阅读说明：本技术 一种基于边缘场检测的二维eit电极阵列结构优化方法 (Two-dimensional EIT electrode array structure optimization method based on fringe field detection ) 是由 孙江涛 梁小凤 徐立军 田文斌 谢跃东 于 2019-12-12 设计创作，主要内容包括：本发明公开了一种基于边缘场检测的二维EIT电极阵列结构优化方法,涉及电阻抗层析成像技术在膀胱尿量监测领域。首先针对膀胱功能障碍患者,在该患者的膀胱底面往上距离h的位置,在人体周身表面设置n个电极,电极覆盖的弧长占人体周长的比例为α；然后按照初始值距离h,电极n以及比例α,将各电极布置在该膀胱功能障碍患者身上,构建重建图像计算该患者的膀胱体积。分别对比例α,电极个数n和距离h进行优化,最后按照最优的最优距离h’,电极个数n’和比例α’对膀胱功能障碍患者身上的实际电极进行调整,并实时监测重建图像,计算该患者的膀胱体积。当患者的膀胱体积达到设定的阈值时,对患者进行预警。本发明针对性的提高了膀胱区域灵敏度,并提出相应的评价指标。(The invention discloses a two-dimensional EIT electrode array structure optimization method based on fringe field detection, which relates to the field of electrical impedance tomography in bladder urine volume monitoring, and is characterized in that firstly, aiming at a bladder dysfunction patient, n electrodes are arranged on the surface of the whole body of the patient at a position with a distance h from the bottom surface of the bladder of the patient to the top, the proportion of the arc length covered by the electrodes to the circumference of the human body is α, then, according to the initial value of the distance h, the electrode n and the proportion α, the electrodes are arranged on the patient with the bladder dysfunction, a reconstructed image is constructed to calculate the bladder volume of the patient, proportional values α and electrode numbers n and the distance h are optimized, and finally, according to the optimal distance h ', the electrode numbers n ' and the proportion α ', the actual electrodes on the patient with the bladder dysfunction are adjusted, the reconstructed image is monitored in real time, the bladder volume of the patient is calculated, and when the bladder volume of the patient reaches a set threshold value, the patient is subjected to early warning.)

1. A two-dimensional EIT electrode array structure optimization method based on fringe field detection is characterized by comprising the following steps:

aiming at a patient with bladder dysfunction, n electrodes are arranged on the surface of the whole body of the patient at a position which is a distance h from the bottom surface of the bladder of the patient to the upper part, and the ratio of the arc length covered by the electrodes to the circumference of the human body is α;

secondly, arranging each electrode on the patient with bladder dysfunction according to the initial value distance h, the electrode n and the proportion α to construct a reconstructed image;

step three, comparing α, optimizing the number n of electrodes and the distance h to obtain three optimal parameter results;

the method comprises the following specific steps:

step 301, calculating the bladder area sensitivity f₁Sensitivity of edge effect f₂Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄；

Wherein:

N₁the number of the bladder area pixel points in the reconstructed image is M, and the number of the measurement frames obtained through voltage measurement is M; z is a radical of_i',j'The sensitivity value of the ith 'row and the jth' column of a sensitivity matrix used for image reconstruction;

N₂number of types of conductivity, g, at maximum or minimum volume of bladder_(Vmax,i)The maximum characteristic value of the edge effect under the maximum volume conductivity i of the bladder; g_(Vmin,i)Is the minimum characteristic value of the edge effect under the minimum volume conductivity i of the bladder; phi_VmaxDiameter at maximum bladder volume set for simulation;

g_(V,i)the characteristic value of the edge effect when the conductivity i of the bladder volume V is obtained;

step 302, judging the initial proportion α, the number of electrodesRespectively calculating the sensitivity f of the bladder area under the number n and the distance h₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, keeping the value of the storage distance h, and entering step 303; otherwise, abandoning the distance h and entering the step 303;

step 303, selecting the next distance value, returning to step 302 to repeatedly judge whether to store and reserve under the condition that the initial proportion α and the number n of the electrodes are not changed until all the distance values meeting the conditions are screened;

the next distance value refers to: the distance h is reduced by a value of 1 cm;

