Blood sampling system for blood station and blood sampling detection method
By conducting ultrasonic coagulation test and signal decomposition on blood samples, analyzing fibrin contraction and turbulence characteristics, and correcting the erythrocyte buildup with the erythrocyte agglomeration index, the detection error caused by erythrocyte agglomeration is solved and the accurate detection of erythrocyte blockage is achieved.
Patent Information
- Application Number
- CN202510392339.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the prior art, it is difficult to accurately detect the detection result errors caused by red blood cell agglomeration when Kur's theorem detects the detection result of red blood cell agglomeration during red blood cell agglomeration.
Coagulation test is performed by adding procoagulant to the blood sample, and ultrasonic waves are emitted during the test, backscattered signals are extracted, and backscattered signals are decomposed into linear and nonlinear component signals, fibrin contraction rate and blood turbulence characteristics are analyzed, and the erythrocyte agglomeration index is used to correct the erythrocyte packing.
Under the influence of erythropoiesis agglomeration, the accurate detection of erythropoiesis is achieved, which improves the confidence and accuracy of the detection.
Smart Images
Figure CN120294318A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of medical testing technologies, and more specifically, to a blood collection system and a blood collection and detection method for blood banks. Background Art
[0002] A blood bank is an institution specifically responsible for collecting, storing, testing, and distributing blood and blood components. The main goal of a blood bank is to provide safe and qualified blood for medical institutions to meet the clinical blood transfusion needs such as surgeries, trauma treatments, disease treatments (such as anemia, leukemia), and organ transplants. To ensure the health and treatment effects of transfused patients, a series of tests are usually required for the collected blood. An important evaluation index among them is the hematocrit in the blood.
[0003] In the prior art, the hematocrit in blood can be detected by two methods: the Wintrobe method and the Coulter principle. Among them, although the Wintrobe method is the gold standard for hematocrit detection, due to its complex operation and long time consumption, it is not suitable for the detection during large-scale blood donations in blood banks. Therefore, blood banks commonly use the Coulter principle to detect the hematocrit in blood. However, according to the literature "Comparison of the results of hematocrit measured by the instrument method and the Wintrobe method and evaluation of diagnostic tests", it is pointed out that the hematocrit results detected by the Coulter principle (i.e., the hematocrit instrument method) are generally lower than those detected by the Wintrobe method. This is because the detection principle of the Coulter principle is to detect the number of particulate matters in the blood (i.e., the number of red blood cells), and during the detection, some red blood cells in the blood aggregate together, and multiple aggregated red blood cells are misidentified as the same red blood cell, which causes errors in the detection results of hematocrit during blood collection and detection in blood banks. Therefore, how to perform confidence detection of hematocrit under the influence of red blood cell aggregation has become a difficult problem faced by the industry. Summary of the Invention
[0004] This application provides a blood collection system and a blood collection and detection method for blood banks, which can perform confidence detection of hematocrit under the influence of red blood cell aggregation.
[0005] In a first aspect, this application provides a blood collection and detection method for blood banks, including: Extracting a first blood sample from the blood collected from a target user, adding a coagulant reagent to the first blood sample for a blood coagulation test, and emitting ultrasonic waves to the first blood sample during the test, and then extracting the backscattering signal after the blood exhibits a turbulent phenomenon; Decomposing the backscattering signal into component signals of multiple modes, and then dividing all component signals into linear component signals representing fibrin contraction in the blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal; Extract the contraction rate of fibrin in the first blood sample from all linear component signals, determine the flow characteristics of the first blood sample through all non-linear component signals, and determine the aggregation index of red blood cells in the non-coagulated blood of the target user by combining the contraction rate and the flow characteristics; Extract a second blood sample from the blood collected from the target user, perform electrical impedance sensing detection on the second blood sample to obtain the hematocrit in the blood of the target user; Correct the hematocrit in the blood of the target user based on the aggregation index, and evaluate the blood quality of the target user through the corrected hematocrit.
[0006] In some embodiments, adding a coagulation promoting reagent to the first blood sample for blood coagulation test specifically includes: Collect blood from the target user to obtain the blood of the target user; Extract a first blood sample from the blood of the target user; Mix the first blood sample with the coagulation promoting reagent and place it in a constant temperature water bath for water isolation and heat preservation until the blood is completely coagulated.
[0007] In some embodiments, emitting ultrasonic waves to the first blood sample during the test and then extracting the backscattering signal after the blood shows a turbulent phenomenon specifically includes: Emit ultrasonic waves with a fixed frequency to the first blood sample during the blood coagulation test; Receive the echo signal of the ultrasonic waves through a pulse receiver; Identify the Reynolds point where the blood shows a turbulent phenomenon in the echo signal; Extract the backscattering signal after the blood shows a turbulent phenomenon from the echo signal according to the Reynolds point.
[0008] In some embodiments, decomposing the backscattering signal into component signals of multiple modes specifically includes: Fit the backscattering signal into a backscattering curve; Perform wavelet decomposition on the backscattering curve through a selected wavelet basis to obtain component signals of multiple modes.
