Blood cell volume histogram statistics method, apparatus, medium, and device

Abstract

The application discloses a blood cell volume histogram statistical method, device, medium and equipment, first, local width features and global width features of each pulse signal in a pulse signal set are identified, then, the local width features are screened, a local width threshold of normal pulse signals on the local width features is determined, and a pulse signal set to be corrected is screened from the pulse signal set based on the threshold. Then, the amplitude is filtered and the global width features are screened, the pulse signal set to be corrected is filtered and processed to obtain a corrected pulse signal set, a global width threshold of normal pulse signals on the global width features is determined, and a normal pulse signal set is screened from the pulse signal set to be corrected based on the threshold. In this way, abnormal signals can be screened from the dimensions of the local width features, the global width features and the like, and finally, a volume histogram drawn based on the normal pulse signal set will conform to the morphology of normal distribution, which is beneficial to subsequent calculation of morphological parameters.

Description

Technical Field

[0001] This invention relates to the field of technology, and in particular to a method, apparatus, medium, and device for statistical analysis of blood cell volume histograms. Background Technology

[0002] Automated hematology analyzers use impedance spectroscopy to count red blood cells and platelets. When blood cells in a diluted solution pass through the detection aperture, the resistance between the electrodes changes, generating a pulse signal proportional to the cell volume across the electrodes. The number and volume of blood cells are determined based on the pulse amplitude. Due to the characteristics of the electric and flow field distribution inside and outside the aperture, only pulse signals generated by a single cell passing straight through the aperture's axial region can accurately represent cell volume. This type of signal is referred to as a "normal signal." Pulse signals generated by a single cell passing through the aperture via other paths, or by multiple cells passing through the aperture simultaneously, are affected by uneven electric field distribution or signal overlap, and their amplitude cannot accurately represent cell volume. This type of signal is referred to as an "abnormal signal."

[0003] When the material, thickness, size, and length of the gem hole are changed, the probability of a single cell passing through the gem hole through an abnormal path or multiple cells passing through the gem hole simultaneously increases. As a result, the collected pulse signal can no longer accurately represent the cell volume. The blood cell histogram obtained by direct measurement based on the pinhole impedance principle is normally distributed. However, in actual detection, due to the presence of many abnormal signals, the blood cell volume distribution histogram often deviates far from the normal distribution, which in turn leads to the distortion of morphological parameters calculated based on the blood cell volume distribution histogram. Summary of the Invention

[0004] Therefore, it is necessary to provide statistical methods, devices, media, and equipment for blood cell volume histograms to address the problem that existing blood cell volume distribution histograms often deviate significantly from the normal distribution, leading to distortion of morphological parameters calculated from the blood cell volume distribution histograms.

[0005] A method for statistical analysis of blood cell volume histograms, the method comprising:

[0006] A set of pulse signals from a collected blood cell sample is acquired, and the local width feature and global width feature of each pulse signal in the set are identified. The particle distribution of the local width feature is then statistically analyzed. The particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature.

[0007] The local width threshold of the normal pulse signal on the local width feature is determined based on the particle distribution of the local width feature.

[0008] Based on the local width threshold, a set of pulse signals to be corrected is selected from the set of pulse signals, and the particle distribution of the global width feature is statistically analyzed in the set of pulse signals to be corrected.

[0009] The set of pulse signals to be corrected is filtered to obtain a set of corrected pulse signals, and the global width threshold of the normal pulse signal on the global width feature is determined based on the particle distribution of the global width feature.

[0010] Based on the global width threshold, a set of normal pulse signals is selected from the set of pulse signals to be corrected, and a volume histogram is plotted based on the set of normal pulse signals.

[0011] In one embodiment, identifying the local width features and global width features of each pulse signal in the pulse signal set includes:

[0012] The full peak width of each pulse signal in the pulse signal set is identified as a global width feature; wherein, the full peak width indicates the time width between the start point and the end point of the pulse signal;

[0013] The preceding peak width of each pulse signal in the pulse signal set is identified as a local width feature, and / or the following peak width of each pulse signal in the pulse signal set is identified as a local width feature; wherein, the preceding peak width indicates the time width between the pulse peak point and the first feature point on the same pulse signal, the amplitude of the first feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the starting point, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point; the following peak width indicates the time width between the pulse peak point and the second feature point on the same pulse signal, the amplitude of the second feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the ending point, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point.

