CN112401890A - Urine dynamic monitor correcting method and monitoring system - Google Patents
Urine dynamic monitor correcting method and monitoring system Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
- A61B5/202—Assessing bladder functions, e.g. incontinence assessment
- A61B5/205—Determining bladder or urethral pressure
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
Abstract
The invention discloses a urine dynamic monitor correcting method and a urine dynamic monitor system, which comprise the following steps: s01, acquiring the urine flow rate index information by adopting a urine flow rate measuring module, processing abnormal fluctuation by a wavelet transform algorithm and then synchronizing the abnormal fluctuation to a main control module; s02, the main control module monitors the electrocardiosignals according to the electrocardiosignal detection module and acquires electrocardio indexes by combining a wavelet function and an R wave detection algorithm; s03, constructing a nonlinear wavelet threshold model aiming at the urine flow rate index and the electrocardio index information to remove high-frequency noise and obtain preprocessing information; s04, detecting the position of the R wave peak of the preprocessed information by adopting a wavelet modulus maximum value method to obtain effective information in the electrocardiosignal, effectively reducing error rate of misunderstanding and missed detection, improving monitoring precision of the monitor, carrying out quantitative analysis and correct diagnosis on the lower urinary tract disorder of the patient, and facilitating the doctor to make an accurate treatment scheme according to the diagnosis result.
Description
Technical Field
The invention relates to the technical field of clinical medicine, in particular to a urine dynamic monitor correcting method and a urine dynamic monitor monitoring system.
Background
With the increasing aging of China, the number of people suffering from lower urinary tract dysfunction is increased. The demand of high performance urodynamic analyzer is also increasing gradually, and the urodynamic analyzer that present domestic hospital urodynamic surgery owned mainly relies on the foreign import, and not only operating procedure is difficult for mastering during the use, and the cost of maintenance of instrument is on the high side moreover, and the consumptive materials such as pressure measurement pipe, connecting tube that the supporting was used simultaneously are expensive.
Therefore, it is very significant to develop a urodynamic analyzer with high cost performance, complete functions and convenient operation.
However, the conventional urodynamic monitoring device is added with a real-time electrocardiosignal monitoring function in the urinary examination equipment, so that the conventional urodynamic monitoring device can acquire the beating condition of the heart rhythm of the patient in real time while the patient performs the urinary examination. However, the conventional urodynamic analyzer is equipped with a conventional electrocardiograph monitor, and the accuracy of measurement of the conventional urodynamic analyzer is easily affected by interference of external factors during the use process of the conventional urodynamic analyzer, such as metal ornaments, electronic devices and the like. When the urodynamic analyzer is matched with the urodynamic analyzer for use, the urodynamic analyzer is continuously interfered, and accurate electrocardiosignals are difficult to obtain. Because of the above problems, in the prior art, only the electrocardiographic information obtained by the urodynamic analyzer is used as an auxiliary reference factor, and special electrocardiographic detection needs to be performed again when abnormality may occur.
Disclosure of Invention
The invention aims to provide a urodynamic monitor correction method and a urodynamic monitor monitoring system, which can effectively reduce the error rate of misunderstanding and omission detection and improve the monitoring precision of a monitor by acquiring an electric signal containing urodynamic rate index information and an electrocardio information effective value and detecting the position of an R wave peak and the condition of existence of a discontinuity in the electric signal, can carry out quantitative analysis and correct diagnosis on the lower urinary tract disorder of a patient by avoiding mutual interference between two instruments, and is favorable for an attending doctor to formulate an accurate treatment scheme according to a diagnosis result.
The technical problem that the multi-layer materials are difficult to separate one by one in the prior art is solved.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a urine dynamic monitor correction method comprises the following steps:
s01, acquiring the urine flow rate index information by adopting a urine flow rate measuring module, processing abnormal fluctuation by a wavelet transform algorithm and then synchronizing the abnormal fluctuation to a main control module;
s02, the main control module monitors the electrocardiosignals according to the electrocardiosignal detection module and acquires electrocardio indexes by combining a wavelet function and an R wave detection algorithm;
s03, constructing a nonlinear wavelet threshold model aiming at the urine flow rate index and the electrocardio index information to remove high-frequency noise and obtain preprocessing information;
and S04, detecting the position of the R wave peak of the preprocessed information by adopting a wavelet modulus maximum value method to obtain effective information in the electrocardiosignal.