[0009] In some embodiments, dividing all component signals into linear component signals representing fibrin contraction in blood and non-linear component signals representing blood turbulent motion according to the fractal index of each component signal specifically includes: Determine the signal amplitude of each component signal; Remove the noise signals in all component signals according to all the signal amplitudes, and regard all the remaining component signals as effective signals; Determine the fractal index of each valid signal; Compare all the fractal indices with a preset fractal threshold, and regard all valid signals with fractal indices less than or equal to the fractal threshold as linear component signals characterizing fibrin contraction in blood; Regard all valid signals with fractal indices greater than the fractal threshold as non - linear component signals characterizing blood turbulent motion.
[0010] In some embodiments, extracting the contraction rate of fibrin in the first blood sample from all the linear component signals specifically includes: Reconstruct all the linear component signals into a contraction echo curve describing fibrin contraction in blood; Determine the autocorrelation sequence of the contraction echo curve; Perform quadratic fitting on the contraction echo curve according to the autocorrelation sequence, and then determine the contraction rate of fibrin in the first blood sample.
[0011] In some embodiments, determining the flow characteristics of the first blood sample through all the non - linear component signals specifically includes: Reconstruct all the non - linear component signals into a flow echo curve of blood turbulent motion; Determine the power spectral density of each frequency of the flow echo curve; Identify the energy transfer characteristics in the first blood sample according to all the power spectral densities; Determine the flow characteristics of the first blood sample according to the energy transfer characteristics.
[0012] In some embodiments, perform resistance induction detection on the second blood sample through a blood cell counter.
[0013] In some embodiments, correcting the hematocrit in the target user's blood based on the aggregation index specifically includes: Obtain the volume of the second blood sample; Determine a correction index based on the aggregation index and the volume of the second blood sample; Correct the hematocrit in the target user's blood according to the correction index.
[0014] In a second aspect, the present application provides a blood collection system for a blood station, including a blood collection detection unit, and the blood collection detection unit includes: A coagulation test module, configured to extract a first blood sample from the blood collected from a target user, add a coagulation - promoting reagent to the first blood sample for a blood coagulation test, and emit ultrasonic waves to the first blood sample during the test, and then extract the back - scatter signal after the blood shows a turbulent phenomenon; A processing module, configured to decompose the backscattered signal into component signals of multiple modes, and then divide all component signals into linear component signals representing fibrin contraction in blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal; The processing module is further configured to extract the contraction rate of fibrin in the first blood sample from all the linear component signals, determine the flow characteristics of the first blood sample through all the nonlinear component signals, and combine the contraction rate and the flow characteristics to determine the aggregation index of red blood cells in the non-coagulated blood of the target user; A hematocrit detection module, configured to extract a second blood sample from the blood collected from the target user, perform resistance induction detection on the second blood sample, and obtain the hematocrit in the blood of the target user; An execution module, configured to correct the hematocrit in the blood of the target user based on the aggregation index, and evaluate the blood quality of the target user through the corrected hematocrit.
[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the blood collection system and blood collection detection method for blood stations provided in this application, first, a first blood sample is extracted from the blood collected from the target user, a blood coagulation reagent is added to the first blood sample for blood coagulation testing, and ultrasonic waves are emitted to the first blood sample during the testing process, and then the backscattered signal after the blood shows a turbulent phenomenon is extracted; the backscattered signal is decomposed into component signals of multiple modes, and then all component signals are divided into linear component signals representing fibrin contraction in blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal; the contraction rate of fibrin in the first blood sample is extracted from all the linear component signals, and the flow characteristics of the first blood sample are determined through all the nonlinear component signals, and the aggregation index of red blood cells in the non-coagulated blood of the target user is determined by combining the contraction rate and the flow characteristics; a second blood sample is extracted from the blood collected from the target user, resistance induction detection is performed on the second blood sample, and the hematocrit in the blood of the target user is obtained; the hematocrit in the blood of the target user is corrected based on the aggregation index, and the blood quality of the target user is evaluated through the corrected hematocrit.
[0016] It can be seen that in the present application, through the coagulation test of ultrasonic detection of blood, the linear component signal and the non-linear component signal are extracted from the ultrasonic echo. During the blood coagulation process, fibrin contracts, and a large number of red blood cells are wrapped by the network structure formed by fibrin to form large particles. This process of fibrin wrapping red blood cells is usually carried out in an orderly manner, and the formed large particles have a predictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of fibrin contraction corresponds to the component of the linear part in the backscattering signal (linear component signal). As large particles are formed, the Reynolds number of the blood becomes larger, causing disordered turbulent motion in the blood. This turbulent motion has an unpredictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of the turbulent motion corresponds to the component of the non-linear part in the backscattering signal (non-linear component signal). Furthermore, the rate of fibrin contraction and the flow characteristics of the turbulent flow in the blood are analyzed through the linear component signal and the non-linear component signal. Subsequently, the number of red blood cell aggregations (i.e., the aggregation index) in the non-coagulated state of the blood is evaluated by combining the rate of fibrin contraction and the flow characteristics of the turbulent flow in the blood. Finally, the hematocrit obtained by conventional detection (i.e., electrical impedance detection) is corrected by the aggregation index. To sum up, the present application can perform a confidence detection of hematocrit under the influence of red blood cell aggregation. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 is an exemplary flowchart of a blood collection detection method for a blood bank according to some embodiments of the present application; Figure 2 is a schematic structural diagram of performing a blood coagulation test according to some embodiments of the present application; Figure 3 is an exemplary curve graph of a backscattering signal according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a blood collection detection unit according to some embodiments of the present application; Figure 5 is a schematic structural diagram of a computer device for implementing the blood collection detection method for a blood bank according to some embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0018] To better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.