[0014] In one embodiment, determining the local width threshold of the normal pulse signal on the local width feature based on the particle distribution of the local width feature includes:

[0015] Based on the particle distribution of the front peak width, obtain the first front peak width feature value with the highest particle number distribution, and the second front peak width feature value with the second highest particle number distribution. Use the larger of the first and second front peak width feature values ​​as the front peak width threshold, which serves as the local width threshold for the front peak width; and / or,

[0016] Based on the particle distribution of the back peak width, obtain the first back peak width feature value with the largest particle number distribution and the second back peak width feature value with the second largest particle number distribution. Take the larger value between the first back peak width feature value and the second back peak width feature value as the back peak width threshold, and use it as the local width threshold of the back peak width.

[0017] In one embodiment, the step of filtering the set of pulse signals to be corrected from the set of pulse signals based on the local width threshold includes:

[0018] In the set of pulse signals, pulse signals that meet the first screening criterion and / or the second screening criterion are selected as the set of pulse signals to be corrected; wherein, the first screening criterion is that the front peak width feature value is less than or equal to the front peak width threshold, the second screening criterion is that the back peak width feature value is less than or equal to the back peak width threshold, and when the identified local width feature includes both the front peak width and the back peak width, the first screening criterion and the second screening criterion must be met simultaneously.

[0019] In one embodiment, the formula for filtering is:

[0020]

[0021] In the above formula, F(Ful i To correct the particle distribution of the pulse signal set, Ful i The particle distribution of the pulse signal set to be corrected is represented by F, where F indicates the total number of pulses in the pulse signal set, u indicates the mean, and σ indicates the standard deviation.

[0022] In one embodiment, determining the global width threshold of the normal pulse signal on the global width feature based on the particle distribution of the global width feature includes:

[0023] In the set of corrected pulse signals, the full peak width feature value corresponding to the upper limit of the preset signal interval is used as the full peak width threshold, which is then used as the global width threshold.

[0024] In one embodiment, the step of filtering the set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold includes:

[0025] In the set of pulse signals to be corrected, pulse signals that meet the third screening criterion are taken as the set of normal pulse signals; wherein, the third screening criterion is that the full peak width feature value is less than or equal to the full peak width threshold.

[0026] A blood cell volume histogram counting device, the device comprising:

[0027] The feature recognition module is used to acquire the pulse signal set of the collected blood cell sample, identify the local width feature and global width feature of each pulse signal in the pulse signal set, and count the particle distribution of the local width feature; wherein, the particle distribution is used to indicate the number of particles with different feature values ​​under the pulse feature;

[0028] A local width threshold determination module is used to determine the local width threshold of a normal pulse signal on the local width feature based on the particle distribution of the local width feature.

[0029] The first filtering module is used to filter out a set of pulse signals to be corrected from the set of pulse signals based on the local width threshold, and to statistically analyze the particle distribution of the global width feature in the set of pulse signals to be corrected.

[0030] The global width threshold determination module is used to filter the set of pulse signals to be corrected to obtain a set of corrected pulse signals, and to determine the global width threshold of the normal pulse signal on the global width feature based on the particle distribution of the global width feature.

[0031] The second filtering module is used to filter out a set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold.

[0032] A plotting module is used to plot a volume histogram based on the normal pulse signal set.

[0033] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described blood cell volume histogram statistical method.

[0034] A blood cell volume histogram statistical device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the blood cell volume histogram statistical method described above.

[0035] This invention provides a method, apparatus, medium, and device for statistical analysis of blood cell volume histograms. First, it identifies the local and global width features of each pulse signal in a pulse signal set. Next, it filters the local width features, including determining a local width threshold for normal pulse signals based on the statistical particle distribution of the local width features, and filtering out a set of pulse signals to be corrected from the pulse signal set based on this local width threshold. Then, it performs amplitude filtering and global width feature filtering, including filtering the set of pulse signals to be corrected to obtain a corrected pulse signal set, determining a global width threshold for normal pulse signals based on the statistical particle distribution of the global width features, and filtering out a set of normal pulse signals from the set of pulse signals to be corrected based on this global width threshold. In this way, abnormal signals can be filtered from various dimensions such as local and global width features. Finally, the volume histogram drawn based on the normal pulse signal set will conform to a normal distribution, facilitating subsequent calculation of morphological parameters. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] in:

[0038] Figure 1 A flowchart illustrating the statistical method for the histogram of blood cell volume.

