A method for defining a new tissue perfusion index based on the vital waveforms of a monitor

Abstract

The present invention relates to the technical field of waveform matching, and specifically relates to a method for defining a new tissue perfusion index based on the vital waveforms of a monitor. The method includes: collecting arterial blood pressure waveforms and pulse oximetry waveforms, performing digital filtering processing on the original waveforms, and aligning the two waveforms on the time axis; performing consistency matching on the arterial blood pressure waveforms and the pulse oximetry waveforms; in the same cardiac cycle, obtaining the first peak of the arterial blood pressure waveform and the second peak of the pulse oximetry waveform, and calculating a first interval between the time point corresponding to the second peak and the time point corresponding to the first peak; calculating the average value of multiple first intervals, which is denoted as the peripheral perfusion time; selecting multiple consecutive respiratory cycles, and calculating multiple peripheral perfusion times; calculating the average value of multiple peripheral perfusion times, which is denoted as the average perfusion time, and determining the state of the tissue perfusion index based on the average perfusion time, realizing the monitoring of the tissue perfusion state without additional equipment and using the existing monitoring waveforms.

Description

Technical Field

[0001] The present invention relates to the technical field of waveform matching, and particularly relates to a method for defining a new tissue perfusion index based on the vital waveforms of a monitor. Background Art

[0002] Tissue perfusion refers to the blood flow through the microcirculation and is crucial for maintaining cell metabolism and organ function. Many critical diseases, such as infection, trauma, blood loss, or heart failure, can lead to insufficient tissue perfusion, which in turn can cause organ failure. If not recognized and intervened in a timely manner, it may endanger the patient's life. Currently, clinically, the tissue perfusion status is mainly evaluated through indicators such as blood gas analysis, blood lactate, and central venous oxygen saturation. However, these methods either require repeated blood sampling and cannot be continuously monitored; or require invasive collection with complex operations; or the collected indicators have a relatively high delay in change.

[0003] In recent years, although new technologies such as capillary refill time and subcutaneous microcirculation imaging have begun to be used, these methods either rely on manual operation resulting in large measurement errors, or the equipment is expensive and requires professional training. Considering that almost every critical patient uses a vital sign monitoring system, which can simultaneously collect waveform data such as electrocardiogram, arterial blood pressure, and pulse oximetry, but currently, this basic waveform information has not been fully explored and utilized, and there is a lack of integrating multiple waveform information to form a quantitative index reflecting tissue perfusion. Therefore, based on the waveforms of existing monitoring devices, the present invention proposes a method for defining a new tissue perfusion index based on the vital waveforms of a monitor to achieve simple, continuous, and quantitative evaluation of the tissue perfusion status. Summary of the Invention

[0004] The purpose of the present invention is to address the problems of complex operations and lack of full utilization of existing collected data when evaluating the tissue perfusion status in the prior art. By providing a method for defining a new tissue perfusion index based on the vital waveforms of a monitor, based on the arterial blood pressure waveform and pulse oximetry waveform collected by the monitor, combined with the signal modulation principle, by obtaining the time delay in the same cardiac cycle from the two waveforms and calculating the average perfusion time of blood from the proximal end to the distal end, the real-time monitoring of the patient's tissue perfusion status is achieved.

[0005] To achieve the above-mentioned invention purpose, the present invention provides the following technical solutions:

[0006] A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, comprising:

[0007] Collecting the arterial blood pressure waveform and pulse oximetry waveform, performing digital filtering processing on the original waveforms, aligning the two waveforms on the time axis; performing consistency matching on the arterial blood pressure waveform and pulse oximetry waveform;

[0008] In the same cardiac cycle, obtain the first peak of the arterial blood pressure waveform and the second peak of the pulse oximetry waveform, and calculate the first interval between the time point corresponding to the second peak and the time point corresponding to the first peak; calculate the average value of multiple first intervals, denoted as the peripheral perfusion time;

[0009] Select multiple consecutive respiratory cycles, calculate multiple peripheral perfusion times; calculate the average value of multiple peripheral perfusion times, denoted as the average perfusion time, and determine the status of the tissue perfusion index based on the average perfusion time.

[0010] As a preferred technical solution of the present application, perform consistency matching, and the method includes:

[0011] Perform waveform matching on the arterial blood pressure waveform and the pulse oximetry waveform, determine the periodic characteristics from the matched arterial blood pressure waveform and pulse oximetry waveform, and determine the phase difference confidence interval according to the periodic characteristics;

[0012] In the phase difference confidence interval, perform phase matching on the arterial blood pressure waveform and the pulse oximetry waveform so that the first peak and the second peak correspond to the same cardiac cycle.

