CN117235585B - Online blood permeation information management system - Google Patents
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- 238000011282 treatment Methods 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000009499 grossing Methods 0.000 claims description 5
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- 238000012806 monitoring device Methods 0.000 description 3
- 230000035515 penetration Effects 0.000 description 3
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- 238000001631 haemodialysis Methods 0.000 description 2
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- 208000001647 Renal Insufficiency Diseases 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
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- 238000001514 detection method Methods 0.000 description 1
- 230000003205 diastolic effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- 210000003734 kidney Anatomy 0.000 description 1
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Abstract
The invention relates to the technical field of data cleaning, in particular to an online blood permeation information management system. The method comprises the steps of dividing an acquired original blood pressure signal into a plurality of local signal segments; dividing the local signal segments into two types according to the discrete condition of the difference between the data values of the adjacent extreme points of each local signal segment; acquiring a noise compensation value according to the difference between the discrete distribution conditions of the difference between the data values of the adjacent extreme points of each type of local signal segment; and adding noise with the noise value being the noise compensation value to the original blood pressure signal, and denoising the original blood pressure signal after adding the noise. According to the invention, noise is added to the original blood pressure signal, so that the low-frequency information in the original blood pressure signal is reduced and decomposed into high-frequency component signals, and the effectiveness of removing the signal noise in the original blood pressure signal is improved.
Description
Technical Field
The invention relates to the technical field of data cleaning, in particular to an online blood permeation information management system.
Background
Hemodialysis is one of the current kidney replacement therapies, which kidney failure patients can rely on to greatly extend life. The on-line blood permeation information management system can monitor various conditions occurring during the treatment of a patient, noise can occur in the blood pressure original signal in the acquisition process, so that the blood pressure original signal can not accurately reflect the stability and the dialysis effect of the circulatory system in the blood permeation process, and the noise signal in the blood pressure original signal is required to be removed.
In the prior art, noise is added to the original blood pressure signal, the original blood pressure signal after the noise is added is decomposed and the noise is removed, but the problem of decomposing low-frequency information in the original blood pressure signal to high-frequency component signals is solved poorly because the noise value of the added noise is not proper, so that the component signals with different frequencies cannot accurately and truly reflect detailed information, and the denoising effect of the noise signals in the original blood pressure signal is poor.
Disclosure of Invention
In order to solve the technical problem that noise removal effect of noise in a blood pressure original signal is poor due to unsuitable noise value added to the blood pressure original signal, the invention aims to provide an on-line blood permeation information management system, and the adopted technical scheme is as follows:
the invention provides an on-line blood permeability information management system, which comprises:
the data acquisition module is used for acquiring original blood pressure signals;
the signal type dividing module is used for dividing the original blood pressure signal into at least two local signal segments; dividing the local signal segments into two types according to the discrete distribution condition of the difference between the data values of the adjacent extreme points of each local signal segment;
the noise compensation value acquisition module is used for acquiring a noise compensation value according to the difference between the discrete distribution conditions of the difference between the data values of the adjacent extreme points of each type of local signal segment;
the signal denoising module is used for adding noise with a noise value being a noise compensation value to the original blood pressure signal and denoising the original blood pressure signal after adding the noise.
Further, the method for dividing the original blood pressure signal into at least two local signal segments comprises the following steps:
and carrying out smoothing treatment on the original blood pressure signal to obtain a mean value segment, and taking a signal segment between the data points corresponding to two extreme points on the original blood pressure signal adjacent to the mean value segment as a local signal segment.
Further, the method for classifying the local signal segments into two types according to the discrete distribution of the differences between the data values of the adjacent extreme points of each local signal segment comprises the following steps:
acquiring a burr characteristic significant value of each local signal segment according to the discrete distribution condition of the difference between the data values of two adjacent extreme points of each local signal segment;
taking the average value of the burr characteristic significant values of all the local signal segments as a burr threshold;
and taking the local signal segment with the burr characteristic significant value smaller than the burr threshold value as a first type of local signal segment, and taking the local signal segment with the burr characteristic significant value larger than or equal to the burr threshold value as a second type of local signal segment.