[0019] Refer to Figure 1 , which is an exemplary flowchart of a blood collection detection method for a blood bank according to some embodiments of the present application. The blood collection detection method 100 for a blood bank mainly includes the following steps: In step 101, a first blood sample is extracted from the blood collected from the target user. A coagulation-promoting reagent is added to the first blood sample for a blood coagulation test, and ultrasonic waves are emitted to the first blood sample during the test, and then the backscattering signal after the blood exhibits a turbulent flow phenomenon is extracted.
[0020] Specifically, extracting the first blood sample from the blood collected from the target user can be achieved in the following manner: 5 ml of whole blood is extracted from the blood collected from the target user, and the extracted whole blood is used as the first blood sample.
[0021] In some embodiments, adding a coagulation-promoting reagent to the first blood sample for a blood coagulation test means mixing the first blood sample with the coagulation-promoting reagent and placing it in a thermostatic water bath for water-insulated heat preservation until the blood is completely coagulated.
[0022] Specifically, mixing the first blood sample with the coagulation-promoting reagent and placing it in a thermostatic water bath for water-insulated heat preservation can be achieved in the following manner: 0.5 ml of the coagulation-promoting reagent is added to the first blood sample and mixed evenly. The mixed first blood sample is placed in a thermostatic water bath, and the thermal circulator of the thermostatic water bath is started to keep the temperature in the thermostatic water bath constant at 37°C. Among them, the coagulation-promoting reagent can be a calcium chloride solution with a concentration of 0.025 mol / L. In other embodiments, other coagulation-promoting reagents can also be selected, which are not limited here.
[0023] In some embodiments, refer to Figure 2 , this figure is a schematic structural diagram of a blood coagulation test shown in some embodiments of the present application. The specific descriptions of each device in this schematic structural diagram are as follows: The transducer is used for mutual conversion between electrical signals and ultrasonic signals; The pulse receiver is used to receive the signal from the transducer and amplify it; The limiter is used to limit the amplitude of the signal to avoid damage to the device; The A / D converter (analog-to-digital converter) is used to convert the analog signal into a digital signal. In this application, it is used to convert the electrical signal of the pulse receiver into a digital signal; The calculator is used to process the digital signal. In this application, it is used to process and analyze the backscattering signal; The placement table is used to place the blood sample; The thermostatic water bath is used to control the ambient temperature during the coagulation test process.
[0024] In some embodiments, emitting ultrasonic waves to the first blood sample during the test and then extracting the backscattering signal after the blood exhibits a turbulent flow phenomenon can be achieved by the following steps: Emitting ultrasonic waves with a fixed frequency to the first blood sample during a blood coagulation test; Receiving the echo signal of the ultrasonic waves through a pulse receiver; Identifying the Reynolds point at which blood turbulence occurs in the echo signal; Extracting the backscattering signal after blood turbulence occurs from the echo signal according to the Reynolds point.
[0025] It should be noted that in this application, the fixed frequency is a preset frequency according to actual needs. To improve the detection sensitivity, this fixed frequency is usually set to a relatively large frequency value. For example, in this application, the fixed frequency is preset to 50 MHz.
[0026] In specific implementation, receiving the echo signal of the ultrasonic waves through a pulse receiver can be achieved in the following manner: collecting the root mean square amplitude of the ultrasonic echo by the pulse receiver at a preset sampling interval, arranging all the root mean square amplitudes in the order of collection, and then taking the arranged sequence as the echo signal of the ultrasonic waves. Among them, the sampling interval can be preset according to actual needs. For example, in this application, the sampling interval is preset to 10 microseconds.
[0027] In specific implementation, identifying the Reynolds point at which blood turbulence occurs in the echo signal can be achieved in the following manner: presetting a sliding window with a length of a, traversing the echo signal through this sliding window, obtaining multiple local signal segments, counting the number of maximum values in each local signal segment as the number of extreme values, and then arranging all the numbers of extreme values in chronological order and comparing them with a preset threshold of the number of extreme values in turn. Taking the time point of the first data in the local signal segment corresponding to the number of extreme values that is greater than the threshold of the number of extreme values as the Reynolds point at which blood turbulence occurs in the echo signal. Among them, traversing the echo signal through this sliding window means taking the data segment composed of the i-th to the (i + a)-th data in the echo signal as a local signal segment, where i takes all integers between 1 and n - a, n is the length of the echo signal, a is the length of the sliding window, the length of this sliding window can be preset according to actual needs. For example, in this application, the length of the sliding window is preset to 10. In addition, the variance threshold can be preset according to actual needs. For example, in this application, the variance threshold is preset to 3.