step 304, calculating the edge effect sensitivity f corresponding to all the distance values meeting the conditions respectively₂Selecting a minimum edge effect sensitivity level f₂The corresponding distance value is taken as the optimal distance h';

step 305, keeping the initial proportion α unchanged, selecting the optimal distance h', and calculating the bladder region sensitivity f corresponding to the initial value of the number n of the electrodes₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, the value of the number n of the storage electrodes is reserved, and the step 306 is entered; otherwise, abandoning the number n of the electrodes and entering the step 306;

step 306, sequentially selecting the electric extreme values one by one from the range of the electrode n, keeping the initial proportion α unchanged, and returning to step 305 to judge whether to store and reserve the electric extreme values under the condition that the selected optimal distance h' is unchanged until the screening of all the numbers of the electrodes meeting the conditions is finished;

307, calculating the edge effect sensitivity f corresponding to the number of electrodes meeting the conditions₂Selecting a minimum edge effect sensitivity level f₂The corresponding number of the electrodes is used as the optimal number n';

step 308, selecting the optimal distance h 'and the number n' of the electrodes, and calculating the sensitivity f of the bladder area corresponding to the initial proportion α₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if so, reserving storageThe value of the initial proportion α is entered into the step 309, otherwise, the initial proportion α is abandoned and the step 309 is entered;

309, reducing the initial proportion α by 30 degrees to serve as a next proportion value, keeping the optimal distance h 'and the number n' of the electrodes unchanged, returning to the step 308 to judge whether storage and reservation are carried out or not until all proportions meeting the conditions are screened;

step 310, calculating the edge effect sensitivity f corresponding to all the ratio values meeting the conditions₂Selecting a minimum edge effect sensitivity level f₂The corresponding proportion value is used as the optimal proportion α';

step four, adjusting actual electrodes on the patient with bladder dysfunction according to the optimal distance h ', the number n ' of the electrodes and the proportion α ', and monitoring a reconstructed image in real time;

and fifthly, when the volume of the bladder of the patient reaches a set threshold value through the reconstructed image, early warning is carried out on the patient to remind the patient to urinate.

2. The two-dimensional EIT electrode array structure optimization method based on fringe field detection as claimed in claim 1, wherein in the second step, the distance h is in the range of 0-19 cm, the initial value is 19cm, the electrode n is in the range of 8, 10, 12, 14 and 16, the initial value is 16, the ratio α is in the range of 0-360 degrees, and the initial value is 360 degrees.

3. The two-dimensional EIT electrode array structure optimization method based on fringe field detection as claimed in claim 1, wherein said reconstructed image is characterized by: when measuring each time, sequentially exciting and measuring electrodes attached to the human abdomen, and inverting the distribution of the internal electrical impedance of the human body through a measured voltage signal and a finite element model, namely reconstructing an image; different pixel point sizes in the image represent different conductivity sizes, and the EIT method measures the bladder volume, namely calculates the bladder volume through reconstructing the image.

Technical Field

The invention relates to application of an Electrical Impedance Tomography (EIT) technology in the field of bladder urine volume monitoring, in particular to a two-dimensional EIT electrode array structure optimization method based on fringe field detection.

Background

Bladder dysfunction is often caused by spinal cord injury, neurological diseases, diabetes, surgery, childbirth, and natural aging. For patients with bladder dysfunction, the treatment is generally performed clinically by a method of timed catheterization. The method of timed catheterization is easy to cause complications such as urinary tract infection, bladder high pressure and the like. With the continuous improvement of medical conditions and the increased demand of patients for quality of life, the restoration of the perception of bladder volume is increasingly important. The bladder capacity monitor can continuously monitor the bladder capacity in real time, and can give an early warning to a patient to remind the patient to urinate when the bladder is nearly full.

The existing bladder volume measuring methods include an ultrasonic method and an electrical impedance tomography method. Ultrasound is often operated by medical personnel due to its complex operation and relatively high price, and is not suitable for daily use by patients. Compared with the prior art, the wearable EIT monitoring device has the advantages of simple structure, low price, non-invasion and real-time monitoring, and is concerned by scholars at home and abroad: as an article: t.schlebusch, s.nienke, s.leonhardt, and m.walter, estimation of bladder volume based on electrical impedance tomography, physiological measurements, vol 35, phase 9, p 1813 and 1823, 9.2014.