[0013] As a preferred technical solution of the present application, after calculating the average value of multiple peripheral perfusion times, it further includes: correcting the average perfusion time based on the heart rate, and the method includes: calculating the average heart rate in multiple consecutive respiratory cycles, and calculating the average cardiac cycle according to the average heart rate; taking the ratio of the average perfusion time to the average cardiac cycle as the peripheral perfusion index, and determining the status of the tissue perfusion index based on the peripheral perfusion index.

[0014] As a preferred technical solution of the present application, the waveform matching includes respiratory cycle matching, and the method includes:

[0015] Perform signal modulation on the arterial blood pressure waveform and extract the first respiratory envelope;

[0016] Perform signal modulation on the pulse oximetry waveform and extract the second respiratory envelope;

[0017] Construct the cross-correlation function of the first respiratory envelope and the second respiratory envelope, and calculate the cross-correlation result;

[0018] Preset a respiratory cycle matching threshold, compare the cross-correlation result with the respiratory cycle matching threshold, and determine the matching result of the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result.

[0019] As a preferred technical solution of the present application, the method of signal modulation includes:

[0020] Obtain the arterial blood pressure waveform or the pulse oximetry waveform, denoted as the real signal; convert the real signal into an analytic signal through linear transformation;

[0021] Take the absolute value of the analytical signal to obtain the instantaneous amplitude signal of the analytical signal;

[0022] Perform low-pass filtering on the instantaneous amplitude signal, and after removing the noise signal, obtain a smooth respiration envelope.

[0023] As a preferred technical solution of the present application, waveform matching includes cardiac cycle variability matching, and the method includes:

[0024] Calculate the time difference between adjacent wave peaks in the arterial blood pressure waveform, denoted as the second interval;

[0025] Calculate the time difference between adjacent wave peaks in the pulse oximetry waveform, denoted as the third interval;

[0026] Preset a cardiac cycle matching threshold; calculate the second difference between the second interval and the third interval, compare the absolute value of the second difference with the cardiac cycle matching threshold, and determine the matching result of the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result.

[0027] As a preferred technical solution of the present application, perform phase matching, and the method includes:

[0028] Preset a sliding window; in the sliding window, calculate the first wave peak of the arterial blood pressure waveform, denoted as the first amplitude, calculate the second wave peak of the pulse oximetry waveform, denoted as the second amplitude; obtain the time point corresponding to the first amplitude, denoted as the first wave peak position, and obtain the time point corresponding to the second amplitude, denoted as the second wave peak position;

[0029] Based on the first amplitude and the second amplitude, perform amplitude matching on the arterial blood pressure waveform and the pulse oximetry waveform;

[0030] Based on the first wave peak position and the second wave peak position, perform wave peak spacing matching on the arterial blood pressure waveform and the pulse oximetry waveform.

[0031] As a preferred technical solution of the present application, perform amplitude matching, and the method includes:

[0032] Obtain the lag amplitude of the second amplitude after passing through multiple time points in the waveform. Based on the multiple lag amplitudes and the first amplitude, use the least squares method to obtain multiple time points that minimize the error, denoted as candidate time points.

[0033] As a preferred technical solution of the present application, perform wave peak spacing matching, and the method includes:

[0034] Calculate the time interval between adjacent first wave peak positions, denoted as the blood pressure waveform interval; calculate the time interval between adjacent second wave peak positions, denoted as the blood oxygen waveform interval;

[0035] Obtain multiple blood oxygen waveform intervals corresponding to multiple candidate time points. Based on the multiple blood oxygen waveform intervals and blood pressure waveform intervals, use the least squares method to obtain multiple candidate time points that minimize the error, denoted as the optimal time points, and obtain multiple pairs of first peak positions and second peak positions corresponding to the cardiac cycle.

[0036] As a preferred technical solution of the present application, after calculating the average value of multiple peripheral perfusion times, it further includes:

[0037] Calculate the coefficient of variation of multiple peripheral perfusion times; compare the coefficient of variation with a preset data fluctuation threshold, and determine whether there are outliers in the multiple peripheral perfusion times according to the size of the comparison result.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. By analyzing the average perfusion time between the arterial blood pressure waveform and pulse oximetry waveform collected by the existing device, the present invention evaluates the tissue perfusion state based on the peripheral perfusion time, effectively solving the problem of complex operation of tissue perfusion state detection in the prior art. Furthermore, it realizes the use of the data of the existing monitoring device without additional equipment, achieving fully automatic and continuous monitoring, providing objective and quantitative evaluation indicators, with good result repeatability and low operator dependence. The present invention can be installed and stored in the existing monitoring device without purchasing special equipment, which can reduce the monitoring cost, improve the utilization rate of the existing device, and has significant popularization value.

