Central venous pressure variability analysis method and device, terminal equipment and storage medium

Abstract

The invention discloses a central venous pressure variability analysis method and device, terminal equipment and a storage medium, relates to the technical field of biomedical engineering, and aims to solve the problem that a measurement result of the central venous pressure variability is influenced by respiratory cycle fluctuation. The method comprises the following steps: collecting a light intensity attenuation signal when near-infrared light passes through human tissue; preprocessing the light intensity attenuation signal to obtain a pure light signal; providing a findpeaks algorithm, analyzing the pure light signal by using the findpeaks algorithm to obtain a wave crest of the pure light signal, and defining an effective breathing cycle according to the wave crest; the findpeak valley intensity difference value of the maximum peak and the maximum trough of the pure optical signal in an effective breathing cycle is analyzed through the findpeak algorithm, and the central venous pressure variability is calculated according to the light peak valley intensity difference value.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical engineering, and particularly to a method, device, terminal device and storage medium for analyzing central venous pressure variability. Background Art

[0002] Central venous pressure variability (CVPV) refers to the fluctuation amplitude of central venous pressure (CVP) during the mechanical ventilation process with the respiratory cycle. Central venous pressure is closely related to volume management, and volume management is the most important factor in maintaining the internal environment stability during the perioperative period. In recent years, more and more studies have shown that goal-directed fluid therapy (GDFT) can provide individualized treatment for patients, prevent potential hypervolemia or hypovolemia in perioperative patients, and reduce complications or mortality. According to the Frank-Starling curve, the preload of the ventricle is proportional to the cardiac output (CO) in the ascending stage. However, if the preload reaches the plateau stage, fluid therapy will not produce the expected effect, but will lead to cardiac overload and tissue edema. Therefore, it is necessary to set monitoring indicators that can reflect the patient's vascular volume, perform individualized fluid replacement according to the changing fluid demand during the perioperative period, optimize the perioperative hemodynamics of the patient, prevent potential circulatory volume deficiency or excess in perioperative patients, and reduce postoperative complications and mortality.

[0003] The methods for measuring blood volume during surgery include: central venous pressure (CVP), pulmonary artery catheter (PAC), and pulmonary artery wedge pressure (PCWP). Although the pulmonary artery catheter is the gold standard for cardiac output detection, its operation is complex and there are many complications, so its clinical application is limited.

[0004] Traditional CVPV measurement relies on invasive central venous catheters, which have risks such as infection and bleeding, and cannot be continuously monitored in real time. In non-invasive CVPV monitoring technologies, problems such as insufficient accuracy or complex operation exist in bioimpedance method, ultrasound method, etc. Near-infrared spectroscopy (NIRS) shows potential in CVP monitoring due to its non-invasive, real-time, portable and other advantages. However, the existing NIRS technology mainly focuses on the measurement of the absolute value of CVP, lacks the analysis of the respiratory cycle fluctuation, and has not established a standardized method for calculating CVPV. The market urgently needs non-invasive measurement of central venous pressure variability to replace invasive central venous pressure variability. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, terminal device and storage medium for analyzing central venous pressure variability, which are used to provide a non-invasive detection method and a standardized calculation method for central venous pressure that conforms to the respiratory cycle.

[0006] In the first aspect, a method for analyzing central venous pressure variability provided by the present invention adopts the following technical solution: A method for analyzing central venous pressure variability, comprising: Collecting the light intensity attenuation signal when near-infrared light passes through human tissue; Preprocessing the light intensity attenuation signal to obtain a pure light signal; Providing a findpeaks algorithm, using the findpeaks algorithm to analyze the pure light signal, obtaining the peaks of the pure light signal, and defining an effective respiratory cycle according to the peaks; Using the findpeaks algorithm to analyze the light peak-valley intensity difference between the maximum peak and the maximum valley of the pure light signal within an effective respiratory cycle, and calculating the central venous pressure variability according to the light peak-valley intensity difference.

[0007] A further technical solution lies in that the preprocessing of the light intensity attenuation signal to obtain a pure light signal specifically includes: Performing wavelet decomposition on the light intensity attenuation signal to remove the baseline drift in the light intensity attenuation signal, obtaining a first light attenuation signal; Denosing the first light attenuation signal by wavelet hard threshold denoising to obtain a second light attenuation signal; Calculating the sampling frequency of the light intensity attenuation signal, and combining the median filtering method to suppress the motion artifacts in the second light attenuation signal to obtain a pure light signal.