The current electrode arrangement commonly used in EIT is a uniform 16-electrode. For example, in the prior art, the invention of patent No. 201510026916.9 applied to zhongshan university proposes a device and a method for monitoring urine volume in bladder in real time based on electrical impedance tomography, and an EIT sensor is used in the article, and the electrode arrangement may be optimal for applications where the object of interest is located in the whole field, such as lung respiration monitoring. However, for monitoring the bladder with the object of interest in the first half of the body, targeted optimization of the sensors is required.

In addition, the relative position of the electrodes and the bladder, the number of the electrodes and other sensor parameters also have influence on the accuracy of bladder volume measurement; improving the accuracy of EIT bladder volume measurements is an urgent need to bring the method further closer to practical applications.

Disclosure of Invention

Aiming at the technical problems in the field, the invention provides a two-dimensional EIT electrode array structure optimization method based on fringe field detection, which can improve the sensitivity of fringe effect to bladder volume, reduce the influence of the deformation degree of a reconstructed image on measurement and increase the reliability of the reconstructed image; meanwhile, the bladder volume measurement under the parameter is ensured to have better consistency under different urine conductivities.

A two-dimensional EIT electrode array structure optimization method based on fringe field detection comprises the following steps:

aiming at a patient with bladder dysfunction, n electrodes are arranged on the surface of the whole body of the patient at a position which is a distance h from the bottom surface of the bladder of the patient to the upper part, and the ratio of the arc length covered by the electrodes to the circumference of the human body is α;

and step two, arranging each electrode on the patient with bladder dysfunction according to the initial value distance h, the electrode n and the proportion α to construct a reconstructed image.

The range of the distance h is 0-19 cm, the initial value is 19cm, the range of the electrode n is (8, 10, 12, 14, 16), the initial value is 16, the range of the proportion α is 0-360 degrees, and the initial value is 360 degrees.

The reconstructed image is: when measuring each time, sequentially exciting and measuring electrodes attached to the human abdomen, and inverting the distribution of the internal electrical impedance of the human body through a measured voltage signal and a finite element model, namely reconstructing an image; different pixel sizes in the image represent different conductivity sizes.

Step three, comparing α, optimizing the number n of electrodes and the distance h to obtain three optimal parameter results;

the method comprises the following specific steps:

step 301, calculating the bladder area sensitivity f₁Sensitivity of edge effect f₂Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄；

Wherein:

N₁the number of the bladder area pixel points in the reconstructed image is M, and the number of the measurement frames obtained through voltage measurement is M; z is a radical of_i',j'The sensitivity value of the ith 'row and the jth' column of the sensitivity matrix used when reconstructing the image.

N₂Number of types of conductivity, g, at maximum or minimum volume of bladder_(Vmax,i)The maximum characteristic value of the edge effect under the maximum volume conductivity i of the bladder; g_(Vmin,i)Is the minimum characteristic value of the edge effect under the minimum volume conductivity i of the bladder; phi_VmaxThe diameter at maximum volume of bladder set for simulation.

A pixel point representing 1/4 a maximum amplitude image, C being a circle centered at the center of gravity of 1/4 the maximum amplitude image; k is 1/4 maximum amplitude image pixel number.

g_(V,i)The characteristic value of the edge effect when the conductivity i of the bladder volume V is obtained;

representing the mean of the values of the edge effect characteristic for a certain volume V at different conductivities.

Represents the maximum volume V_maxMean values of edge effect characteristic values at different conductivities;

representing the minimum volume V_minMean values of edge effect characteristic values at different conductivities;

step 302, judging the initial proportion α, and respectively calculating the sensitivity f of the bladder area under the condition of the number n of electrodes and the distance h₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, keeping the value of the storage distance h, and entering step 303; otherwise, abandoning the distance h and entering the step 303;

and 303, selecting the next distance value, returning to 302 to repeatedly judge whether to store and reserve under the condition that the initial proportion α and the number n of the electrodes are not changed until all the distance values meeting the conditions are screened.