As a preferable scheme of the invention, the data end of the urine flow rate measurement module detects the liquid quality through a gravity sensor, adopts differential calculation to obtain a urine flow rate index, and adopts a wavelet transform algorithm to process abnormal fluctuation.
As a preferred scheme of the present invention, the wavelet transform algorithm adopts a Mallat function to decompose and reconstruct the urine flow rate index, two input ends of the Mallat function respectively correspond to a high-pass filter G and a low-pass filter H, and the corresponding expressions of the high-pass filter G and the low-pass filter H are as follows:
the H (ω) and G (ω) satisfy the following equations, respectively:
|H(ω)|2+|H(ω+π)|2＝1
|G(ω)|2+|G(ω+π)|2＝1
according to the fourier variation, the relationship satisfied by H (ω) and G (ω) is:
H(ω)G(ω)*+H(ω+π)G(ω+π)*＝0。
as a preferred embodiment of the present invention, the filter bank is constructed according to a relation that the high-pass filter G and the low-pass filter H satisfy:
G(ω)＝±e-jωH*(ω+π)
and performing information reconstruction on the output signal by performing orthogonal decomposition on the signal output by the filter bank.
As a preferred scheme of the present invention, the reconstruction information is synchronized to the main control module, and the main control module sets a nonlinear wavelet threshold for the wavelet function to perform high-frequency noise processing.
As a preferred embodiment of the present invention, the non-linear wavelet threshold denoising step is as follows:
firstly, adopting sym4 wavelet as a basis function for processing reconstruction information and electrocardiosignals, and utilizing Mallat decomposition algorithm to carry out 5-layer decomposition on the original signals to obtain corresponding wavelet coefficients on each layer of scale;
secondly, processing the wavelet coefficients decomposed in each layer by using a soft threshold function, setting the wavelet coefficients lower than the threshold to be zero, keeping the wavelet coefficients higher than the threshold, and removing high-frequency noise in the original signal;
and finally, setting the wavelet coefficient lower than the threshold to zero, obtaining a new wavelet coefficient on each layer, and reconstructing the new wavelet coefficient through a Mallat recombination algorithm to finally obtain the electrocardiosignal with high signal-to-noise ratio.
As a preferred scheme of the invention, a threshold model is constructed by adopting the soft threshold function to the amplitude value and the decomposition layer number of the wavelet coefficient, and a global threshold is set according to the length of the signal to be processed and the doped signal.
As a preferred scheme of the invention, when the Mallat recombination algorithm is adopted to reconstruct the wavelet coefficient, the R wave peak value in different time intervals is detected by using the R wave detection algorithm, and the determined R wave position is replenished into the original sequence.
As a preferred embodiment of the present invention, the R-wave detection algorithm comprises the following steps:
firstly, baseline drift of electrocardiosignals received by the main control module is filtered by a high-pass filter, myoelectric components are filtered by a low-pass filter, power frequency interference is filtered by a band-pass filter, and analog signals of the electrocardiosignals are converted into digital signals;
secondly, decomposing the original digital signal by adopting a Mallat algorithm, performing three-layer decomposition on the digital signal by utilizing a wavelet function, and setting corresponding coefficients as a coarse coefficient and a detail coefficient after the decomposition is finished;
thirdly, acquiring a modulus maximum value from the wavelet coefficient, comparing the modulus maximum value with a threshold value, and keeping data information higher than the threshold value;
and finally, connecting the peak points of the retained maximum module extreme values, and marking the intersection point of the connecting line and the horizontal axis as the position of the R wave peak.
A urine dynamic monitor correction monitoring system comprises a urine flow rate measuring module, a bladder pressure measuring module, a urethra pressure measuring module and an electrocardiosignal detecting module, wherein the urine flow rate measuring module is used for monitoring urination, the urine flow rate is detected by a gravity sensor in the urine flow rate measuring module and is synchronously transmitted to a data collecting module, the bladder pressure measuring module and the urethra pressure measuring module measure pressure values in a bladder and a urethra through pressure sensors, the pressure values are synchronously transmitted to the main control module through the data collecting module to monitor pressure change in real time, the signal end of the data collecting module is interactively connected with the main control module, the data end of the main control module processes electrocardio data through the electrocardiosignal detecting module, and electrocardio signals are monitored in real time through an R wave detecting algorithm.