[0008] A further technical solution lies in that the defining of the effective respiratory cycle according to the peaks specifically includes: Selecting peak pairs with a light intensity difference of 50 ≤ I ≤ 100 between adjacent peaks, and defining the time interval therebetween as the effective respiratory cycle; where I is the light intensity difference between adjacent peaks.

[0009] A further technical solution lies in that the calculation of the central venous pressure variability according to the light peak-valley intensity difference adopts the following formula:

[0010] Where CVPV is the central venous pressure variability, PPmax is the light intensity difference between the maximum peak and the maximum valley within a single respiratory cycle, and PPmin is the light intensity difference between the minimum peak and the minimum valley within a single respiratory cycle.

[0011] Compared with the prior art, a method for analyzing central venous pressure variability provided by the present invention has the following beneficial effects: CVPV reflects the patient's volume status and right heart function and is a key indicator for guiding fluid therapy. Compared with pulse variability, CVPV focuses more on reflecting the changes in central venous pressure during the respiratory cycle. From the perspective of the central venous system, it is not affected by factors such as peripheral vascular resistance and vascular elasticity that interfere with arterial waveforms, and has unique advantages in evaluating cardiac filling status. Especially when there are different respiratory depths and frequencies in patients, or when arterial pressure waveforms are distorted due to severe peripheral vascular diseases or the use of vasoactive drugs, CVPV can still stably and accurately evaluate the patient's volume status and provide a reliable basis for the fluid resuscitation strategy.

[0012] In a second aspect, the present invention further provides a central venous pressure variability analysis device, including: An acquisition unit for acquiring the light intensity attenuation signal when near-infrared light passes through human tissues; A preprocessing unit for preprocessing the light intensity attenuation signal to obtain a pure light signal; A calculation unit for providing a findpeaks algorithm, using the findpeaks algorithm to analyze the pure light signal to obtain the peaks of the pure light signal, and defining an effective respiratory cycle based on the peaks; using the findpeaks algorithm to analyze the difference in light peak-valley intensity between the maximum peak and the maximum valley of the pure light signal within an effective respiratory cycle, and calculating the central venous pressure variability based on the difference in light peak-valley intensity.

[0013] In a third aspect, the present invention further provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The terminal device is characterized in that when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program. The computer-readable storage medium is characterized in that when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

[0015] Compared with the prior art, the beneficial effects of the device, terminal device, and storage medium provided by the present invention are the same as those of the central venous pressure variability analysis method described in the above technical solution, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1Schematic diagram of the cardiopulmonary interaction mechanism for pulse pressure variation; Figure 2 Comparison chart of changes in cardiac output and near-infrared waveforms; Figure 3 Flowchart of a method provided by an embodiment of the present invention; Figure 4 Waveform diagram of central venous pressure signal before filtering in an embodiment of the present invention; Figure 5 Waveform diagram of central venous pressure signal after filtering in an embodiment of the present invention; Figure 6 Comparison chart of power spectral density before and after denoising in an embodiment of the present invention; Figure 7 Schematic diagram of the measurement principle of photoplethysmogram; Figure 8 Waveform diagram for respiratory cycle recognition in an actual case of the present invention; Figure 9 Analysis diagram of central venous pressure variability within the respiratory cycle in an actual case of the present invention. Detailed implementation manners

[0017] For the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and do not limit their sequence. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and the terms "first" and "second" do not necessarily limit being different.

[0018] It should be noted that in the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific manner.

[0019] First, a brief introduction to the reasons for the generation of central venous pressure variability: The variability of central venous pressure (CVP) (CVP variability, ΔCVP) refers to the fluctuation amplitude of CVP during the respiratory cycle, reflecting the change of right atrial pressure with respiratory movement. The core reason for its generation is closely related to the periodic influence of respiration on venous return and right heart output.

[0020] The essence of the variability of central venous pressure is the change in stroke volume of the respiratory system. During inspiration, the pressure in the pleural cavity increases. The increased pressure is transmitted to the right atrium because the wall of the right atrium is very thin and in direct contact with the pleura as shown in the figure. The increase in right atrial pressure (RAP) reduces the pressure gradient of systemic venous return, and the preload of the right ventricle also decreases accordingly. If the right ventricle responds to the preload, its cardiac output will decrease. This decrease is transmitted to the left ventricle, and its preload also decreases accordingly. Due to the pulmonary transit time, this decrease occurs during expiration. Conversely, if the left ventricle responds to the preload, the decrease in preload will lead to a decrease in its cardiac output, and the cardio-pulmonary interaction mechanism of pulse pressure variation is as Figure 1 shown.