[0028] It should be noted that in this application, the Reynolds point refers to the time point when the laminar flow motion in the blood changes to turbulent flow motion during the blood coagulation process. During the blood coagulation process, since fibrinogen in the blood is converted into fibrin and binds to red blood cells, it causes turbulent flow motion in the blood, thereby causing fluctuations in the ultrasonic echo.
[0029] In specific implementation, the backward scattering signal after the occurrence of blood turbulence can be extracted from the echo signal according to the Reynolds point in the following manner, that is: the echo signal after the Reynolds point is used as the backward scattering signal after the occurrence of blood turbulence.
[0030] It should be noted that in this application, the backward scattering signal is the signal that returns in the incident direction among the scattered waves after the ultrasonic wave contacts the blood.
[0031] In some embodiments, referring to Figure 3 , this figure is an exemplary curve graph of the backward scattering signal shown according to some embodiments of this application. This curve graph consists of four stages, and each stage is specifically described as follows: In the first stage, with the addition of the procoagulant reagent, red blood cells are dispersed and suspended in the plasma, and the backward scattering intensity of the ultrasonic wave decreases; In the second stage, the dispersed red blood cells begin to aggregate, fibrin is formed, and the ultrasonic wave is more reflected in the incident direction, and the backward scattering intensity of the ultrasonic wave increases; In the third stage, due to the formation of a large amount of fibrin and the contraction of fibrin, a large number of red blood cells are wrapped by the reticular structure of fibrin, and turbulent motion occurs in the blood, and the backward scattering intensity of the ultrasonic wave shows irregular fluctuations; In the fourth stage, a large number of red blood cells are wrapped by the reticular structure of fibrin to form a blood clot, the blood gradually completes coagulation, and the fluctuations in the backward scattering intensity of the ultrasonic wave disappear and remain stable.
[0032] In step 102, the backward scattering signal is decomposed into component signals of multiple modes, and then all the component signals are divided into a linear component signal characterizing fibrin contraction in the blood and a non-linear component signal characterizing blood turbulent motion according to the fractal index of each component signal.
[0033] In some embodiments, the decomposition of the backward scattering signal into component signals of multiple modes can be achieved by the following steps: The backward scattering signal is fitted into a backward scattering curve; The backward scattering curve is subjected to wavelet decomposition through a selected wavelet basis to obtain component signals of multiple modes.
[0034] In specific implementation, the fitting of the backward scattering signal into a backward scattering curve can be achieved in the following manner, that is: the backward scattering signal is fitted into a curve through the Lagrange interpolation method in the prior art, and this curve is used as the backward scattering curve. In other embodiments, the backward scattering signal can also be fitted through other prior arts, which is not limited here.
[0035] In specific implementation, wavelet decomposition is performed on the backscattering curve through a selected wavelet basis to obtain component signals of multiple modes, which can be implemented in the following manner, that is: wavelet decomposition is performed on the backscattering curve through the Haar wavelet to obtain multiple curves, and all the obtained curves are respectively used as the component signals of each mode. In other embodiments, wavelet decomposition can also be performed on the backscattering curve through other existing wavelet bases, which is not limited here.
[0036] It should be noted that in this application, the component signal is a component of a specific vibration mode in the backscattering signal.
[0037] In some embodiments, dividing all component signals into linear component signals representing fibrin contraction in blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal can be implemented by the following steps: Determine the signal amplitude of each component signal; Remove the noise signals in all component signals according to all the signal amplitudes, and regard all the remaining component signals as effective signals; Determine the fractal index of each effective signal; Compare all the fractal indices with a preset fractal threshold, and regard all the effective signals with fractal indices less than or equal to the fractal threshold as linear component signals, that is, the linear component signals representing fibrin contraction in blood; Regard all the effective signals with fractal indices greater than the fractal threshold as nonlinear component signals, that is, the nonlinear component signals representing blood turbulent motion.
[0038] In specific implementation, determining the signal amplitude of each component signal can be implemented in the following manner, that is: for each component signal, subtract the minimum value from the maximum value in each component signal, and use the obtained differences as the signal amplitudes of each component signal respectively.
[0039] It should be noted that in this application, the signal amplitude is a parameter value used to measure the fluctuation range of each component signal. The larger the signal amplitude, the larger the fluctuation range of the component signal, and the smaller the signal amplitude, the smaller the fluctuation range of the component signal.
[0040] In specific implementation, removing the noise signals in all component signals according to all the signal amplitudes can be implemented in the following manner, that is: regard the component signals with all signal amplitudes less than a preset amplitude threshold as noise signals to be removed, where the amplitude threshold can be preset according to actual needs. For example, in this application, the amplitude threshold is preset to 1.
[0041] In specific implementation, the comparison by determining the fractal index of each valid signal can be achieved in the following manner, that is: determine the Hurst index of each valid signal, and take the reciprocal of the Hurst index of each valid signal as the fractal index of each valid signal respectively.