Compared with the prior art, the invention has the following beneficial effects:
the invention combines the index information of the urine flow rate and the electric signal in the electrocardio information, reserves effective information carried by a break point in the middle of an original signal based on the position of the R wave peak in the detected electric signal and the condition of existence of the break point, respectively carries out urodynamic detection and electrocardio detection according to the information of the undisturbed R wave peak and the break point, avoids mutual interference, can effectively reduce the error rate of misunderstanding and omission detection, improves the monitoring precision of a monitor, after the data of the two are obtained, the data are processed by a series of mathematical processing methods, then the data of each path are transmitted to an upper computer by a Bluetooth module, the data are classified by the upper computer again, by respectively drawing curves without noises such as interference and the like, quantitative analysis and correct diagnosis are carried out on the lower urinary tract disorder of a patient according to the curves, which is beneficial for an attending doctor to make an accurate treatment scheme according to a diagnosis result.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
FIG. 1 is a flow chart of a method for correcting a urodynamic monitor according to an embodiment of the present invention;
fig. 2 is a diagram illustrating the results of a urine dynamic monitor correction monitoring system according to an embodiment of the present invention.
The reference numerals in the drawings denote the following, respectively:
1-a uroflow rate measurement module; 2-bladder pressure measurement module; 3-a urethral pressure measurement module; 4-electrocardiosignal detection module; 5-a gravity sensor; 6-a pressure sensor; 7-a data acquisition module; 8-a main control module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and 2, the invention provides a method for correcting a urodynamic monitor, which detects the position of R wave peak and the existence of discontinuity in an electrical signal by obtaining the electrical signal containing the index information of urine flow rate and the effective value of electrocardiographic information, can remove noise, simultaneously retain discontinuity in the original signal, effectively retain information carried by the discontinuity, decompose the original signal, recombine wavelet coefficients of each layer scale without oscillation, effectively restore the signal, set the detection interval of R wave peak, adjust the size of threshold, effectively reduce the error rate of misinterpretation and missed detection, and improve the monitoring accuracy of the monitor.
The method comprises the following steps:
s01, acquiring the urine flow rate index information by adopting a urine flow rate measuring module, processing abnormal fluctuation by a wavelet transform algorithm and then synchronizing the abnormal fluctuation to a main control module;
s02, the main control module monitors the electrocardiosignals according to the electrocardiosignal detection module and acquires electrocardio indexes by combining a wavelet function and an R wave detection algorithm;
s03, constructing a nonlinear wavelet threshold model aiming at the urine flow rate index and the electrocardio index information to remove high-frequency noise and obtain preprocessing information;
and S04, detecting the position of the R wave peak of the preprocessed information by adopting a wavelet modulus maximum value method to obtain effective information in the electrocardiosignal.
The data end of the urine flow rate measurement module detects the liquid quality through a gravity sensor, adopts differential calculation to obtain a urine flow rate index, and adopts a wavelet transform algorithm to process abnormal fluctuation.
In the embodiment, a soft threshold function is adopted to adjust the threshold value according to the dimensions of different layers, a nonlinear wavelet threshold method is selected to remove high-frequency noise, a wavelet decomposition and reconstruction algorithm is utilized to remove baseline drift, the first derivative of an R peak detection Gaussian function is used as a wavelet function to detect an R peak, the impact jitter phenomenon existing in a urine flow rate index influences the flow rate calculation, the quality data is processed by using a sliding average, and then the wavelet threshold method is selected to remove the impact jitter to obtain an effective and smooth quality waveform, so that the reliability and the effectiveness of a flow rate curve are ensured.