[0040] 2. Based on the fact that there is a correlation between the heart rate, respiratory signal and arterial blood pressure waveform, and there is also a correlation between the heart rate, respiratory signal and pulse oximetry waveform, the present invention uses the signal processing principle to match the arterial blood pressure waveform and pulse oximetry waveform, so that the peaks of the two waveforms used to calculate the average perfusion time belong to the same cardiac cycle, improving the accuracy of the average perfusion time.

[0041] 3. The usage method of devices such as the vital sign monitor of the present invention is very simple and intuitive, facilitating the operation of front-line clinical medical staff. First, in the equipment preparation stage, ensure that the patient is correctly connected to the vital sign monitor, place the arterial catheter and pulse oximetry probe on the same upper limb, reduce the compression of the measured limb, and start the data acquisition software. Enter the basic patient information in the system and select a suitable display mode, such as the real-time value or trend graph of the average perfusion time, etc. The system will automatically start collecting and analyzing waveform data, updating every 5 respiratory cycles, and simultaneously dynamically displaying the trend changes of the average perfusion time and peripheral perfusion index. Medical staff can evaluate the patient's tissue perfusion state based on this and promptly detect abnormal changes in hemodynamics. During use, it is necessary to pay attention to regularly checking the waveform quality, paying attention to the coefficient of variation, excluding possible false difference interference, and regularly calibrating the system. Description of the Drawings

[0042] Figure 1 It is a schematic diagram of the overall process of the method;

[0043] Figure 2 It is a waveform schematic diagram of the arterial blood pressure waveform, pulse oximetry waveform, and the corresponding envelope analysis results;

[0044] Figure 3 It is a waveform schematic diagram for obtaining the peak interval and extracting the amplitude;

[0045] Figure 4 It is a schematic diagram of the actual application of this method in calculating the peripheral perfusion time. Specific Embodiment

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0047] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0048] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0049] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0050] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use, or the orientation or positional relationship commonly understood by those skilled in the art. Such terms are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. In addition, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0051] Embodiment 1: Refer to Figures 1-4 As shown, a method for defining a new tissue perfusion index based on the life waveforms of a monitor provided in this embodiment includes:

[0052] Synchronously collect the arterial blood pressure waveform and pulse oximetry waveform within the same time period through a clinical conventional monitor, perform digital filtering on the original waveforms, accurately align the two waveforms on the time axis, and ensure the synchronization of the sampling timestamps of the two waveforms. Obtain the signal parameters of the arterial blood pressure waveform and pulse oximetry waveform in the vital monitor, such as frequency, amplitude, phase, angular frequency, etc. Represent the two waveforms as corresponding mathematical formulas. The mathematical representation of the arterial blood pressure waveform:

[0053] ,

[0054] The mathematical representation of the pulse oximetry waveform:

[0055] ,

[0056] where t is the time variable; is the amplitude of the arterial blood pressure waveform, is the amplitude of the pulse oximetry waveform; ω is the angular frequency, related to the heart rate; is the initial phase of the arterial blood pressure waveform, is the initial phase of the pulse oximetry waveform; R(t) is the respiratory modulation component, showing periodic changes; is the noise component of the arterial blood pressure waveform, is the noise component of the pulse oximetry waveform.

[0057] It should be noted that the present invention is based on physiology and hemodynamics. From a physiological perspective, there is a time delay in the conduction of the arterial blood pressure wave from the proximal end to the peripheral microcirculation, and this time delay is affected by vascular compliance, vascular tone, and peripheral resistance. When tissue perfusion is insufficient, vasoconstriction will cause the conduction time to prolong. From a hemodynamic perspective, the conduction speed of the pulse oximetry waveform is positively correlated with the elasticity of the blood vessel wall and vascular tone. At the same time, if the patient also has microcirculation dysfunction, it will further affect the conduction characteristics of the pulse oximetry waveform, causing further delay in waveform conduction, resulting in tissue perfusion insufficiency and cell hypoxia in the patient.