[0039] Figure 2 Histogram of blood cell volume plotted using existing techniques;

[0040] Figure 3 A schematic diagram for determining the start point, end point, pulse peak point, first characteristic point, and second characteristic point in normal and abnormal signals;

[0041] Figure 4 Blood cell volume histogram plotted for this invention;

[0042] Figure 5 A schematic diagram of a blood cell volume histogram statistical device;

[0043] Figure 6 This is a block diagram of a blood cell volume histogram statistical device. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0046] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0047] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a blood cell volume histogram statistical method in one embodiment. This method addresses the following: Figure 2 The phenomenon shown is that, due to the presence of many abnormal signals, the blood cell volume distribution histogram drawn by existing technology often deviates far from the normal distribution, which in turn leads to the distortion of morphological parameters calculated based on the blood cell volume distribution histogram.

[0048] To address the above issues, the steps provided by the blood cell volume histogram statistical method in this embodiment include:

[0049] S101, acquire the pulse signal set of the collected blood cell sample, identify the local width feature and global width feature of each pulse signal in the pulse signal set, and statistically analyze the particle distribution of the local width feature.

[0050] Among them, the fully automated blood cell analyzer uses impedance method to detect the count of blood cells such as red blood cells and platelets. When a single blood cell in the diluted solution passes through the detection gem hole, the resistance between the electrodes changes, forming a pulse signal proportional to the cell volume at both ends of the electrodes. When a series of blood cells pass through the detection gem hole in sequence, a pulse signal set will be generated.

[0051] In this set of pulse signals, it is necessary to identify the local width features and global width features of each pulse signal. As for how to define the "local" and "global" widths, it can be determined according to the actual situation. For example, half the width of the pulse signal can be considered as local.

[0052] In one specific embodiment, the local width features and global width features of each pulse signal in the pulse signal set are identified in the following manner:

[0053] According to the pulse recognition algorithm, the full peak width of each pulse signal in the pulse signal set is identified as the global width feature; the initial peak width of each pulse signal in the pulse signal set is identified as the local width feature; and / or, the subsequent peak width of each pulse signal in the pulse signal set is identified as the local width feature. Here, the full peak width indicates the time width between the start and end points of the pulse signal. The initial peak width indicates the time width between the pulse peak point and the first feature point on the same pulse signal, where the amplitude of the first feature point is half the amplitude difference between the pulse peak point and the start point amplitude, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point. The subsequent peak width indicates the time width between the pulse peak point and the second feature point on the same pulse signal, where the amplitude of the second feature point is half the amplitude difference between the pulse peak point and the end point amplitude, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point.

[0054] For example, with Figure 3 For example, Figure 3 (a) represents a “normal signal”, where “1” is the start point; “2” is the end point; “3” is the peak point of the pulse; the amplitude of “4” is half the difference between the amplitudes of “3” and “1”, “4” is the first characteristic point, and the time width from “4” to “3” is the width of the first peak; the amplitude of “5” is half the difference between the amplitudes of “3” and “2”, “5” is the second characteristic point, and the time width from “5” to “3” is the width of the second peak.

[0055] Figure 3(b) is an “abnormal signal”, which will be identified as two independent pulses, “b1” and “b2”. “1” is the starting point of “b1”; “2” is the ending point of “b1”; “3” is the peak point of “b1”; the amplitude of “4” is half of the amplitude difference between “3” and “1”; “4” is the first characteristic point of “b1”; the time width from “4” to “3” is the first peak width of “b1”; the amplitude of “5” is half of the amplitude difference between “3” and “2”; “5” is the second characteristic point of “b1”; the time width from “5” to “3” is the second peak width of “b1”. “2” is the starting point of “b2”; “6” is the ending point of “b2”; “7” is the peak point of “b2”; the amplitude of “8” is half of the amplitude difference between “7” and “2”, “8” is the first characteristic point of “b2”, and the time width from “8” to “7” is the width of the first peak of “b2”; the amplitude of “9” is half of the amplitude difference between “7” and “6”, “9” is the second characteristic point of “b2”, and the time width from “9” to “7” is the width of the second peak of “b2”.

[0056] Furthermore, by summarizing the distribution information of the first peak width, last peak width, and total peak width of all pulse signals, we can obtain... in Let A represent the a-th pulse signal, and A represent the total number of pulse signals; k represents the k-th pulse feature of the pulse signal, and K represents the total number of pulse features; optionally, let k=1 in the pulse feature represent the "front peak width", k=2 in the pulse feature represent the "back peak width", k=3 in the pulse feature represent the "full peak width", and k=4 in the pulse feature represent the "pulse peak value".