The wavelet transformation algorithm adopts a Mallat function to decompose and reconstruct the urine flow rate index, two input ends of the Mallat function respectively correspond to a high-pass filter G and a low-pass filter H, and the corresponding expressions of the high-pass filter G and the low-pass filter H are as follows:
the H (ω) and G (ω) satisfy the following equations, respectively:
|H(ω)|2+|H(ω+π)|2＝1
|G(ω)|2+|G(ω+π)|2＝1
according to the fourier variation, the relationship satisfied by H (ω) and G (ω) is:
H(ω)G(ω)*+H(ω+π)G(ω+π)*＝0。
in this embodiment, the relationship satisfied by H (ω) and G (ω) is conditioned by the orthonormal of the scale function, the orthonormal of the wavelet function, and the mutual orthogonality between the scale function and the wavelet function, and a relationship satisfied by a filter bank may be constructed.
Constructing a filter bank according to a relation satisfied by the high-pass filter G and the low-pass filter H, wherein the relation satisfied is as follows:
G(ω)＝±e-jωH*(ω+π)
and performing information reconstruction on the output signal by performing orthogonal decomposition on the signal output by the filter bank.
In this embodiment, it can be known through the relations that a uniquely determined relation exists between the filter group consisting of the scale function, the wavelet function, the high-pass filter and the low-pass filter, and it can be seen that the signal decomposition of the orthogonal multi-resolution analysis is performed.
And synchronizing the reconstruction information to the main control module, and setting a nonlinear wavelet threshold value aiming at the wavelet function in the main control module to perform high-frequency noise processing.
The non-linear wavelet threshold denoising step comprises the following steps:
firstly, adopting sym4 wavelet as a basis function for processing reconstruction information and electrocardiosignals, and utilizing Mallat decomposition algorithm to carry out 5-layer decomposition on the original signals to obtain corresponding wavelet coefficients on each layer of scale;
secondly, processing the wavelet coefficients decomposed in each layer by using a soft threshold function, setting the wavelet coefficients lower than the threshold to be zero, keeping the wavelet coefficients higher than the threshold, and removing high-frequency noise in the original signal;
and finally, setting the wavelet coefficient lower than the threshold to zero, obtaining a new wavelet coefficient on each layer, and reconstructing the new wavelet coefficient through a Mallat recombination algorithm to finally obtain the electrocardiosignal with high signal-to-noise ratio.
And constructing a threshold model for the amplitude of the wavelet coefficient and the decomposition layer number by adopting the soft threshold function, and setting a global threshold according to the length of the signal to be processed and the doped signal.
In this embodiment, a soft threshold function is adopted to take an absolute value of an element in a signal sequence to be processed to make the absolute value become a positive number, a sorting function is used to sort the sequence from small to large, and then the element in a new sequence is squared to obtain a correlation threshold.
In the embodiment, after a threshold model is constructed by using a soft threshold function, the length of the whole scale space is determined according to the length of original data, signals doped with various noises are subjected to various types of wavelet transformation, the amplitude of a wavelet coefficient is inversely proportional to the number of decomposition layers, the larger the number of decomposition layers is, the smaller the amplitude is, the globally fixed threshold is corrected according to the characteristic of the multiple of the amplitude coefficient between adjacent layers, and the signal-to-noise ratio and the mean square error after the noises are removed are effectively improved.
And when the Mallat recombination algorithm is adopted to reconstruct the wavelet coefficient, the R wave peak values in different time intervals are detected by utilizing an R wave detection algorithm, and the determined R wave position is replenished into the original sequence.
In this embodiment, after the threshold is selected, the original signal needs to be decomposed and the filter coefficients need to be processed by using the filter combination corresponding to the Mallat algorithm, the absolute value of the filter coefficient of each layer is then compared with the threshold T determined by the corresponding layer, if the modulus of the wavelet coefficient is smaller than the determined threshold, the wavelet coefficient is considered as noise and is set to zero, and if the modulus of the wavelet coefficient is larger than the determined threshold, the wavelet coefficient is considered as a useful signal and is retained.
The R wave detection algorithm comprises the following steps:
firstly, baseline drift of electrocardiosignals received by the main control module is filtered by a high-pass filter, myoelectric components are filtered by a low-pass filter, power frequency interference is filtered by a band-pass filter, and analog signals of the electrocardiosignals are converted into digital signals;
secondly, decomposing the original digital signal by adopting a Mallat algorithm, performing three-layer decomposition on the digital signal by utilizing a wavelet function, and setting corresponding coefficients as a coarse coefficient and a detail coefficient after the decomposition is finished;
thirdly, acquiring a modulus maximum value from the wavelet coefficient, comparing the modulus maximum value with a threshold value, and keeping data information higher than the threshold value;
and finally, connecting the peak points of the retained maximum module extreme values, and marking the intersection point of the connecting line and the horizontal axis as the position of the R wave peak.