Further, the method for obtaining the burr feature significant value of each local signal segment according to the discrete distribution condition of the difference between the data values of two adjacent extreme points of each local signal segment comprises the following steps:
taking the absolute value of the difference between the data values of two adjacent extreme points of each local signal segment as the height difference of each local signal segment; taking the height difference with the same value as the same height difference;
and acquiring the burr characteristic significant value of each local signal segment by combining the discrete distribution condition of each type of height difference of each local signal segment and the difference between each height difference.
Further, the calculation formula of the burr feature significance value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein W is the burr characteristic significant value of each local signal section; n is the number of categories of the height difference of each local signal segment; />Probability of occurrence of the nth level difference for each local signal segment; />The average value of the probability of occurrence of all the height differences of each local signal segment; z is the number of height differences for each local signal segment; />The z-th height difference for each local signal segment; />The average value of all the height differences of each local signal segment; a is a preset positive number; />As a hyperbolic tangent function.
Further, the method for acquiring the noise compensation value includes:
acquiring signal noise intensity characteristic values of each type of local signal segment according to the discrete distribution condition of the height difference of each type of local signal segment;
and taking the absolute value of the difference between the signal noise intensity characteristic values of the two types of local signal segments as a noise compensation value.
Further, the calculation formula of the signal noise intensity characteristic value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Signal noise intensity characteristic values for the i-th type of local signal segment; />The number of level differences for the i-th type of local signal segment; />The f-th height difference that is the i-th type of local signal segment; />Is the average of all the height differences of the i-th type of local signal segment.
Further, the method for adding noise with noise value being noise compensation value to the original blood pressure signal comprises the following steps:
and adding noise with a noise value being a noise compensation value to the original blood pressure signal by using an EEMD algorithm.
Further, the method for denoising the blood pressure original signal after adding noise comprises the following steps:
decomposing the original blood pressure signal added with noise by using an EMD method to obtain initial component signals with different frequencies; respectively filtering the initial component signals with different frequencies to obtain denoising component signals with each frequency; and superposing the denoising component signals according to the sequence from low frequency to high frequency pairs to obtain denoised blood pressure original signals.
Further, the noise added to the original blood pressure signal follows a gaussian distribution.
The invention has the following beneficial effects:
in the embodiment of the invention, as the pressure change of blood in the arterial process in the hemodialysis process shows periodicity, in order to accurately analyze the original blood pressure signal, the original blood pressure signal is divided into a plurality of local signal segments; the method comprises the steps that as various types of noise exist in a blood pressure original signal, burrs exist on the surface of the blood pressure original signal, the saliency of the burrs at different positions is different, the discrete distribution condition of the difference between the data values of adjacent extreme points of a local signal section is presented, the obvious condition of the burrs on the surface of the local signal section is presented, and the local signal section is divided into two types based on the discrete distribution condition of the difference between the data values of the adjacent extreme points of the local signal section; the discrete distribution condition of the difference between the data values of the adjacent extreme points of each type of local signal segment reflects the noise signal intensity of each type of local signal segment, after the noise of the noise compensation value obtained based on the difference between the two types of noise signal intensities is added to the original blood pressure signal, when the original blood pressure signal is decomposed, the original low-frequency signal can be better decomposed into the low-frequency signal, the decomposition effect of the high-frequency information in the original blood pressure signal can not be greatly influenced, so that the component signals with different frequencies accurately represent real detail information, and the denoising effect of the noise signal in the original blood pressure signal is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of an on-line blood permeation information management system according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to the specific implementation, structure, characteristics and effects of an on-line blood-permeable information management system according to the invention with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of an on-line blood permeability information management system provided by the present invention with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an on-line blood information management system according to an embodiment of the present invention is shown, the system includes: the system comprises a data acquisition module 101, a signal type division module 102, a noise compensation value acquisition module 103 and a signal denoising module 104.