[0058] The cardiac cycle refers to each periodic activity of the heart, that is, the time required for the heart to complete one contraction and one relaxation activity. Since both the arterial blood pressure waveform and the pulse oximetry waveform originate from the pulsation of the same cardiac cycle, the time difference between the two waveforms can accurately reflect the conduction time of blood flow from the proximal end to the distal end. However, to make full use of this time difference information and accurately analyze the blood flow conduction characteristics, it is necessary to ensure that the two waveforms can accurately correspond to the same cardiac cycle. In order to analyze the arterial blood pressure waveform and pulse oximetry waveform under the same cardiac cycle, it is necessary to perform consistency matching on the two waveforms.

[0059] As a preferred technical solution of the present application, for consistency matching, the method includes:

[0060] Perform waveform matching on the arterial blood pressure waveform and the pulse oximetry waveform, determine the periodic characteristics from the matched arterial blood pressure waveform and pulse oximetry waveform, and determine the phase difference confidence interval according to the periodic characteristics;

[0061] In the phase difference confidence interval, perform phase matching on the arterial blood pressure waveform and the pulse oximetry waveform so that the first peak and the second peak correspond to the same cardiac cycle.

[0062] It should be noted that the arterial blood pressure waveform and the pulse oximetry waveform originate from cardiac pulsation. The speed of the heart rate will directly affect the speed and efficiency of blood circulation and affect the propagation speed of the waveform. Respiratory activity will have a certain impact on blood pressure and form a respiratory wave. This fluctuation is caused by the change of intrathoracic negative pressure during respiration and the indirect influence of the activity of respiratory muscles on the circulatory system. During the respiratory cycle, the change of intrathoracic negative pressure will affect the volume of returned blood and the output of the right ventricle, and then have a dilating or contracting effect on the pulmonary circulation blood vessels, ultimately affecting arterial blood pressure. During respiration, as the partial pressure of oxygen in the alveoli changes, the oxygenation degree of hemoglobin will also change, which also affects the pulse oximetry waveform. Therefore, the cardiac cycle and the respiratory cycle can be used as natural markers for matching the arterial blood pressure waveform and the pulse oximetry waveform.

[0063] Specifically, for example Figure 2 As shown, using the signal modulation principle, perform envelope analysis on the arterial blood pressure waveform and the pulse oximetry waveform to obtain the corresponding envelope waveform. Perform waveform matching on the two obtained envelope waveforms, including respiratory cycle matching and / or cardiac cycle variation matching. In the matched waveform, extract physical signal parameters with periodic characteristics such as the amplitude size and interval time of the signal from the envelope of the arterial blood pressure waveform, select multiple similar continuous waveforms from the envelope of the pulse oximetry waveform, and record the time period of the continuous waveform as the phase difference confidence interval.

[0064] In the obtained multiple phase difference confidence intervals, perform phase matching on the arterial blood pressure waveform and the pulse oximetry waveform, including amplitude matching and / or peak spacing matching, so that the first peak and the second peak correspond to the same cardiac cycle.

[0065] Based on the fact that there is a correlation between the heart rate, respiratory signal and the arterial blood pressure waveform, and there is also a correlation between the heart rate, respiratory signal and the pulse oximetry waveform, use the signal processing principle to match the arterial blood pressure waveform and the pulse oximetry waveform so that the peaks of the two waveforms used to calculate the mean perfusion time both belong to the same cardiac cycle, improving the accuracy of the mean perfusion time.

[0066] Furthermore, the waveform matching includes respiratory cycle matching, and the method includes:

[0067] Perform signal modulation on the arterial blood pressure waveform and extract the first respiratory envelope;

[0068] Perform signal modulation on the pulse oximetry waveform and extract the second respiratory envelope;

[0069] Construct the cross-correlation function of the first respiratory envelope and the second respiratory envelope, and calculate the cross-correlation result;

[0070] Preset a respiratory cycle matching threshold, compare the cross-correlation result with the respiratory cycle matching threshold, and determine the matching result of the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result.

[0071] Specifically, since the respiratory rate is lower than the heart rate, the arterial blood pressure waveform and the pulse oximetry waveform are high-frequency signals, reflecting the dynamics of the heartbeat and blood circulation. The respiratory wave is a low-frequency signal, reflecting the respiratory dynamics. The arterial blood pressure waveform and the pulse oximetry waveform extracted by the monitor will be superimposed with the low-frequency respiratory wave, that is, the periodic fluctuation caused by respiration. Through signal processing techniques, such as Hilbert transform and low-pass filtering, we can extract these low-frequency components related to respiration.

