as input, and the inflation algorithm is used. The algorithm PID system is a closed-loop control method.

7. The intelligent inflation algorithm for an upper arm sphygmomanometer according to claim 6, characterized in that: The inflation algorithm is as follows: Among them, Kp, Ki and Kd are the coefficients of the proportional term, integral term and differential term of the system respectively, e(t) is the slope of the pressure value, and u(t) is the actual output of the system.

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