[0057] Finally, the particle distribution of the local width feature is statistically analyzed, and this particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature. If all pulse signals are statistically analyzed... The distribution of particle numbers under various characteristic values ​​of the pulse characteristic "pre-peak width", i.e., the statistical Pre i (i∈1,2,3,…P), where P represents the total number of eigenvalues ​​of the first peak width, Pre i This represents the total number of pulse signals whose first peak value is equal to the width of the i-th first peak among all pulse signals. If we count all pulse signals... The distribution of particle numbers under various characteristic values ​​of the pulse characteristic "pre-peak width", i.e., the statistical Sub i (i∈1,2,3,…S), where S represents the total number of eigenvalues ​​for the width of the subsequent peak, Sub i This represents the total number of pulse signals whose peak value is equal to the width of the i-th pulse.

[0058] S102, Determine the local width threshold of the normal pulse signal based on the particle distribution of the local width feature.

[0059] The local width threshold is the threshold used to distinguish between a pulse signal as a "normal signal" and an "abnormal signal" based on the feature of local width.

[0060] In one specific embodiment, if the local width feature is "front peak width" and / or "back peak width", the local width threshold is determined as follows:

[0061] Based on the particle distribution of the front peak width, obtain the first front peak width feature value with the largest particle number distribution and the second front peak width feature value with the second largest particle number distribution. Use the larger value between the first and second front peak width feature values ​​as the front peak width threshold, which serves as the local width threshold for the front peak width; and / or, based on the particle distribution of the back peak width, obtain the first back peak width feature value with the largest particle number distribution and the second back peak width feature value with the second largest particle number distribution. Use the larger value between the first and second back peak width feature values ​​as the back peak width threshold, which serves as the local width threshold for the back peak width.

[0062] For example, regarding the front peak width, in the recorded particle distribution... i In a sequence (i∈1,2,3,…P), if there are 100 pulse signals, and 100 of them have a peak width of A (pre1 = 100), representing the largest proportion, this can be denoted as the first peak width feature value A. Meanwhile, 50 of these pulse signals have a peak width of B (pre2 = 50), representing the second largest proportion, which can be denoted as the second peak width feature value B. Then, A and B are compared. If A is larger, A is used as the peak width threshold, serving as the local width threshold for the peak width. If B is larger, B is used as the peak width threshold, serving as the local width threshold Th1 for the peak width.

[0063] Similarly, regarding the width of the rear peak, in the recorded particle distribution Sub i In a sequence (i∈1,2,3,…S), if there are 1000 pulse signals, and 100 of them have a peak width of A (sub1 = 100), representing the largest proportion, this can be denoted as the first peak width characteristic value A. Meanwhile, 50 of these pulse signals have a peak width of B (sub2 = 50), representing the second largest proportion, which can be denoted as the second peak width characteristic value B. Next, A and B are compared. If A is larger, it is used as the peak width threshold, serving as the local width threshold for the peak width. If B is larger, it is used as the peak width threshold, serving as the local width threshold Th2 for the peak width.

[0064] S103, based on the local width threshold, select the pulse signal set to be corrected from the pulse signal set, and statistically analyze the particle distribution of global width features in the pulse signal set to be corrected.

[0065] Among them, the pulse signals in the pulse signal set to be corrected meet the requirements of normal signals in terms of local width characteristics.

[0066] In one specific embodiment, the set of pulse signals to be corrected is selected in the following manner:

[0067] Within the pulse signal set, pulse signals that meet the first and / or second screening criteria are selected as the set of pulse signals to be corrected. The first screening criterion is that the front peak width feature value is less than or equal to the front peak width threshold, expressed as follows: The second screening criterion is that the characteristic value of the subsequent peak width is less than or equal to the threshold value of the subsequent peak width, expressed as follows: Furthermore, when the identified local width features include both the front peak width and the back peak width, both the first screening criterion and the second screening criterion must be met simultaneously.

[0068] Next, the particle number distribution of the pulse signal set to be corrected is statistically analyzed at various characteristic values ​​of the "full peak width" of the pulse feature, i.e., statistical Ful i (i∈1,2,3,…F), where F represents the total number of eigenvalues ​​of the full peak width, Ful i This represents the total number of pulse signals whose total peak value is equal to the i-th full peak width characteristic value.