[0021] When the venous volume output is excessive, the venous waveform has large fluctuations. When the venous volume output tends to be stable, the venous waveform has smaller fluctuations. Therefore, doctors can judge the patient's physiological condition through the variability of the venous waveform. The relationship between cardiac output and the change of near-infrared waveform is as Figure 2 shown.

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0023] The present invention relates to a method for monitoring and processing central venous pressure signals and central venous pressure variability of a non-invasive central venous pressure monitor. The monitoring device includes a central venous pressure acquisition probe and a central venous pressure data monitoring platform, which is suitable for providing central venous pressure signals of a monitoring object. The data is stored as a numerical curve that changes with sampling points, and further programming is performed to process the acquired data to obtain a standard signal. The programming involves a non-invasive central venous pressure variability calculation method based on near-infrared spectroscopy.

[0024] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] Referring to Figure 3 , a central venous pressure variability analysis method, the main process includes the following steps: Step S1: Collect the light intensity attenuation signal when near-infrared light passes through human tissue; Step S2: Preprocess the light intensity attenuation signal to obtain a pure light signal.

[0026] Among them, the preprocessing of the light intensity attenuation signal to obtain a pure light signal specifically includes: Step S21: Perform wavelet decomposition on the light intensity attenuation signal to remove the baseline drift in the light intensity attenuation signal, and obtain a first light attenuation signal.

[0027] Specifically, use the wavedec function to perform L-layer (L = 8) wavelet decomposition on the light intensity attenuation signal to obtain decomposition coefficients c and the lengths of each layer l . The mathematical expression is:

[0028] In the formula, is the detail coefficient of the j-th layer, is the approximation coefficient of the L-th layer, is the wavelet basis function, is the scaling function.

[0029] Use the wrcoef function to reconstruct the approximation component of the L-th layer as the baseline. The mathematical expression is:

[0030] In the formula, b(n) is the baseline, a L (k) is the approximation coefficient of the L-th layer, is the scaling function.

[0031] Subtract the baseline from the original light intensity attenuation signal to obtain the first light attenuation signal. Among them, the first light attenuation signal is the signal after removing the baseline drift from the light intensity attenuation signal, and the expression is:

[0032] In the formula, is the first light attenuation signal, is the light intensity attenuation signal, is the baseline.

[0033] Step S22: Denoise the first light attenuation signal through wavelet hard threshold denoising to obtain a second light attenuation signal.

[0034] Specifically, decompose the first light attenuation signal through the sym3 wavelet basis to obtain wavelet coefficients of each layer, and then calculate the threshold through the sqtwolog rule. The expression is:

[0035] In the formula, is the threshold, N is the signal length, is the noise standard deviation.

[0036] Perform hard threshold processing on the wavelet coefficients to obtain the processed coefficients. The expression is:

[0037] In the formula, is the wavelet coefficient, is the processed coefficient.

[0038] Reconstruct the signal using the processed coefficient to obtain the second optical attenuation signal. Among them, the second optical attenuation signal is the denoised signal.

[0039] Step S23: Calculate the sampling frequency of the optical intensity attenuation signal, and combine the median filtering method to suppress the motion artifacts in the second optical attenuation signal to obtain a pure optical signal.

[0040] Specifically, the sampling frequency of the optical intensity attenuation signal is the number of times the signal is sampled per unit time, which reflects the density of signal acquisition. If the total signal acquisition duration (unit: second) and the total number of samples collected are known, the sampling frequency calculation expression is:

[0041] In the formula, is the sampling frequency, N is the total number of samples collected, and T is the total acquisition duration.

[0042] According to the sampling frequency calculate the window size corresponding to 80 ms ; perform median filtering on the second optical attenuation signal to obtain a pure optical signal, and the expression is:

[0043] In the formula, is the second optical attenuation signal, is the pure optical signal , median represents the median operation.

[0044] The multi-stage joint signal denoising method systematically solves complex interference problems such as baseline drift, noise, and motion artifacts by processing biomedical signals in stages. Through the cooperation of multi-stage technologies, the multi-resolution analysis of wavelet transform is combined with the non-linear characteristics of median filtering to cover various types of noise such as low-frequency trends, high-frequency noise, and pulse interference, and solves the limitations of a single algorithm. The waveforms of the central venous pressure signal before and after filtering are as shown in Figure 4 and Figure 5 shown, and the comparison of the power spectral density before and after denoising is as shown in Figure 6 shown.

[0045] Step S3: Provide the findpeaks algorithm, use the findpeaks algorithm to analyze the pure optical signal to obtain the peaks of the pure optical signal, and define an effective respiratory cycle according to the peaks.