[0072] In this embodiment, the method of signal modulation includes: obtaining the arterial blood pressure waveform or the pulse oximetry waveform, denoted as the real signal. Using linear transformation techniques such as Hilbert transform or quadrature down-conversion, convert the real signal into an analytic signal. Take the absolute value of the analytic signal to obtain the instantaneous amplitude signal of the analytic signal. Perform low-pass filtering on the instantaneous amplitude signal to obtain a smooth respiratory envelope after removing the noise signal. The respiratory envelope is used to reflect the dynamic change characteristics of the respiratory signal, such as the depth and frequency of respiration. In this embodiment, the signal modulation formula used for respiratory cycle matching is:

[0073] ,

[0074] where t is the time point; signal is the arterial blood pressure waveform or the pulse oximetry waveform collected by the patient monitor; Hilbert is a linear transformation that converts the arterial blood pressure waveform or the pulse oximetry waveform into an analytic signal; abs is to take the absolute value, and by taking the absolute value of the analytic signal after Hilbert transform, the instantaneous amplitude of the signal is obtained; lowpass is to perform low-pass filtering on the instantaneous amplitude signal to remove the noise signal in the instantaneous amplitude signal and obtain a smooth respiratory envelope. By performing signal modulation on the arterial blood pressure waveform or the pulse oximetry waveform, a smooth respiratory envelope can be obtained, which can be used to monitor the depth and frequency of respiration and evaluate the change of respiratory status.

[0075] Construct the cross-correlation function of the first respiratory envelope and the second respiratory envelope, and the formula is:

[0076] ,

[0077] where Env_ABP(t) is the respiratory envelope of the arterial blood pressure waveform; is the respiratory envelope of the pulse oximetry waveform; is the time delay.

[0078] Calculate the cross - correlation result CrossCorr(τ), preset the respiratory cycle matching threshold, compare the cross - correlation result with the respiratory cycle matching threshold, and determine whether the arterial blood pressure waveform and the pulse oximetry waveform match according to the comparison result. If the cross - correlation result is greater than or equal to the respiratory cycle matching threshold, it indicates that the arterial blood pressure waveform and the pulse oximetry waveform match in the respiratory cycle.

[0079] Furthermore, waveform matching includes cardiac cycle variation matching, and the method includes:

[0080] Calculate the time difference between adjacent wave peaks in the arterial blood pressure waveform, denoted as the second interval;

[0081] Calculate the time difference between adjacent wave peaks in the pulse oximetry waveform, denoted as the third interval;

[0082] Preset the cardiac cycle matching threshold; calculate the second difference between the second interval and the third interval, compare the absolute value of the second difference with the cardiac cycle matching threshold, and determine the matching result of the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result.

[0083] Specifically, the calculation formula for the second interval is:

[0084] ,

[0085] where t1_peak(i) is the time point of the i - th first wave peak in the arterial blood pressure waveform.

[0086] The calculation formula for the third interval is:

[0087] ,

[0088] where t2_peak(j) is the time point of the j - th second wave peak in the pulse oximetry waveform.

[0089] Preset the cardiac cycle matching threshold; calculate the second difference between the second interval and the third interval, compare the absolute value of the difference with the cardiac cycle matching threshold, and determine whether the arterial blood pressure waveform and the pulse oximetry waveform match. If the absolute value of the difference is less than the cardiac cycle matching threshold, it means that the arterial blood pressure waveform and the pulse oximetry waveform match in the cardiac cycle.

[0090] Furthermore, perform phase matching, and the method includes:

[0091] A preset sliding window; in the sliding window, calculate the first peak of the arterial blood pressure waveform, denoted as the first amplitude, and calculate the second peak of the pulse oximetry waveform, denoted as the second amplitude; obtain the time point corresponding to the first amplitude, denoted as the first peak position, and obtain the time point corresponding to the second amplitude, denoted as the second peak position;

[0092] Based on the first amplitude and the second amplitude, perform amplitude matching on the arterial blood pressure waveform and the pulse oximetry waveform. Since the arterial blood pressure waveform and the pulse oximetry waveform are not exactly the same, when the waveform noise is large or there is a change in heart rhythm, simply based on amplitude matching may lead to incorrect peak correspondence. Therefore, in this embodiment, it is also necessary to perform peak distance matching on the arterial blood pressure waveform and the pulse oximetry waveform based on the first peak position and the second peak position.