The data acquisition module 101 is configured to acquire a blood pressure raw signal.
Specifically, a blood pressure monitoring device on a dialysis machine is used for monitoring a blood pressure signal in real time in a dialysis process, and the blood pressure monitoring device is usually connected to an arterial access or a venous access of a patient so as to measure pressure change of blood in the dialysis process and timely display the pressure change on a monitoring instrument to obtain a blood pressure original signal. The original blood pressure signal is a signal generated by pressure change of blood in an artery, reflects the propulsive force of the heart to the blood and the resistance of the blood vessel when the heart pumps the blood, and shows the vascular state, the circulatory stability and the blood pressure regulation condition of a patient in the dialysis process.
The original blood pressure signal itself is not an electrical signal, but a physical signal generated by pressure changes of blood in an artery, but can be converted into an electrical signal by a sensor for measurement and recording. Currently, a blood pressure signal can be converted into an electrical signal by a cuff-type sphygmomanometer, a piezoresistive sensor and the like in medical equipment.
It should be noted that, data points of voltage values of each moment in a historical time period of the blood pressure monitoring device are obtained, the time interval between adjacent moments is extremely small, and a curve obtained by performing curve fitting on the data points in the historical time period is equivalent to an original blood pressure signal; the data value of a data point of the original blood pressure signal is the voltage value of the data point at the corresponding moment.
There may be various types of noise in the original blood pressure signal, such as myoelectric noise, respiratory noise, and environmental noise, which can interfere with the accuracy and interpretation of the signal. If the filtering smoothing operation is only carried out on the original blood pressure signal, the noise removing effect in the original blood pressure signal is poor, and the subsequent analysis of the original blood pressure signal is influenced. Therefore, the invention performs denoising processing by adding noise to the original blood pressure signal.
A signal type dividing module 102, configured to divide a blood pressure original signal into at least two local signal segments; the local signal segments are classified into two types according to the discrete distribution of the differences between the data values of the adjacent extreme points of each local signal segment.
Signals with different waveforms in the original blood pressure signal, such as a systolic waveform, appear as a peak in the blood pressure waveform; a diastolic waveform, which appears as a lower valley point in the blood pressure waveform; pulse waveforms, typically appear as a complete waveform cycle in a blood pressure waveform. The original blood pressure signal is divided into a plurality of local signal segments, and the waveforms of the local signal segments are analyzed, so that the medical staff can be helped to observe and evaluate the blood pressure change. Dividing the original blood pressure signal into a plurality of local signal segments, wherein the specific method is as follows:
and carrying out smoothing treatment on the original blood pressure signal to obtain a mean value segment, and taking a signal segment between the data points corresponding to two extreme points on the original blood pressure signal adjacent to the mean value segment as a local signal segment.
In the embodiment of the invention, mean filtering is selected to carry out smoothing treatment on the original blood pressure signal, and in other embodiments of the invention, median filtering, gaussian filtering and the like can be used for treatment.
It should be noted that, the time corresponding to each data point on the mean value segment has a corresponding data point in the original blood pressure signal, that is, the corresponding time of the original blood pressure signal and the corresponding data point on the mean value segment are the same.
The decomposition process of the empirical mode decomposition (Empirical Mode Decomposition, EMD) method mainly decomposes the high frequency component in the original signal into the front IMF component and the low frequency component into the rear IMF component, but there may be a case where the low frequency component is decomposed into the front high frequency component signal in the decomposition process, resulting in excessive concentration of energy in the high frequency part, reducing the energy of the high frequency component representing real details. If the low frequency component is erroneously decomposed into the high frequency IMF component, the frequency characteristics of the signal are prone to inaccuracy, and further analysis and processing of the signal can be adversely affected. In order to ensure that a lower-frequency signal in the original blood pressure signal can be better decomposed into a low-frequency component signal, the stability and the repeatability of decomposition are improved, and noise is added to the original blood pressure signal, so that the decomposition effect on different frequency components in the decomposition process of the original blood pressure signal is better.