[0046] Among them, the specific effective breathing cycle defined according to the wave peak is: select wave peak pairs with the difference in light intensity between adjacent wave peaks 50 ≤ I ≤ 100, and define their time interval as the effective breathing cycle. ; where I is the difference in light intensity between adjacent wave peaks. For example, the light intensities of adjacent wave peaks are and , and the corresponding times are and . When , the effective breathing cycle is .

[0047] Step S4: Use the findpeaks algorithm to analyze the difference in light intensity between the maximum wave peak and the maximum wave valley of the pure light signal within an effective breathing cycle, and calculate the central venous pressure variability according to the difference in light intensity between the wave peak and the wave valley.

[0048] Specifically, use the findpeaks algorithm to locate wave peaks with a minimum peak spacing and a minimum peak height of 0.2, and determine the position of the wave valley by searching backward for the local minimum of the signal.

[0049] Within a single breathing cycle, the maximum difference in light intensity between the wave peak and the wave valley is denoted as PPmax, and the minimum difference in light intensity between the wave peak and the wave valley is denoted as PPmin. Then, the central venous pressure variability based on near-infrared spectroscopy is:

[0050] In the formula, CVPV is the central venous pressure variability, PPmax is the difference in light intensity between the maximum wave peak and the maximum wave valley within a single breathing cycle, and PPmin is the difference in light intensity between the minimum wave peak and the minimum wave valley within a single breathing cycle.

[0051] The following is an illustration with an actual case: The patient is in a supine position and a near-infrared spectroscopy central venous pressure optical probe is used. Measure the original waveform of the photoplethysmogram of the patient's neck. The measurement principle of the photoplethysmogram is as Figure 7 shown.

[0052] Preprocess the collected central venous pressure photoplethysmogram waveform and identify the characteristic points of the wave peaks and wave valleys; Respiratory cycle frequency identification: Select wave peak pairs with the difference in light intensity between adjacent wave peaks 50 ≤ I ≤ 100 (light intensity unit), and define their time interval as the effective breathing cycle; the division of the processed pure light signal and the breathing cycle is as Figure 8 shown; Find the maximum peak-to-valley difference and the minimum peak-to-valley difference within each respiratory cycle; use the formula to calculate the central venous pressure variability for each cycle. The analysis results of the respiratory cycle and the central venous pressure variability are as Figure 9 described and summarized in the following table.

[0053] Table of respiratory cycle and CVPV data: Respiratory cycle Time period (s) Central venous pressure variability CVPV (%) Respiratory cycle Time period (s) Central venous pressure variability CVPV (%) Cycle 1 0.0-3.0s CVPV = 18.24% Cycle 13 66.1-67.9s CVPV = 22.08% Cycle 2 3.1-6.2s CVPV = 17.13% Cycle 14 67.9-69.8s CVPV = 22.16% Cycle 3 6.2-7.9s CVPV = 17.64% Cycle 15 69.8-71.5s CVPV = 22.57% Cycle 4 7.9-9.6s CVPV = 17.89% Cycle 16 71.5-73.5s CVPV = 22.69% Cycle 5 9.6-11.3s CVPV = 18.79% Cycle 17 73.5-75.4s CVPV = 23.26% Cycle 6 11.3-16.8s CVPV = 18.56% Cycle 18 75.4-77.1s CVPV = 25.28% Cycle 7 16.8-26.5s CVPV = 18.69% Cycle 19 77.1-80.1s CVPV = 24.26% Cycle 8 26.5-35.6s CVPV = 19.01% Cycle 20 80.1-82.2s CVPV = 22.26% Cycle 9 35.6-44.9s CVPV = 19.32% Cycle 21 82.2-87.6s CVPV = 21.33% Cycle 10 44.9-53.4s CVPV = 20.11% Cycle 22 87.6-93.9s CVPV = 20.29% Cycle 11 53.4-61.9s CVPV = 20.56% Cycle 23 93.9-95.6s CVPV = 18.24% Cycle 12 61.9-66.1s CVPV = 21.72% Cycle 24 95.6-97.3s CVPV = 16.68% Average central venous pressure variability (CVPV): 19.95%. The above data were measured when the patient was in the supine position. Respiratory cycle 10 - respiratory cycle 18 was the stage when the central venous pressure increased during abdominal compression of the subject, and respiratory cycle 18 - 24 was the stage when the central venous pressure decreased after the compression ended.