[0042] It should be noted that in this application, the fractal index is a parameter value for measuring the complexity of a signal. The smaller the fractal index, the smaller the signal complexity, and the signal is closer to being smooth and regular. The larger the fractal index, the greater the signal complexity, and the signal is closer to being disordered and chaotic.
[0043] In addition, it should be noted that the fractal threshold in this application is preset to 2. Those skilled in the art know that the variation threshold can be preset to other values according to actual needs, and all fall within the protection scope of the present invention, which will not be elaborated here.
[0044] Furthermore, it should be noted that in this application, the linear component signal is a curve representing the contraction process of fibrin in blood, and the non-linear component signal is a curve representing the turbulent motion of blood. During blood coagulation, fibrin contracts, and a large number of red blood cells are wrapped by the network structure formed by fibrin to form large particles. This process of fibrin wrapping red blood cells is usually carried out in an orderly manner, and the formed large particles have a predictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of orderly fibrin contraction corresponds to the component of the linear part in the backscattering signal (linear component signal). With the formation of large particles, the Reynolds number of blood becomes larger, causing disorderly turbulent motion in the blood. This turbulent motion has an unpredictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of disorderly turbulent motion corresponds to the component of the non-linear part in the backscattering signal (non-linear component signal).
[0045] In step 103, extract the contraction rate of fibrin in the first blood sample from all the linear component signals, determine the flow characteristics of the first blood sample through all the non-linear component signals, and combine the contraction rate and the flow characteristics to determine the aggregation index of red blood cells in the uncoagulated blood of the target user.
[0046] In some embodiments, extracting the contraction rate of fibrin in the first blood sample from all the linear component signals can be achieved by the following steps: Reconstruct all the linear component signals into a contraction echo curve describing the contraction of fibrin in blood; Determine the autocorrelation sequence of the contraction echo curve; Perform quadratic fitting on the contraction echo curve according to the autocorrelation sequence, and then determine the contraction rate of fibrin in the first blood sample.
[0047] In specific implementation, all linear component signals can be reconstructed into a contraction echo curve describing fibrin contraction in blood in the following manner: add all the linear component signals, and use the obtained curve as the contraction echo curve describing fibrin contraction in blood.
[0048] It should be noted that in this application, the contraction echo curve is a curve describing the response of the intensity change of the backscattering signal of ultrasonic waves to the change of fibrin contraction in blood.
[0049] In specific implementation, the autocorrelation sequence of the contraction echo curve can be determined in the following manner: First, preset multiple lag times, calculate the autocorrelation coefficients of the contraction echo curve at multiple lag times, and arrange all the autocorrelation coefficients in ascending order according to the magnitudes of the lag times. Use the obtained sequence as the autocorrelation sequence. Among them, the lag time can be preset according to actual needs. For example, in this application, the lag time is preset as all integers between 0 and 300.
[0050] It should be noted that in this application, the autocorrelation sequence is a sequence describing the temporal correlation of data in the curve.
[0051] In specific implementation, the contraction rate of fibrin in the first blood sample can be determined by performing a second-order fitting on the contraction echo curve according to the autocorrelation sequence in the following manner: First, subtract 0.01 from all the autocorrelation coefficients in the autocorrelation sequence in turn, record the lag time of the first negative autocorrelation coefficient after subtraction, then set the starting time of the contraction echo curve to 0, then fit the contraction echo curve between 0 and k time points into a straight line by the autoregressive method in the prior art, and finally use the slope of this straight line as the contraction rate of fibrin in the first blood sample, where k is the lag time of the first negative autocorrelation coefficient after subtraction.
[0052] It should be noted that in this application, the contraction rate is a parameter value measuring the speed at which fibrin in the first blood sample forms a reticular structure and contracts to wrap red blood cells. The greater the contraction rate, the faster fibrin in the first blood sample forms a reticular structure and contracts to wrap red blood cells; the smaller the contraction rate, the slower fibrin in the first blood sample forms a reticular structure and contracts to wrap red blood cells.
[0053] In some embodiments, the following steps can be used to determine the flow characteristics of the first blood sample through all non-linear component signals: Reconstruct all non-linear component signals into a flow echo curve of the turbulent motion of blood; Determine the power spectral density of each frequency of the flow echo curve; Identify the energy transfer characteristics in the first blood sample based on all the power spectral densities; Determine the flow characteristics of the first blood sample based on the energy transfer characteristics.
[0054] In specific implementation, reconstructing all the non-linear component signals into the flow echo curve of the turbulent motion of blood can be achieved in the following way, that is: add all the non-linear component signals, and use the obtained curve after addition as the flow echo curve of the turbulent motion of blood.
[0055] It should be noted that in this application, the flow echo curve is a curve that describes the response of the intensity change of the backscattering signal of ultrasonic waves to the transformation of blood flow characteristics.
[0056] In specific implementation, determining the power spectral density of each frequency of the flow echo curve can be achieved in the following way, that is: First, convert the flow echo curve from the time domain to the frequency domain through fast Fourier transform to obtain the frequency spectrum diagram of the flow echo curve, and then calculate the power spectral density of each frequency in the frequency spectrum diagram, and use the obtained power spectral densities as the power spectral densities of each frequency of the flow echo curve respectively.