[0069] S104, the set of pulse signals to be corrected is filtered to obtain the set of corrected pulse signals, and the global width threshold of the normal pulse signal on the global width feature is determined based on the particle distribution of the global width feature.

[0070] Although the pulse signals in the pulse signal set to be corrected in S103 meet the requirements of normal signals in terms of local width characteristics, they do not fully meet the requirements of normal signals in terms of amplitude. Therefore, they are filtered in the subsequent S104.

[0071] In one specific embodiment, the formula for filtering is:

[0072]

[0073] In the above formula, F(Ful i To correct the particle distribution of the pulse signal set, it can also be denoted as... Ful i This represents the particle distribution of the pulse signal set to be corrected. F indicates the total number of pulses in the set, u indicates the mean, and σ indicates the standard deviation. σ and u are adjustable parameters and need to be adjusted according to the actual situation.

[0074] In correcting the pulse signal set In this method, the full peak width feature value corresponding to the upper limit of the preset confidence interval is used as the full peak width threshold, which is then used as the global width threshold Th3. Optionally, the preset confidence interval here is the 95% confidence interval, but other intervals can also be set.

[0075] S105, based on the global width threshold, filter out the normal pulse signal set from the pulse signal set to be corrected, and draw a volume histogram based on the normal pulse signal set.

[0076] In other words, this set of normal pulse signals has limitations on local width characteristics, global width characteristics, and amplitude. Therefore, the pulse signals in it meet the requirements of normal signals in terms of local width characteristics, global width characteristics, and amplitude.

[0077] In one specific embodiment, the normal pulse signal set is selected in the following manner:

[0078] In the set of pulse signals to be corrected, pulse signals that meet the third screening criterion are considered as the normal pulse signal set; the third screening criterion is that the full peak width characteristic value is less than or equal to the full peak width threshold. Therefore, it can also be understood as the set of pulse signals acquired from S101. In the middle, filter out all that simultaneously meet the requirements. And / or, and The pulse signal.

[0079] Finally, a histogram of red blood cell volume distribution was obtained based on these pulse signals, and the results are as follows: Figure 4 As shown. This can improve the accuracy of morphological parameter calculations to a certain extent, achieving an effect similar to the morphology of the erythrocyte volume distribution histogram obtained by the sheath flow impedance method.

[0080] The aforementioned blood cell volume histogram statistical method first identifies the local and global width features of each pulse signal in the pulse signal set. Next, it filters the local width features, including determining the local width threshold for normal pulse signals based on the statistical particle distribution of local width features, and then filtering out the pulse signal set to be corrected based on this local width threshold. Then, it performs amplitude filtering and global width feature filtering, including filtering the pulse signal set to be corrected to obtain the corrected pulse signal set, determining the global width threshold for normal pulse signals based on the statistical particle distribution of global width features, and then filtering out the normal pulse signal set from the pulse signal set to be corrected based on this global width threshold. In this way, abnormal signals can be filtered from various dimensions such as local and global width features. Finally, the volume histogram drawn based on the normal pulse signal set will conform to a normal distribution, facilitating subsequent calculation of morphological parameters.

[0081] In one embodiment, such as Figure 5 As shown, a blood cell volume histogram statistical device is proposed, which includes:

[0082] The feature recognition module 501 is used to acquire the pulse signal set of the collected blood cell sample, identify the local width feature and global width feature of each pulse signal in the pulse signal set, and count the particle distribution of the local width feature; wherein, the particle distribution is used to indicate the number of particles under different feature values ​​of the pulse feature;

[0083] The local width threshold determination module 502 is used to determine the local width threshold of the normal pulse signal on the local width feature based on the particle distribution of the local width feature;

[0084] The first filtering module 503 is used to filter out the set of pulse signals to be corrected from the set of pulse signals based on the local width threshold, and to statistically analyze the particle distribution of global width features in the set of pulse signals to be corrected.

[0085] The global width threshold determination module 504 is used to filter the set of pulse signals to be corrected to obtain the set of corrected pulse signals, and to determine the global width threshold of the normal pulse signal on the global width feature based on the particle distribution of the global width feature.

[0086] The second filtering module 505 is used to filter out a set of normal pulse signals from the set of pulse signals to be corrected based on a global width threshold.

[0087] The plotting module 506 is used to plot a volume histogram based on a normal pulse signal set.