The next distance value refers to: the distance h is reduced by a value of 1 cm;

step 304, calculating the edge effect sensitivity f corresponding to all the distance values meeting the conditions respectively₂Selecting a minimum edge effect sensitivity level f₂The corresponding distance value is taken as the optimal distance h';

step 305, keeping the initial proportion α unchanged, selecting the optimal distance h', and calculating the bladder region sensitivity f corresponding to the initial value of the number n of the electrodes₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, the value of the number n of the storage electrodes is reserved, and the step 306 is entered; otherwise, abandoning the number n of the electrodes and entering the step 306;

and step 306, sequentially selecting the electric extreme values one by one from the range of the electrode n, keeping the initial proportion α unchanged, and returning to step 305 to judge whether to store and reserve the electric extreme values under the condition that the selected optimal distance h' is unchanged until the screening of all the numbers of the electrodes meeting the conditions is finished.

307, calculating the edge effect sensitivity f corresponding to the number of electrodes meeting the conditions₂Choose the smallestSensitivity of edge effects f₂The corresponding number of the electrodes is used as the optimal number n';

step 308, selecting the optimal distance h 'and the number n' of the electrodes, and calculating the sensitivity f of the bladder area corresponding to the initial proportion α₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄If so, keeping the stored initial proportion α value and entering the step 309, otherwise, abandoning the initial proportion α and entering the step 309;

and 309, reducing the initial proportion α by 30 degrees to serve as a next proportion value, keeping the optimal distance h 'and the number n' of the electrodes unchanged, returning to the step 308 to judge whether storage and retention are performed or not until all proportions meeting the conditions are screened.

Step 310, calculating the edge effect sensitivity f corresponding to all the ratio values meeting the conditions₂Selecting a minimum edge effect sensitivity level f₂The corresponding proportion value is used as the optimal proportion α';

and step four, adjusting the actual electrodes on the patient with bladder dysfunction according to the optimal distance h ', the number n ' of the electrodes and the proportion α ', and monitoring the reconstructed image in real time.

And fifthly, when the volume of the bladder of the patient reaches a set threshold value through the reconstructed image, early warning is carried out on the patient to remind the patient to urinate.

The invention has the advantages that:

1) the two-dimensional EIT electrode array structure optimization method based on fringe field detection optimizes an EIT sensor, pertinently improves the sensitivity of a bladder area, and provides corresponding evaluation indexes;

2) the two-dimensional EIT electrode array structure optimization method based on the fringe field detection selects the fringe effect sensitivity degree with a key effect as a main optimization target, simplifies the optimization process and ensures the performance of the optimization result in the aspects of image deformation and urine conductivity consistency;

3) the two-dimensional EIT electrode array structure optimization method based on fringe field detection improves bladder volume measurement accuracy based on fringe effect through optimization, and can be manufactured into a cheap device to monitor the bladder volume more accurately.

Drawings

FIG. 1 is a schematic diagram of the present invention extracting three sensor parameters that have a greater effect on bladder volume measurements;

FIG. 2 is a flow chart of a two-dimensional EIT electrode array structure optimization method based on fringe field detection according to the present invention;

FIG. 3 is a plot of bladder zone sensitivity for different electrode-to-bladder floor distances in accordance with the present invention;

FIG. 4 is a graph showing the sensitivity of edge effects for different electrode-to-bladder floor distances in accordance with the present invention;

FIG. 5 is a schematic representation of the target area and 1/4 maximum amplitude image of the present invention;

FIG. 6 shows the consistency error of volume measurement under three urine conductivity variation trends according to the invention.

Detailed Description

The following describes embodiments of the present invention in detail and clearly with reference to the examples and the accompanying drawings.