[0093] Specifically, for example Figure 3 As shown, obtain the arterial blood pressure waveform and the pulse oximetry waveform in the sliding window, and obtain the local maximum values of the two waveforms in their respective sliding windows by directly comparing the waveform values. If the local maximum value exceeds a preset ratio of the maximum value of the entire waveform, then regard the local maximum value as the peak position in the sliding window, which corresponds to the first peak in the arterial blood pressure waveform, denoted as the first amplitude; and corresponds to the second peak in the pulse oximetry waveform, denoted as the second amplitude; the waveform value at the peak position is the amplitude of this section of the waveform in the sliding window. In the sliding window, denote the first peak position of the arterial blood pressure waveform as , and denote the first amplitude as , where n1 is a positive integer; denote the second peak position of the pulse oximetry waveform as , and denote the second amplitude as , where n2 is a positive integer.

[0094] Specifically, for amplitude matching, the method includes:

[0095] Obtain the lagged amplitude of the second amplitude at multiple time points in the waveform. Based on the multiple lagged amplitudes and the first amplitude, use the least squares method to obtain multiple time points that minimize the error E(k), denoted as candidate time points. The formula is:

[0096] ,

[0097] where i is the position index of the corresponding first peak or second peak; k is the index of the lag time point of the pulse oximetry waveform, representing the offset of the waveform lag; both i and k are positive integers. For different lag time points, sort the obtained errors E(k) from smallest to largest. Select the lag time points corresponding to the smallest multiple errors E(k) as candidate time points. The index k corresponding to the candidate time point is denoted as candidate index , where m is a positive integer.

[0098] Specifically, for the peak - to - peak interval matching, the method includes:

[0099] Calculate the time interval between adjacent first - peak positions, denoted as the blood - pressure waveform interval. , and the formula is:

[0100] ,

[0101] where i is the position index of the first peak.

[0102] Calculate the time interval between adjacent second - peak positions, denoted as the blood - oxygen waveform interval, and the formula is:

[0103] ,

[0104] where j is the position index of the second peak.

[0105] Obtain the i - th blood - pressure waveform interval , and obtain multiple candidate indices corresponding to the blood - oxygen waveform intervals , where i and j are equal. Calculate the error between the blood - oxygen waveform interval and the blood - pressure waveform interval by the least - squares method, and obtain multiple candidate time points that minimize the error, denoted as the matching time points. The index k corresponding to the matching time point is denoted as the matching index . The first - peak position and the second - peak position corresponding to the matching index are in the same cardiac cycle.

[0106] In the same cardiac cycle after matching, obtain the first peak of the arterial - blood - pressure waveform and the second peak of the pulse - oximetry waveform, and calculate the first interval between the time point corresponding to the second peak and the time point corresponding to the first peak; calculate the average value of multiple first intervals, denoted as the end - perfusion time EPT, and the calculation formula is:

[0107] ,

[0108] where mean is the average - value function; is the matching index; i is the position index of the first peak or the second peak.

[0109] Specifically, in the arterial - blood - pressure waveform and the pulse - oximetry waveform, the peak is the position that can best reflect the physiological event of cardiac contraction and is less affected by abnormal physiological state changes such as arrhythmia. At the same time, in one cardiac cycle, the peak of the arterial - blood - pressure waveform or the pulse - oximetry waveform will only appear once. Therefore, on the premise that the arterial - blood - pressure waveform and the pulse - oximetry waveform meet the matching condition, obtain the first peak of the arterial - blood - pressure waveform and the second peak of the pulse - oximetry waveform in the same cardiac cycle, and calculate the first interval between them. Calculate the average value of multiple first intervals in the same cardiac cycle as the end - perfusion time EPT.

[0110] Select multiple consecutive respiratory cycles, and obtain arterial blood pressure waveforms and pulse oximetry waveforms. Calculate multiple end-tidal perfusion times, and denote the average of the multiple end-tidal perfusion times as the average perfusion time AEPT, with the unit of ms. Determine the status of the tissue perfusion index based on the average perfusion time. In this embodiment, it is preset to calculate the average perfusion time among 5 consecutive respiratory cycles.