The EMD method is a well known technique to those skilled in the art, and will not be described herein.
And if burrs generated by noise exist on the surface of the local signal segment, whether the burr features obviously influence the accuracy of EMD decomposition or not, and whether the burrs are obvious or not is reflected by adjacent extreme points of the local signal segment, the local signal segment is divided according to the discrete distribution condition of the difference between the data values of the adjacent extreme points of the local signal segment.
Preferably, the method for classifying the local signal segments is as follows: acquiring a burr characteristic significant value of each local signal segment according to the discrete distribution condition of the difference between the data values of two adjacent extreme points of each local signal segment; taking the average value of the burr characteristic significant values of all the local signal segments as a burr threshold; and taking the local signal segment with the burr characteristic significant value smaller than the burr threshold value as a first type of local signal segment, and taking the local signal segment with the burr characteristic significant value larger than or equal to the burr threshold value as a second type of local signal segment.
The specific acquisition method of the burr characteristic significant value comprises the following steps: taking the absolute value of the difference between the data values of two adjacent extreme points of each local signal segment as the height difference of each local signal segment; taking the height difference with the same value as the same height difference; and acquiring the burr characteristic significant value of each local signal segment by combining the discrete distribution condition of each type of height difference of each local signal segment and the difference between each height difference.
As an example, if the height difference of one local signal segment is 2, 1, 4 in order, the local signal segment has three types of height differences, and the number of height differences in the local signal segment is equal to the number of extreme points of the local signal segment minus 1.
The calculation formula of the burr feature significance value of each local signal segment is as follows:
wherein W is the burr characteristic significant value of each local signal section; n is the number of categories of the height difference of each local signal segment;probability of occurrence of the nth level difference for each local signal segment; />The average value of the probability of occurrence of all the height differences of each local signal segment; z is the number of height differences for each local signal segment; />The z-th height difference for each local signal segment; />For the average of all height differences of each local signal segmentThe method comprises the steps of carrying out a first treatment on the surface of the a is a preset positive number, takes an empirical value of 0.01, and plays a role in preventing the denominator from being 0 to cause meaningless division; />As a hyperbolic tangent function.
The kurtosis of the signal amplitude distribution in the local signal section is represented, and the kurtosis of the signal amplitude in the local signal section is close to 0 only when the amplitude of the original blood pressure signal is not changed or is uniformly distributed, namely the height difference is 0; when each type of height difference has smaller change, the kurtosis of the signal amplitude in the local signal section changes, and the greater the height difference of each type is, the greater the kurtosis of the signal amplitude in the local signal section is, the more obvious the burr characteristic represented by the original blood pressure signal is, and the greater the burr characteristic significant value W is.
But the glitch feature of the signal is not well presented when the remaining signal amplitude is uniformly distributed. Therefore, the signal fluctuation degree of the local signal section needs to be considered, the height difference of the local signal section presents the signal fluctuation condition, and when the signal fluctuation degree is more discrete, the more obvious the burr characteristics of the signal surface in the local signal section are, namely whenWhen the height difference of the local signal section is larger, the height difference of the local signal section is more discrete, the signal fluctuation condition in the local signal section is larger, the burr characteristic of the surface of the local signal section is more obvious, and the burr characteristic significant value W is larger.
The greater the burr feature significant value W, the more obvious the burr feature of the local signal section is, and the burr information on the surface of the local signal section can be better decomposed in the EMD decomposition process; the smaller the burr feature significance value W, the less obvious the burr feature of the local signal section surface is, and the burr feature may be mixed in the mean envelope line and be removed poorly.