The invention discloses a two-dimensional EIT electrode array structure optimization method based on fringe field detection, which comprises the steps of firstly, extracting three sensor parameters which have large influence on bladder volume measurement, as shown in figure 1, respectively covering an electrode with an arc length accounting for perimeter proportion α, an electrode-to-bladder bottom distance h and an electrode number n, keeping an initial proportion α and the electrode number n unchanged, optimizing the distance h, then, keeping an optimization result of the distance h, optimizing the electrode number n, using an initial value for a proportion α, finally keeping the optimization results of the distance h and the electrode number n, optimizing the initial proportion α, judging and verifying each optimization process by adopting four evaluation standards, wherein the four evaluation standards comprise bladder region sensitivity, fringe effect sensitivity, reconstruction image deformation degree and different urine conductivity consistency, arranging a final optimal value meeting conditions on a patient, monitoring the reconstruction image in real time, and giving an early warning to the patient and reminding the patient to urinate.

As shown in fig. 2, the following steps are divided:

step one, aiming at a patient with bladder dysfunction, extracting sensor parameters with large influence on bladder volume measurement, namely a distance h, an electrode n and a proportion α;

and step two, arranging each electrode on the patient with bladder dysfunction according to the initial value distance h, the electrode n and the proportion α to construct a reconstructed image.

The range of the distance h is 0-19 cm, the initial value is 19cm, the range of the electrode n is (8, 10, 12, 14, 16), the initial value is 16, the range of the proportion α is 0-360 degrees, and the initial value is 360 degrees.

The reconstructed image is: when measuring each time, sequentially exciting and measuring electrodes attached to the abdomen of the human body, and obtaining a measurement frame by one-time complete voltage measurement; taking 16 electrodes adjacent excitation as an example, the measurement frame includes that, when 1,2 electrodes are excited, 3,4 electrodes, 4,5 electrodes … 15,16 electrodes are measured sequentially; 2,3 electrodes, 4,5 electrodes, 5,6 electrodes … 16,1 electrode are measured sequentially; … 16,1, 2,3, 4, … 14,15 electrodes are measured sequentially when the electrodes are excited; n (n-3) in total. Inverting the distribution of the internal electrical impedance of the human body through a finite element model obtained by measuring voltage signals and simulating, namely reconstructing an image; different pixel point sizes in the image represent different conductivity sizes, and the EIT method measures the bladder volume, namely calculates the bladder volume through reconstructing the image.

Step three, comparing α, optimizing the number n of electrodes and the distance h to obtain three optimal parameter results;

four evaluation criteria: bladder zone sensitivity f₁Sensitivity of edge effect f₂Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄The method is used for optimizing each parameter and judging whether the optimization result is optimal or not, and provides a multi-target problem for a plurality of evaluation standards, and the method mainly depends on the edge effect sensitivity degree f in the bladder volume measurement process₂And other standards can ensure that the bladder volume measurement can be normally carried out only in a certain range, so that the edge effect sensitivity degree f is selected₂As a primary objective, other objectives are converted into constraints;

the method comprises the following specific steps:

step 301, calculating the bladder area sensitivity f₁Sensitivity of edge effect f₂Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄；

Sensitivity of edge effects f₂The characteristic value is characterized by the ratio of the difference value of the maximum volume average value and the minimum volume average value of the edge effect characteristic value under different conductivities to the diameter of the maximum volume.

Degree of distortion f of reconstructed image₃The proportion of the portion of the maximum amplitude image that does not conform to the shape of the true image object is characterized 1/4.

Consistency of conductivity f for different urine₄The consistency of the conductivity across different urine samples was measured by normalizing the shift from the average for different conductivity across different volumes to volume.

Wherein:

N₁the number of the bladder area pixel points in the reconstructed image is M, and the number of the measurement frames obtained through voltage measurement is M; z is a radical of_i',j'The sensitivity value of the ith 'row and the jth' column of the sensitivity matrix used when reconstructing the image. Wherein the content of the first and second substances,

(x, y) is the coordinate of the bladder area of the reconstructed image with the pixel point at the upper left corner as the origin, and i' represents the coordinate asThe serial number of the pixel point at (x, y); m is the serial number of the excitation electrode, n is the serial number of the measurement electrode, and the number M of measurement frames under different excitation measurement modes can be calculated by knowing M and n; j' is the serial number in the measurement frame of electrode m excitation, electrode n measurement, certain excitation measurement mode; a is the size of a certain pixel. Phi_mPotential generated for (m, m +1) electrode pair, phi_nFor the potential generated by the (n, n +1) electrode pair, I_mIs the current between the (m, m +1) electrode pair, I_nIs the current between the (n, n +1) electrode pair.