[0111] As a preferred technical solution of this application, after calculating the average of multiple end-tidal perfusion times, it further includes: calculating the standard deviation of the multiple end-tidal perfusion times, calculating the ratio of the standard deviation to the average perfusion time to obtain the coefficient of variation of the multiple end-tidal perfusion times. Compare the coefficient of variation with a preset data fluctuation threshold, and determine whether there are outliers in the multiple end-tidal perfusion times according to the size of the comparison result. If the coefficient of variation exceeds the data fluctuation threshold, it indicates that the data fluctuation range of the end-tidal perfusion time is too large, which may cause a large error in the calculated average perfusion time AEPT. It is necessary to re-obtain the waveform parameters to improve the accuracy of identifying the tissue perfusion status. At the same time, image and / or voice prompts need to be given on the monitor to facilitate timely intervention by medical staff and eliminate abnormal factors causing data fluctuations.

[0112] Since the speed and efficiency of blood circulation are directly affected by the heart rate. Therefore, as a preferred solution of this application, after calculating the average of multiple end-tidal perfusion times, it further includes: correcting the average perfusion time, and the method includes: calculating the average heart rate in multiple consecutive respiratory cycles, and calculating the average cardiac cycle according to the average heart rate; using the ratio of the average perfusion time to the average cardiac cycle as the end-tidal perfusion index EPTc, which is used to represent how many heartbeats are required to drive the blood flow through the distance from the wrist to the fingertips, and determining the status of the tissue perfusion index based on the end-tidal perfusion index. Compare the end-tidal perfusion index with a preset end-tidal perfusion threshold. If the end-tidal perfusion index is less than the end-tidal perfusion threshold, it indicates that the tissue perfusion status is good; if the end-tidal perfusion index is greater than or equal to the end-tidal perfusion threshold, it indicates that the tissue perfusion status is poor, and clinical medical staff need to provide medical care to the patient.

[0113] By analyzing the mean perfusion time between the arterial blood pressure waveform and the pulse oximetry waveform collected by existing devices, the present invention evaluates the tissue perfusion status based on the peripheral perfusion time, effectively solving the problem of complex operation in detecting the tissue perfusion status in the prior art. Furthermore, it realizes the full-automatic and continuous monitoring by using the data of existing monitoring devices without additional equipment, provides objective and quantitative evaluation indicators, and has good result repeatability and low operator dependence. From the perspective of economic benefits, the present invention can be installed and stored in existing monitoring devices without purchasing special equipment, which can reduce the monitoring cost and improve the utilization rate of existing devices, having significant popularization value. In terms of practical value, the present invention is applicable to various critically ill patients, can be used in a variety of clinical scenarios, is easy to operate and promote, helps to standardize treatment, and can significantly improve the monitoring and treatment level of critically ill patients.

[0114] The usage method of devices such as the life monitor of the present invention is very simple and intuitive. For example Figure 4 as shown, it is convenient for front-line medical staff in clinical practice to operate. First, in the equipment preparation stage, ensure that the patient is correctly connected to the life monitor, place the arterial catheter and pulse oximetry probe on the same upper limb to reduce the compression of the measured limb, and start the data acquisition software. Enter the basic patient information into the system and select a suitable display mode, such as real-time value or trend graph, etc. The system will automatically start collecting and analyzing waveform data, update the average perfusion time AEPT value every 5 respiratory cycles, and simultaneously dynamically display the trend changes of the average perfusion time AEPT and the peripheral perfusion index EPTc. Medical staff can evaluate the tissue perfusion status of the patient based on this and timely detect abnormal changes in hemodynamics. During use, it is necessary to pay attention to regularly checking the waveform quality, paying attention to the coefficient of variation, excluding possible artifact interference, and regularly calibrating the system. It should be noted that for patients with atrial fibrillation, due to the irregular heart rate that may affect the reliability of the data, it is necessary to extend the sampling time or comprehensively evaluate in combination with other indicators.

[0115] The above embodiments are only used to illustrate the present invention and do not limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above respective embodiments, the present invention is not limited to the above specific embodiments. Therefore, any modification or equivalent replacement to the present invention; and all technical solutions and their improvements that do not deviate from the spirit and scope of the invention are covered by the scope of the claims of the present invention.