Taking the average value of the burr feature significance values of all the local signal segments as a burr threshold, taking the local signal segments with the burr feature significance values smaller than the burr threshold as a first type of local signal segments, wherein the surface of the local signal segments of the first type has weaker burr features; and taking the local signal section with the burr characteristic significance value larger than or equal to the burr threshold value as a second type of local signal section, wherein the surface of the local signal section of the second type has stronger burr characteristic.
A noise compensation value obtaining module 103, configured to obtain a noise compensation value according to a difference between discrete distribution situations of differences between data values of adjacent extreme points of each type of local signal segment.
The modal decomposition process is always repeated to remove the mean envelope curve, so that component signals with different levels and components are obtained, and because the mean envelope curve contains differences among different signal components of the original blood pressure signal, the differences can be reserved in the component signals with different levels, and when the mean envelope curve at a signal section with higher noise density and lower intensity in the original blood pressure signal is too gentle, the mean envelope curve cannot contain the differences of the original blood pressure signal, and aliasing of different noise components can be caused after the mean envelope curve is removed. Therefore, when the intensity or position of noise added to the original blood pressure signal is inappropriate, the more easily the signal segment with smooth envelope is generated in the decomposition process, resulting in errors in the decomposition of the original blood pressure signal. The invention determines the noise intensity of the added noise in the original blood pressure signal, namely, obtains the noise compensation value.
Preferably, the specific acquisition method of the noise compensation value is as follows: acquiring signal noise intensity characteristic values of each type of local signal segment according to the discrete distribution condition of the height difference of each type of local signal segment; the absolute value of the difference between the signal noise intensity characteristic values of the two types of local signal segments is used as a noise compensation value.
It should be noted that in the embodiment of the present invention, the local signal segments of the same type are distributed continuously. The number of height differences for each type of local signal segment is equal to the number of extreme points of each type of local signal segment minus 1.
And the discrete distribution condition of the height difference of each type of local signal segment presents the signal noise intensity of each type of local signal segment, and then the signal noise intensity characteristic value of each type of local signal segment is obtained according to the discrete distribution condition of the height difference of each type of local signal segment. The calculation formula of the signal noise intensity characteristic value is as follows:
in the method, in the process of the invention,signal noise intensity characteristic values for the i-th type of local signal segment; />The number of level differences for the i-th type of local signal segment; />The f-th height difference that is the i-th type of local signal segment; />Is the average of all the height differences of the i-th type of local signal segment.
The more discrete the height difference distribution of each type of local signal segment, the greater the intensity of noise that accounts for each type of local signal segment, the noise intensity characteristic valueThe larger. Wherein, i has values of 1 and 2.
The local signal section with strong burr features has better decomposition effect in the EMD decomposition process, can decompose burrs into high-frequency signal components better, and the local signal section with weak burr features can decompose low-frequency information into high-frequency signal components easily. Therefore, the invention obtains the noise value of adding noise to the original blood pressure signal, namely the noise compensation value, based on the difference between the noise intensity characteristic values of the local signal segment with the weak burr characteristic and the local signal segment with the strong burr characteristic. The noise value of the noise represents the noise intensity.
Preferably, the specific acquisition method of the noise compensation value is as follows: the absolute value of the difference between the signal noise intensity characteristic values of the two types of local signal segments is used as a noise compensation value.
The signal denoising module 104 is configured to add white noise with a noise value being a noise compensation value to the blood pressure original signal, and denoise the blood pressure original signal to which the white noise is added.
Specifically, noise with a noise value being a noise compensation value is added to the original blood pressure signal by using the (Ensemble Empirical Mode Decomposition, EEMD) method, so that after noise is added to a signal segment with lower original signal noise intensity, the mean envelope curve at the signal segment with lower original noise intensity is not too gentle, and more differences of the original blood pressure signal are contained. In the process of decomposing the original blood pressure signal by using EMD, the original low-frequency signal can be better decomposed into low-frequency signal components, and the decomposition effect of high-frequency information in the original blood pressure signal can not be greatly influenced. The EEMD method is a technology known to those skilled in the art, and is not described herein.