In this embodiment, the R-wave detection algorithm is used to process the de-noised signal after wavelet decomposition and reconstruction, so that various noises doped in the useful signal frequency band can be effectively removed, the characteristic points in the original signal can be retained, the original signal is very smooth after de-noising by the wavelet algorithm, the oscillation phenomenon is effectively suppressed, the complex situation of aliasing of the original signal frequency band and the noise signal frequency band can be processed, and the practicability is high.
A urine dynamic monitor correction monitoring system comprises a urine flow rate measuring module 1, a bladder pressure measuring module 2, a urethra pressure measuring module 3 and an electrocardiosignal detecting module 4, wherein the urine flow rate measuring module 1 is used for monitoring urination, the urine flow rate is detected by a gravity sensor 5 and is synchronously transmitted to a data collecting module 7, the bladder pressure measuring module 2 and the urethra pressure measuring module 3 measure pressure values in a bladder and a urethra through a pressure sensor 6, the pressure values are synchronously transmitted to a main control module 8 through the data collecting module 7 to monitor pressure change in real time, the signal end of the data collecting module 7 is interactively connected with the main control module 8, the data end of the main control module 8 processes electrocardiosignal data through the electrocardiosignal detecting module 4, and electrocardiosignal is monitored in real time through an R wave detecting algorithm.
In this embodiment, for make full use of urodynamic flow meter, carry out the modularized design with urine driving system, the maintenance of the part of being convenient for like this and improve utilization ratio, and adopt the bluetooth to carry out data transmission, avoided a great deal of data line, make medical personnel more convenient when the mobile device, as long as the bluetooth is connected after successfully, just can carry out data acquisition module's data transmission.
In this embodiment, the uroflow rate measuring module 1, the bladder pressure measuring module 2, the urethra pressure measuring module 3 and the electrocardiosignal detecting module 4 are connected with the main control module 8 through the wireless module, and then the data processed by the processor are transmitted to the upper computer through the bluetooth module, and the data are classified by the upper computer, so as to draw a corresponding curve, and the quantitative analysis and the correct diagnosis can be performed on the lower urinary tract disorder of the patient according to the drawn curve, thereby being beneficial for the main treating doctor to make an accurate treatment scheme according to the diagnosis result.
In this embodiment, the urine flow rate measurement module 1 can only measure the liquid mass through the weighing sensor, and the urine flow rate is indirectly calculated through differentiation, so that the accuracy and stability in obtaining the urine flow rate value are high.
In this embodiment C, adopt external sensor to carry out bladder pressure measurement, send the pressure signal of gathering to the data acquisition end after handling, because external sensor can not produce direct contact with the patient, cross infection's the condition can not take place, and same pressure sensor can repetitious usage and not fragile, and low cost can reduce patient's inspection cost.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A urine dynamic monitor correction method is characterized in that: the method comprises the following steps:
s01, acquiring the urine flow rate index information by adopting a urine flow rate measuring module, processing abnormal fluctuation by a wavelet transform algorithm and then synchronizing the abnormal fluctuation to a main control module;
s02, the main control module monitors the electrocardiosignals according to the electrocardiosignal detection module and acquires electrocardio indexes by combining a wavelet function and an R wave detection algorithm;
s03, constructing a nonlinear wavelet threshold model aiming at the urine flow rate index and the electrocardio index information to remove high-frequency noise and obtain preprocessing information;
and S04, detecting the position of the R wave peak of the preprocessed information by adopting a wavelet modulus maximum value method to obtain effective information in the electrocardiosignal.
2. The urodynamic monitor correction method of claim 1, wherein: the data end of the urine flow rate measurement module detects the liquid quality through a gravity sensor, adopts differential calculation to obtain a urine flow rate index, and adopts a wavelet transform algorithm to process abnormal fluctuation.