N₂The number of conductivity types at the maximum or minimum volume of the bladder, in this example 3, g_(Vmax,i)The characteristic value of the edge effect under the maximum volume conductivity i of the bladder is obtained; g_(Vmin,i)Is the characteristic value of the edge effect under the minimum volume conductivity i of the bladder; the method for calculating the characteristic value comprisesp_i'Is the pixel value of the i' th pixel point, y_i'Is the ordinate of the ith' pixel point. Phi_VmaxThe diameter at maximum volume of bladder set for simulation.

The pixel point representing the 1/4 maximum amplitude image, C, is a circle having an area equal to the area of the target region in the 1/4 maximum amplitude image, centered at the center of gravity of the 1/4 maximum amplitude image. The target area is a reconstructed image in an ideal imaging state, namely is completely overlapped with the simulation setting; k is 1/4 maximum amplitude image pixel number.

g_(V,i)Is the edge effect characteristic value of the conductivity i at the bladder volume V;

representing the mean of the values of the edge effect characteristic for a certain volume V at different conductivities.

Represents the maximum volume V_maxDifferent conductivityMean of the lower edge effect feature values;

representing the minimum volume V_minMean values of edge effect characteristic values at different conductivities; v_max490ml was chosen for this example as the maximum bladder volume V; v_minRepresents the minimum value of the bladder volume V, 40ml being selected for this example;

step 302, judging the initial proportion α, and respectively calculating the sensitivity f of the bladder area under the condition of the number n of electrodes and the distance h₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, keeping the value of the storage distance h, and entering step 303; otherwise, abandoning the distance h and entering the step 303;

and 303, selecting the next distance value, returning to 302 to repeatedly judge whether to store and reserve under the condition that the initial proportion α and the number n of the electrodes are not changed until all the distance values meeting the conditions are screened.

The next distance value refers to: the distance h is reduced by a value of 1 cm;

step 304, calculating the edge effect sensitivity f corresponding to all the distance values meeting the conditions respectively₂Selecting a minimum edge effect sensitivity level f₂The corresponding distance value is taken as the optimal distance h';

step 305, keeping the initial proportion α unchanged, selecting the optimal distance h', and calculating the bladder region sensitivity f corresponding to the initial value of the number n of the electrodes₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄Whether it is within a normal range; if yes, the value of the number n of the storage electrodes is reserved, and the step 306 is entered; otherwise, abandoning the number n of the electrodes and entering the step 306;

and step 306, sequentially selecting the electric extreme values one by one from the range of the electrode n, keeping the initial proportion α unchanged, and returning to step 305 to judge whether to store and reserve the electric extreme values under the condition that the selected optimal distance h' is unchanged until the screening of all the numbers of the electrodes meeting the conditions is finished.

Step 307,Respectively calculating the edge effect sensitivity degree f corresponding to the number of all electrodes meeting the conditions₂Selecting a minimum edge effect sensitivity level f₂The corresponding number of the electrodes is used as the optimal number n';

step 308, selecting the optimal distance h 'and the number n' of the electrodes, and calculating the sensitivity f of the bladder area corresponding to the initial proportion α₁Degree of distortion f of reconstructed image₃And consistency of conductivity f of different urine₄If so, keeping the stored initial proportion α value and entering the step 309, otherwise, abandoning the initial proportion α and entering the step 309;

and 309, reducing the initial proportion α by 30 degrees to serve as a next proportion value, keeping the optimal distance h 'and the number n' of the electrodes unchanged, returning to the step 308 to judge whether storage and retention are performed or not until all proportions meeting the conditions are screened.

Step 310, calculating the edge effect sensitivity f corresponding to all the ratio values meeting the conditions₂Selecting a minimum edge effect sensitivity level f₂The corresponding proportion value is used as the optimal proportion α';

and step four, adjusting the actual electrodes on the patient with bladder dysfunction according to the optimal distance h ', the number n ' of the electrodes and the proportion α ', and monitoring the reconstructed image in real time.

And fifthly, when the volume of the bladder of the patient reaches a set threshold value, early warning is carried out on the patient to remind the patient to urinate.