[0054] CVPV reflects the patient's volume status and right heart function and is a key indicator for guiding fluid therapy. Compared with pulse variability, CVPV focuses more on reflecting the changes in central venous pressure during the respiratory cycle, directly correlates with the fluctuations in right atrial pressure, and can more accurately reflect the cardiac preload and volume responsiveness. Pulse variability mainly monitors the changes in arterial pulse waves and indirectly reflects the fluctuations in stroke volume; while CVPV starts from the perspective of the central venous system, is not affected by factors such as peripheral vascular resistance and vascular elasticity that interfere with the arterial waveform, and has unique advantages in evaluating the cardiac filling status. Especially in the case of severe peripheral vascular diseases and the use of vasoactive drugs leading to distorted arterial pressure waveforms, CVPV can still stably and accurately evaluate the patient's volume status and provide a reliable basis for the fluid resuscitation strategy. In addition, CVPV is directly related to right heart function and can more sensitively capture the impact of changes in right heart pump function on central venous pressure. When guiding fluid therapy for patients with right heart failure, it has stronger pertinence and clinical value than pulse variability.

[0055] This embodiment of the present invention also discloses a central venous pressure variability analysis device, including: An acquisition unit for acquiring the light intensity attenuation signal when near-infrared light passes through human tissue; A preprocessing unit for preprocessing the light intensity attenuation signal to obtain a pure light signal; A calculation unit for providing the findpeaks algorithm, using the findpeaks algorithm to analyze the pure light signal to obtain the peaks of the pure light signal, and defining an effective respiratory cycle based on the peaks; using the findpeaks algorithm to analyze the difference in light peak-to-valley intensity between the maximum peak and the maximum valley within an effective respiratory cycle of the pure light signal, and calculating the central venous pressure variability based on the difference in light peak-to-valley intensity.

[0056] An embodiment of the present invention also discloses a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the methods described above are implemented.

[0057] An embodiment of the present invention also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the above embodiment are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0058] The computer-readable storage medium can be an internal storage unit of the terminal in any of the foregoing embodiments, such as the hard disk or memory of the terminal. The computer-readable storage medium can also be an external storage device of the terminal, such as a plug-in hard disk equipped on the terminal, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the terminal. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0059] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0060] In several embodiments provided in the present application, it should be understood that the disclosed terminals and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces or units, and can also be electrical, mechanical, or other forms of connection.

[0061] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0062] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0063] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for analyzing central venous pressure variability, characterized in that Including: Collecting the light intensity attenuation signal when near-infrared light passes through human tissue; Preprocessing the light intensity attenuation signal to obtain a pure light signal; Providing a findpeaks algorithm, analyzing the pure light signal by using the findpeaks algorithm to obtain the peaks of the pure light signal, and defining an effective respiratory cycle according to the peaks; Analyzing the light peak-valley intensity difference between the maximum peak and the maximum valley of the pure light signal within an effective respiratory cycle by using the findpeaks algorithm, and calculating the central venous pressure variability according to the light peak-valley intensity difference.

2. The method for analyzing central venous pressure variability according to claim 1, characterized in that, The preprocessing the light intensity attenuation signal to obtain a pure light signal specifically includes: Performing wavelet decomposition on the light intensity attenuation signal to remove the baseline drift in the light intensity attenuation signal and obtain a first light attenuation signal; Denosing the first light attenuation signal by wavelet hard threshold denoising to obtain a second light attenuation signal; Calculating the sampling frequency of the light intensity attenuation signal, and combining the median filtering method to suppress the motion artifacts in the second light attenuation signal to obtain a pure light signal.

3. The central venous pressure variability analysis method according to claim 1, characterized in that The defining an effective respiratory cycle according to the peaks specifically includes: Selecting peak pairs with the light intensity difference between adjacent peaks 50≤I≤100, and defining the time interval between them as the effective respiratory cycle; where I is the light intensity difference between adjacent peaks.

4. A method for analyzing central venous pressure variability according to claim 1, wherein The calculating the central venous pressure variability according to the light peak-valley intensity difference adopts the following formula: ； Where CVPV is the central venous pressure variability, PPmax is the light intensity difference between the maximum peak and the maximum valley within a single respiratory cycle, and PPmin is the light intensity difference between the minimum peak and the minimum valley within a single respiratory cycle.

5. A central venous pressure variability analysis device, characterized in that, Including: A collecting unit for collecting the light intensity attenuation signal when near-infrared light passes through human tissue; A preprocessing unit for preprocessing the light intensity attenuation signal to obtain a pure light signal; A calculating unit for providing a findpeaks algorithm, analyzing the pure light signal by using the findpeaks algorithm to obtain the peaks of the pure light signal, and defining an effective respiratory cycle according to the peaks; analyzing the light peak-valley intensity difference between the maximum peak and the maximum valley of the pure light signal within an effective respiratory cycle by using the findpeaks algorithm, and calculating the central venous pressure variability according to the light peak-valley intensity difference.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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