3. The urodynamic monitor correction method of claim 2, wherein: the wavelet transformation algorithm adopts a Mallat function to decompose and reconstruct the urine flow rate index, two input ends of the Mallat function respectively correspond to a high-pass filter G and a low-pass filter H, and the corresponding expressions of the high-pass filter G and the low-pass filter H are as follows:
the H (ω) and G (ω) satisfy the following equations, respectively:
|H(ω)|2+|H(ω+π)|2＝1
|G(ω)|2+|G(ω+π)|2＝1
according to the fourier variation, the relationship satisfied by H (ω) and G (ω) is:
H(ω)G(ω)*+H(ω+π)G(ω+π)*＝0。
4. the urodynamic monitor correction method of claim 3, wherein: constructing a filter bank according to a relation satisfied by the high-pass filter G and the low-pass filter H, wherein the relation satisfied is as follows:
G(ω)＝±e-jωH*(ω+π)
and performing information reconstruction on the output signal by performing orthogonal decomposition on the signal output by the filter bank.
5. The urodynamic monitor correction method of claim 4, wherein: and synchronizing the reconstruction information to the main control module, and setting a nonlinear wavelet threshold value aiming at the wavelet function in the main control module to perform high-frequency noise processing.
6. The urodynamic monitor correction method of claim 5, wherein: the non-linear wavelet threshold denoising step comprises the following steps:
firstly, adopting sym4 wavelet as a basis function for processing reconstruction information and electrocardiosignals, and utilizing Mallat decomposition algorithm to carry out 5-layer decomposition on the original signals to obtain corresponding wavelet coefficients on each layer of scale;
secondly, processing the wavelet coefficients decomposed in each layer by using a soft threshold function, setting the wavelet coefficients lower than the threshold to be zero, keeping the wavelet coefficients higher than the threshold, and removing high-frequency noise in the original signal;
and finally, setting the wavelet coefficient lower than the threshold to zero, obtaining a new wavelet coefficient on each layer, and reconstructing the new wavelet coefficient through a Mallat recombination algorithm to finally obtain the electrocardiosignal with high signal-to-noise ratio.
7. The urodynamic monitor modification method of claim 6, wherein the soft threshold function is used to construct a threshold model for the amplitude of wavelet coefficients and the number of decomposition layers, and a global threshold is set according to the length of the signal to be processed and the doped signal.
8. The method for correcting urodynamic monitor according to claim 6, wherein the Mallat recombination algorithm is used to reconstruct the wavelet coefficients, the R-wave detection algorithm is used to detect the R-wave peak values in different time intervals, and the determined R-wave positions are added to the original sequence again.
9. The urodynamic monitor correction method of claim 8, wherein the R-wave detection algorithm comprises the following steps:
firstly, baseline drift of electrocardiosignals received by the main control module is filtered by a high-pass filter, myoelectric components are filtered by a low-pass filter, power frequency interference is filtered by a band-pass filter, and analog signals of the electrocardiosignals are converted into digital signals;
secondly, decomposing the original digital signal by adopting a Mallat algorithm, performing three-layer decomposition on the digital signal by utilizing a wavelet function, and setting corresponding coefficients as a coarse coefficient and a detail coefficient after the decomposition is finished;
thirdly, acquiring a modulus maximum value from the wavelet coefficient, comparing the modulus maximum value with a threshold value, and keeping data information higher than the threshold value;
and finally, connecting the peak points of the retained maximum module extreme values, and marking the intersection point of the connecting line and the horizontal axis as the position of the R wave peak.
10. A urine dynamic monitor correction monitoring system is characterized by comprising a urine flow rate measuring module (1) for monitoring urination, a bladder pressure measuring module (2), a urethra pressure measuring module (3) and an electrocardiosignal detecting module (4), wherein the urine flow rate measuring module (1) detects urine flow rate through a gravity sensor (5) and synchronously transmits the urine flow rate to a data collecting module (7), the bladder pressure measuring module (2) and the urethra pressure measuring module (3) measure pressure values in bladder and urethra through a pressure sensor (6) and synchronously transmit the pressure values to a main control module (8) through the data collecting module (7) for monitoring pressure change in real time, the signal end of the data collecting module (7) is interactively connected with the main control module (8), the data end of the main control module (8) processes electrocardio data through the electrocardiosignal detecting module (4), and the electrocardiosignals are monitored in real time through an R wave detection algorithm.
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