It should be noted that, the noise added to the original blood pressure signal may be uniformly distributed noise, that is, white noise and gaussian noise. The noise value of the noise is the noise intensity, and in the embodiment of the invention, the value of the voltage value.
Decomposing the original blood pressure signal added with noise by using an EMD method to obtain initial component signals with different frequencies; respectively filtering the initial component signals with different frequencies to obtain denoising component signals with each frequency; and superposing the denoising component signals according to the sequence from low frequency to high frequency pairs to obtain denoised blood pressure original signals.
When decomposing the blood pressure original signal after adding the signal, the blood pressure original signal is decomposed from high frequency to low frequency.
In the embodiment of the invention, the average filtering algorithm is selected to filter the initial component signal of each frequency, so that the noise signal in the initial component signal is effectively removed, and in other embodiments of the invention, median filtering, gaussian filtering and the like can be used for processing, and the method is not described in detail.
The denoised blood pressure original signal can help medical staff to more accurately observe the change of the blood pressure signal, and necessary intervention measures are adopted according to the detection result. The blood penetration information management system collects and stores accurate blood penetration data, allows a user to access and manage blood penetration related data in real time through the Internet or an internal local area network and the like, and helps medical staff evaluate treatment effects, monitor physiological indexes of patients remotely and respond timely.
The present invention has been completed.
In summary, in the embodiment of the present invention, the acquired original blood pressure signal is divided into a plurality of local signal segments; dividing the local signal segments into two types according to the discrete condition of the difference between the data values of the adjacent extreme points of each local signal segment; acquiring a noise compensation value according to the difference between the discrete distribution conditions of the difference between the data values of the adjacent extreme points of each type of local signal segment; and adding noise with the noise value being the noise compensation value to the original blood pressure signal, and denoising the original blood pressure signal after adding the noise. According to the invention, noise is added to the original blood pressure signal, so that the low-frequency information in the original blood pressure signal is reduced and decomposed into high-frequency component signals, and the effectiveness of removing the signal noise in the original blood pressure signal is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. An on-line blood-permeable information management system, the system comprising:
the data acquisition module is used for acquiring original blood pressure signals;
the signal type dividing module is used for dividing the original blood pressure signal into at least two local signal segments; dividing the local signal segments into two types according to the discrete distribution condition of the difference between the data values of the adjacent extreme points of each local signal segment;
the noise compensation value acquisition module is used for acquiring a noise compensation value according to the difference between the discrete distribution conditions of the difference between the data values of the adjacent extreme points of each type of local signal segment;
the signal denoising module is used for adding noise with a noise value being a noise compensation value to the original blood pressure signal and denoising the original blood pressure signal after adding the noise;
the method for dividing the local signal segments into two types according to the discrete distribution condition of the difference between the data values of the adjacent extreme points of each local signal segment comprises the following steps:
acquiring a burr characteristic significant value of each local signal segment according to the discrete distribution condition of the difference between the data values of two adjacent extreme points of each local signal segment;
taking the average value of the burr characteristic significant values of all the local signal segments as a burr threshold;
taking the local signal section with the burr characteristic significant value smaller than the burr threshold value as a first type of local signal section, and taking the local signal section with the burr characteristic significant value larger than or equal to the burr threshold value as a second type of local signal section;
the method for obtaining the burr characteristic significant value of each local signal segment according to the discrete distribution condition of the difference between the data values of two adjacent extreme points of each local signal segment comprises the following steps:
taking the absolute value of the difference between the data values of two adjacent extreme points of each local signal segment as the height difference of each local signal segment; taking the height difference with the same value as the same height difference;
combining the discrete distribution condition of each type of height difference of each local signal segment and the difference between each height difference to obtain a burr characteristic significant value of each local signal segment;
the calculation formula of the burr characteristic significant value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein W is the burr characteristic significant value of each local signal section; n is the number of categories of the height difference of each local signal segment; />Probability of occurrence of the nth level difference for each local signal segment; />The average value of the probability of occurrence of all the height differences of each local signal segment; z is the number of height differences for each local signal segment; />The z-th height difference for each local signal segment; />The average value of all the height differences of each local signal segment; a is a preset positive number; />As a hyperbolic tangent function.