[0057] In specific implementation, identifying the energy transfer characteristics in the first blood sample based on all the power spectral densities can be achieved in the following way, that is: First, fit all the power spectral densities into a straight line by the autoregressive method in the prior art, where the independent variable is the natural logarithm of power, and the dependent variable is the natural logarithm of the power spectral density, and then use the slope of this straight line as the energy transfer characteristics in the first blood sample.
[0058] It should be noted that in this application, the energy transfer characteristic is a parameter value that measures the degree of energy attenuation of ultrasonic waves in blood. The larger this energy transfer characteristic is, the faster the energy of ultrasonic waves attenuates in blood, and the smaller this energy transfer characteristic is, the slower the energy of ultrasonic waves attenuates in blood.
[0059] In specific implementation, determining the flow characteristics of the first blood sample based on the energy transfer characteristics can be achieved in the following way, that is: First, extract the frequency value with the highest amplitude from the frequency spectrum diagram of the flow echo curve, and then divide the energy transfer characteristic by this frequency value, and use the obtained quotient as the flow characteristics of the first blood sample.
[0060] It should be noted that in this application, the flow characteristic is a parameter value that measures the degree of turbulence chaos of the first blood sample. The larger this flow characteristic is, the more chaotic the turbulence of the first blood sample is, and the smaller this flow characteristic is, the more stable the turbulence of the first blood sample is.
[0061] In some embodiments, determining the aggregation index of red blood cells in the unfrozen blood of the target user by combining the shrinkage rate and the flow characteristics means taking the ratio of the shrinkage rate to the flow characteristics as the aggregation index of red blood cells in the unfrozen blood of the target user.
[0062] It should be noted that in this application, the aggregation index is a parameter value for measuring the number of aggregated red blood cells in the blood in the unfrozen state. The larger the aggregation index, the more aggregated red blood cells in the blood, and the smaller the aggregation index, the fewer aggregated red blood cells in the blood.
[0063] In step 104, a second blood sample is extracted from the blood collected from the target user, and the second blood sample is subjected to electrical impedance detection to obtain the hematocrit of the red blood cells in the blood of the target user.
[0064] Specifically, extracting a second blood sample from the blood collected from the target user can be achieved in the following manner: 5 ml of whole blood is extracted from the blood collected from the target user, and the extracted whole blood is used as the second blood sample.
[0065] It should be noted that the "second" in this application is only used to distinguish different blood samples, does not represent the order, and does not limit the blood samples. The first blood sample and the second blood sample in this application are only used to distinguish to indicate that these are two blood samples for different test processes.
[0066] Specifically, performing electrical impedance detection on the second blood sample to obtain the hematocrit of the red blood cells in the blood of the target user can be achieved in the following manner: According to the method in the literature "Comparison of the results of hematocrit measured by instrument method and Wintrobe method and evaluation of diagnostic tests", the SYSMEXXT-1800i blood cell counter is used to detect the second blood sample to obtain the hematocrit of the red blood cells in the blood of the target user.
[0067] It should be noted that the electrical impedance detection in this application refers to the detection process of counting the red blood cells in the blood through the Coulter principle in electronics, and then obtaining the hematocrit of the red blood cells in the blood.
[0068] In step 105, the hematocrit of the blood of the target user is corrected based on the aggregation index, and the blood quality of the target user is evaluated through the corrected hematocrit.
[0069] In some embodiments, correcting the hematocrit of the blood of the target user based on the aggregation index can be achieved through the following steps: Obtain the volume of the second blood sample Determine the correction index based on the aggregation index and the volume of the second blood sample; Calibrate the hematocrit in the blood of the target user according to the calibration index.
[0070] In specific implementation, determining the calibration index based on the aggregation index and the volume of the second blood sample can be achieved in the following manner: First, obtain the volume of the second blood sample. Then, multiply the volume of the second blood sample by the aggregation index, and input the opposite number of the obtained product into the natural exponential function. Subsequently, use the output value of the natural exponential function as the calibration index.
[0071] It should be noted that in this application, the calibration index is a dimensionless coefficient used to calibrate the hematocrit in the blood.
[0072] In specific implementation, calibrating the hematocrit in the blood of the target user according to the calibration index can be achieved in the following manner: Divide the hematocrit in the blood of the target user by the calibration index, and use the obtained value as the calibrated hematocrit.
[0073] In addition, in specific implementation, evaluating the blood quality of the target user through the calibrated hematocrit can be achieved in the following manner: First, obtain the gender of the target user. Then, query the range of normal reference values of the corresponding hematocrit according to the gender of the target user (usually 0.40 - 0.50 for males and 0.35 - 0.45 for females). Subsequently, compare the calibrated hematocrit with the range of normal reference values. If the calibrated hematocrit is lower than this range, it is evaluated as anemia. If the calibrated hematocrit is higher than this range, it is evaluated as abnormal increase in red blood cells. If the calibrated hematocrit is within this range, it is evaluated as normal blood.