Claims

1. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, characterized in that, Comprising: Collecting arterial blood pressure waveforms and pulse oximetry waveforms, and performing consistency matching on the arterial blood pressure waveforms and pulse oximetry waveforms; In the same cardiac cycle, obtaining the first peak of the arterial blood pressure waveform and the second peak of the pulse oximetry waveform, and calculating the first interval between the corresponding time points of the second peak and the first peak; Calculating the average value of multiple first intervals, denoted as the peripheral perfusion time; Calculating the average value of multiple peripheral perfusion times in consecutive respiratory cycles, denoted as the average perfusion time, and determining the status of the tissue perfusion index based on the average perfusion time; The consistency matching includes: Performing waveform matching on the arterial blood pressure waveform and the pulse oximetry waveform, determining periodic characteristics from the matched arterial blood pressure waveform and pulse oximetry waveform, and determining the phase difference confidence interval according to the periodic characteristics; in the phase difference confidence interval, performing phase matching on the arterial blood pressure waveform and the pulse oximetry waveform; The waveform matching includes respiratory cycle matching, and the method includes: Performing signal modulation on the arterial blood pressure waveform to extract the first respiratory envelope; performing signal modulation on the pulse oximetry waveform to extract the second respiratory envelope; constructing the cross-correlation function of the first respiratory envelope and the second respiratory envelope, and calculating the cross-correlation result; presetting a respiratory cycle matching threshold, comparing the cross-correlation result with the respiratory cycle matching threshold, and determining the matching result of the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result; The method of the phase matching includes: In a preset sliding window, calculating the local maximum value of the arterial blood pressure waveform in the sliding window, and taking the local maximum value greater than a preset ratio of the maximum value of the arterial blood pressure waveform as the first peak, denoted as the first amplitude; calculating the local maximum value of the pulse oximetry waveform in the sliding window, and taking the local maximum value greater than a preset ratio of the maximum value of the pulse oximetry waveform as the second peak, denoted as the second amplitude; obtaining the time point corresponding to the first amplitude, denoted as the first peak position, and obtaining the time point corresponding to the second amplitude, denoted as the second peak position; performing amplitude matching on the arterial blood pressure waveform and the pulse oximetry waveform based on the first amplitude and the second amplitude; performing peak interval matching on the arterial blood pressure waveform and the pulse oximetry waveform based on the first peak position and the second peak position.

2. The method for defining a new tissue perfusion index based on the vital signs of a monitor according to claim 1, characterized in that, Also including: Correcting the average perfusion time based on the heart rate, and the method includes: Calculating the average heart rate in consecutive respiratory cycles, and calculating the average cardiac cycle according to the average heart rate; Taking the ratio of the average perfusion time to the average cardiac cycle as the peripheral perfusion index, and determining the status of the tissue perfusion index based on the peripheral perfusion index.

3. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, as claimed in claim 1, wherein The method of signal modulation includes: Obtaining the arterial blood pressure waveform or the pulse oximetry waveform, denoted as the real signal; converting the real signal into an analytic signal through linear transformation; Taking the absolute value of the analytic signal to obtain the instantaneous amplitude signal; Performing low-pass filtering on the instantaneous amplitude signal to obtain a smooth respiratory envelope after removing the noise signal.

4. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, as claimed in claim 1, wherein The waveform matching includes cardiac cycle variation matching, and the method includes: Calculating the time interval between adjacent peaks in the arterial blood pressure waveform, denoted as the second interval; Calculating the time interval between adjacent peaks in the pulse oximetry waveform, denoted as the third interval; Preset a matching threshold for the cardiac cycle; calculate the second difference between the second interval and the third interval, compare the absolute value of the second difference with the cardiac cycle matching threshold, and determine the matching result between the arterial blood pressure waveform and the pulse oximetry waveform according to the comparison result.

5. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, as claimed in claim 1, wherein The amplitude matching method includes: Obtain the lag amplitude of the second amplitude after multiple time points, and based on the multiple lag amplitudes and the first amplitude, obtain multiple time points that minimize the error through the least squares method, denoted as candidate time points.

6. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor, as claimed in claim 5, wherein The method for matching the peak-to-peak interval includes: Calculate the time interval between adjacent first peak positions, denoted as the blood pressure waveform interval; calculate the time interval between adjacent second peak positions, denoted as the blood oxygen waveform interval. Obtain multiple blood oxygen waveform intervals corresponding to multiple candidate time points, and based on the multiple blood oxygen waveform intervals and the blood pressure waveform interval, obtain multiple candidate time points that minimize the error through the least squares method, denoted as the optimal time points, and obtain multiple pairs of first peak positions and second peak positions corresponding to the cardiac cycle.

7. A method for defining a new tissue perfusion index based on the vital waveforms of a monitor according to claim 1, characterized in that, After calculating the average perfusion time, it further includes: Calculate the coefficient of variation of multiple peripheral perfusion times; compare the coefficient of variation with a preset data fluctuation threshold, and determine whether there are outliers in the multiple peripheral perfusion times according to the size of the comparison result.

Images