2. The system of claim 1, wherein the method for dividing the original blood pressure signal into at least two partial signal segments comprises:
and carrying out smoothing treatment on the original blood pressure signal to obtain a mean value segment, and taking a signal segment between the data points corresponding to two extreme points on the original blood pressure signal adjacent to the mean value segment as a local signal segment.
3. The system of claim 1, wherein the method for obtaining the noise compensation value comprises:
acquiring signal noise intensity characteristic values of each type of local signal segment according to the discrete distribution condition of the height difference of each type of local signal segment;
and taking the absolute value of the difference between the signal noise intensity characteristic values of the two types of local signal segments as a noise compensation value.
4. An on-line blood-permeability information management system according to claim 3, wherein the signal noise intensity characteristic value is calculated as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Signal noise intensity characteristic values for the i-th type of local signal segment; />The number of level differences for the i-th type of local signal segment; />The f-th height difference that is the i-th type of local signal segment; />Is the average of all the height differences of the i-th type of local signal segment.
5. The system of claim 1, wherein the method for adding noise with noise compensation to the original blood pressure signal comprises:
and adding noise with a noise value being a noise compensation value to the original blood pressure signal by using an EEMD algorithm.
6. The system of claim 1, wherein the method for denoising the noise-added blood pressure raw signal comprises:
decomposing the original blood pressure signal added with noise by using an EMD method to obtain initial component signals with different frequencies; respectively filtering the initial component signals with different frequencies to obtain denoising component signals with each frequency; and superposing the denoising component signals according to the sequence from low frequency to high frequency pairs to obtain denoised blood pressure original signals.
7. The system of claim 1, wherein the noise added to the raw blood pressure signal is gaussian.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108804832A (en) * | 2018-06-14 | 2018-11-13 | 东南大学 | A kind of interval threshold Denoising of Partial Discharge based on VMD |
| CN110096956A (en) * | 2019-03-25 | 2019-08-06 | 中国地质大学（武汉） | Signal antinoise method and device based on EEMD and arrangement entropy second differnce |
| CN111396032A (en) * | 2020-04-23 | 2020-07-10 | 西安石油大学 | Method and device for correcting temperature drift and computer storage medium |
| CN113238190A (en) * | 2021-04-12 | 2021-08-10 | 大连海事大学 | Ground penetrating radar echo signal denoising method based on EMD combined wavelet threshold |
| CN116974401A (en) * | 2023-09-22 | 2023-10-31 | 深圳市柯达科电子科技有限公司 | LCD inductance touch screen interference signal data processing method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108804832A (en) * | 2018-06-14 | 2018-11-13 | 东南大学 | A kind of interval threshold Denoising of Partial Discharge based on VMD |
| CN110096956A (en) * | 2019-03-25 | 2019-08-06 | 中国地质大学（武汉） | Signal antinoise method and device based on EEMD and arrangement entropy second differnce |
| CN111396032A (en) * | 2020-04-23 | 2020-07-10 | 西安石油大学 | Method and device for correcting temperature drift and computer storage medium |
| CN113238190A (en) * | 2021-04-12 | 2021-08-10 | 大连海事大学 | Ground penetrating radar echo signal denoising method based on EMD combined wavelet threshold |
| CN116974401A (en) * | 2023-09-22 | 2023-10-31 | 深圳市柯达科电子科技有限公司 | LCD inductance touch screen interference signal data processing method |
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