[0074] In addition, on the other hand of this application, in some embodiments, this application provides a blood collection system for a blood station, including a blood collection and detection unit. Refer to Figure 4 This figure is a schematic structural diagram of the blood collection and detection unit shown in some embodiments of this application. The blood collection and detection unit 400 includes: a coagulation test module 401, a processing module 402, a hematocrit detection module 403, and an execution module 404, which are described as follows: The coagulation test module 401. In this application, the collection module 401 is mainly used to extract a first blood sample from the blood collected from the target user, add a coagulation-promoting reagent to the first blood sample for blood coagulation testing, and emit ultrasonic waves to the first blood sample during the testing process, and then extract the backscattered signal after the blood shows a turbulent phenomenon. The processing module 402. In the present application, the processing module 402 is mainly used to decompose the backscattering signal into component signals of multiple modes, and then divide all the component signals into linear component signals representing fibrin contraction in blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal; It should be noted that in the present application, the processing module 402 is further used to extract the contraction rate of fibrin in the first blood sample from all the linear component signals, determine the flow characteristics of the first blood sample through all the nonlinear component signals, and combine the contraction rate and the flow characteristics to determine the aggregation index of red blood cells in the non-coagulated blood of the target user; The hematocrit detection module 403. In the present application, the hematocrit detection module 403 is mainly used to extract a second blood sample from the blood collected from the target user, perform resistance induction detection on the second blood sample, and obtain the hematocrit in the blood of the target user; The execution module 404. In the present application, the execution module 404 is mainly used to correct the hematocrit in the blood of the target user based on the aggregation index, and evaluate the blood quality of the target user through the corrected hematocrit.
[0075] In addition, the present application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned blood collection detection method for blood stations.
[0076] In some embodiments, refer to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the blood collection detection method for blood stations according to some embodiments of the present application. The blood collection detection method for blood stations in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.
[0077] The processor 501 can be a general central processing unit (CPU) or an application-specific integrated circuit (ASIC).
[0078] The communication bus 502 can be used to transmit information between the above components.
[0079] The memory 503 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.
[0080] Among them, the memory 503 is used to store the program code for executing the solution of this application and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The blood collection detection method for blood stations in the above embodiments can be implemented through one or more software modules in the program code in the processor 501 and the memory 503.
[0081] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.
[0082] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).
[0083] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.
[0084] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned blood collection detection method for a blood bank is implemented.
[0085] In summary, in the blood collection system and blood collection detection method for a blood bank disclosed in the embodiments of the present application, first, a first blood sample is extracted from the blood collected from a target user, a coagulant reagent is added to the first blood sample for a blood coagulation test, and ultrasonic waves are emitted to the first blood sample during the test, and then the backscattered signal after the blood exhibits a turbulent phenomenon is extracted; the backscattered signal is decomposed into component signals of multiple modes, and then all the component signals are divided into linear component signals representing fibrin contraction in the blood and nonlinear component signals representing blood turbulent motion according to the fractal index of each component signal; the contraction rate of fibrin in the first blood sample is extracted from all the linear component signals, and the flow characteristics of the first blood sample are determined through all the nonlinear component signals, and the aggregation index of red blood cells in the unfrozen blood of the target user is determined by combining the contraction rate and the flow characteristics; a second blood sample is extracted from the blood collected from the target user, and a resistance induction detection is performed on the second blood sample to obtain the hematocrit in the blood of the target user; the hematocrit in the blood of the target user is corrected based on the aggregation index, and the blood quality of the target user is evaluated through the corrected hematocrit.
[0086] It can be seen that in the present application, through the coagulation test of ultrasonic detection of blood, the linear component signal and the non-linear component signal are extracted from the ultrasonic echo. During the blood coagulation process, fibrin shrinks, and a large number of red blood cells are wrapped by the network structure formed by fibrin to form large particles. This process of fibrin wrapping red blood cells is usually carried out in an orderly manner, and the formed large particles have a predictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of fibrin shrinkage corresponds to the component of the linear part in the backscattering signal (linear component signal). As the large particles are formed, the Reynolds number of the blood increases, causing disordered turbulent motion in the blood. This turbulent motion has an unpredictable impact on the intensity of the backscattering signal of ultrasonic waves, that is, the impact of the turbulent motion corresponds to the component of the non-linear part in the backscattering signal (non-linear component signal). Furthermore, the rate of fibrin shrinkage and the flow characteristics of the turbulent flow in the blood are analyzed through the linear component signal and the non-linear component signal. Subsequently, the number of red blood cell aggregations (i.e., the aggregation index) in the non-coagulated state of the blood is evaluated by combining the rate of fibrin shrinkage and the flow characteristics of the turbulent flow in the blood. Finally, the hematocrit obtained by conventional detection (i.e., electrical impedance detection) is corrected by the aggregation index. In summary, the present application can perform a confidence detection of hematocrit under the influence of red blood cell aggregation.
[0087] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.
[0088] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.
Claims
1. A blood collection and detection method for a blood station, characterized in that, Including: Extracting a first blood sample from the blood collected from the target user, adding a coagulation promoting reagent to the first blood sample for blood coagulation testing, and emitting ultrasonic waves to the first blood sample during the testing process, and then extracting the backscattering signal after the blood exhibits a turbulent phenomenon; Decomposing the backscattering signal into component signals of multiple modes, and then classifying all component signals into linear component signals characterizing fibrin contraction in the blood and nonlinear component signals characterizing blood turbulent motion according to the fractal index of each component signal; Extracting the contraction rate of fibrin in the first blood sample from all linear component signals, determining the flow characteristics of the first blood sample through all nonlinear component signals, and combining the contraction rate and the flow characteristics to determine the aggregation index of red blood cells in the unfrozen blood of the target user; Extracting a second blood sample from the blood collected from the target user, performing electrical impedance sensing detection on the second blood sample to obtain the hematocrit in the blood of the target user; Correcting the hematocrit in the blood of the target user based on the aggregation index, and evaluating the blood quality of the target user through the corrected hematocrit.
2. The method according to claim 1, characterized in that, Adding a coagulation promoting reagent to the first blood sample for blood coagulation testing means mixing the first blood sample with the coagulation promoting reagent and then placing it in a constant temperature water bath for water insulation until the blood is completely coagulated.
3. The method according to claim 1, characterized in that, Emitting ultrasonic waves to the first blood sample during the testing process and then extracting the backscattering signal after the blood exhibits a turbulent phenomenon specifically includes: Emitting ultrasonic waves with a fixed frequency to the first blood sample during the blood coagulation testing; Receiving the echo signal of the ultrasonic waves through a pulse receiver; Identifying the Reynolds point where the blood exhibits a turbulent phenomenon in the echo signal; Extracting the backscattering signal after the blood exhibits a turbulent phenomenon from the echo signal according to the Reynolds point.
4. The method according to claim 1, wherein Decomposing the backscattering signal into component signals of multiple modes specifically includes: Fitting the backscattering signal into a backscattering curve; Performing wavelet decomposition on the backscattering curve through a selected wavelet basis to obtain component signals of multiple modes.
5. The method according to claim 1, characterized in that Classifying all component signals into linear component signals characterizing fibrin contraction in the blood and nonlinear component signals characterizing blood turbulent motion according to the fractal index of each component signal specifically includes: Determining the signal amplitude of each component signal; Removing the noise signals in all component signals according to all the signal amplitudes, and taking all the remaining component signals as effective signals; Determining the fractal index of each effective signal; Comparing all the fractal indices with a preset fractal threshold, and taking all the effective signals with fractal indices less than or equal to the fractal threshold as linear component signals characterizing fibrin contraction in the blood; Taking all the effective signals with fractal indices greater than the fractal threshold as nonlinear component signals characterizing blood turbulent motion.
6. The method according to claim 1, characterized in that, Extracting the contraction rate of fibrin in the first blood sample from all linear component signals specifically includes: Reconstructing all the linear component signals into a contraction echo curve describing fibrin contraction in the blood; Determine the autocorrelation sequence of the contraction echo curve; Perform quadratic fitting on the contraction echo curve according to the autocorrelation sequence, and then determine the contraction rate of fibrin in the first blood sample.
7. The method according to claim 1, wherein Determining the flow characteristics of the first blood sample through all the non-linear component signals specifically includes: Reconstruct all the non-linear component signals into a flow echo curve of the turbulent motion of blood; Determine the power spectral density of each frequency of the flow echo curve; Identify the energy transfer characteristics in the first blood sample according to all the power spectral densities; Determine the flow characteristics of the first blood sample according to the energy transfer characteristics.
8. The method according to claim 1, wherein Perform resistance induction detection on the second blood sample by a blood cell counter.
9. The method according to claim 1, wherein Based on the aggregation index, correcting the hematocrit in the blood of the target user specifically includes: Obtain the volume of the second blood sample; Determine a correction index based on the aggregation index and the volume of the second blood sample; Correct the hematocrit in the blood of the target user according to the correction index.
10. A blood collection system for a blood bank, comprising a blood collection and detection unit, characterized in that, The blood collection and detection unit includes: A coagulation test module, configured to extract a first blood sample from the blood collected from a target user, add a coagulation-promoting reagent to the first blood sample to perform a blood coagulation test, and emit ultrasonic waves to the first blood sample during the test, and then extract the backscattered signal after the blood has a turbulent phenomenon; A processing module, configured to decompose the backscattered signal into component signals of multiple modes, and then divide all the component signals into linear component signals representing fibrin contraction in blood and non-linear component signals representing blood turbulent motion according to the fractal index of each component signal; The processing module is further configured to extract the contraction rate of fibrin in the first blood sample from all the linear component signals, determine the flow characteristics of the first blood sample through all the non-linear component signals, and combine the contraction rate and the flow characteristics to determine the aggregation index of red blood cells in the non-coagulated blood of the target user; A hematocrit detection module, configured to extract a second blood sample from the blood collected from a target user, perform resistance induction detection on the second blood sample, and obtain the hematocrit in the blood of the target user; An execution module, configured to correct the hematocrit in the blood of the target user based on the aggregation index, and evaluate the blood quality of the target user through the corrected hematocrit.