[0088] Figure 6An internal structural diagram of a blood cell volume histogram counting device in one embodiment is shown. Figure 6 As shown, the blood cell volume histogram statistical device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement the blood cell volume histogram statistical method. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to perform the blood cell volume histogram statistical method. Those skilled in the art will understand that… Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the blood cell volume histogram statistical device to which the present application is applied. The specific blood cell volume histogram statistical device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0089] A computer-readable storage medium storing a computer program, which, when executed by a processor, performs the following steps: acquiring a set of pulse signals from a collected blood cell sample; identifying the local width feature and global width feature of each pulse signal in the set of pulse signals; and statistically analyzing the particle distribution of the local width feature; determining a local width threshold for normal pulse signals based on the particle distribution of the local width feature; selecting a set of pulse signals to be corrected from the set of pulse signals based on the local width threshold; statistically analyzing the particle distribution of the global width feature in the set of pulse signals to be corrected; filtering the set of pulse signals to be corrected to obtain a corrected set of pulse signals; and determining a global width threshold for normal pulse signals based on the particle distribution of the global width feature; selecting a set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold; and plotting a volume histogram based on the set of normal pulse signals.

[0090] A blood cell volume histogram statistical device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: acquiring a set of pulse signals from a collected blood cell sample; identifying the local width feature and global width feature of each pulse signal in the set; and statistically analyzing the particle distribution of the local width feature; determining a local width threshold for normal pulse signals based on the particle distribution of the local width feature; filtering a set of pulse signals to be corrected from the set of pulse signals based on the local width threshold; statistically analyzing the particle distribution of the global width feature in the set of pulse signals to be corrected; filtering the set of pulse signals to be corrected to obtain a corrected set of pulse signals; and determining a global width threshold for normal pulse signals based on the particle distribution of the global width feature; filtering a set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold; and plotting a volume histogram based on the normal pulse signal set.

[0091] It should be noted that the above-mentioned blood cell volume histogram statistical method, apparatus, device, and computer-readable storage medium belong to the same general inventive concept, and the contents of the embodiments of the blood cell volume histogram statistical method, apparatus, device, and computer-readable storage medium are applicable to each other.

[0092] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for statistical analysis of blood cell volume histograms, characterized in that, The method includes: Acquire a set of pulse signals from a collected blood cell sample, identify the local width feature and global width feature of each pulse signal in the set, and statistically analyze the particle distribution of the local width feature; wherein the particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature, and identifying the local width feature and global width feature of each pulse signal in the set includes: identifying the full peak width of each pulse signal in the set as the global width feature; wherein the full peak width indicates the time width between the start point and the end point of the pulse signal; identifying the first peak width of each pulse signal in the set as the local width feature, and / or identifying the... The peak width of each pulse signal in the pulse signal set is used as a local width feature; wherein, the peak width indicates the time width between the pulse peak point and the first feature point on the same pulse signal, the amplitude of the first feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the starting point, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point; the peak width indicates the time width between the pulse peak point and the second feature point on the same pulse signal, the amplitude of the second feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the ending point, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point; Determining the local width threshold of the normal pulse signal based on the particle distribution of the local width feature, the step of determining the local width threshold of the normal pulse signal based on the particle distribution of the local width feature includes: obtaining a first front peak width feature value with the largest particle number distribution and a second front peak width feature value with the second largest particle number distribution based on the particle distribution of the front peak width, and using the larger value of the first front peak width feature value and the second front peak width feature value as the front peak width threshold, so as to serve as the local width threshold of the front peak width; and / or, obtaining a first back peak width feature value with the largest particle number distribution and a second back peak width feature value with the second largest particle number distribution based on the particle distribution of the back peak width, and using the larger value of the first back peak width feature value and the second back peak width feature value as the back peak width threshold, so as to serve as the local width threshold of the back peak width; Based on the local width threshold, a set of pulse signals to be corrected is selected from the set of pulse signals, and the particle distribution of the global width feature is statistically analyzed in the set of pulse signals to be corrected. The set of pulse signals to be corrected is filtered to obtain a set of corrected pulse signals, and the global width threshold of the normal pulse signal on the global width feature is determined based on the particle distribution of the global width feature. This includes: in the set of corrected pulse signals, the full peak width feature value corresponding to the upper limit of the preset signal interval is used as the full peak width threshold, which is then used as the global width threshold. Based on the global width threshold, a set of normal pulse signals is selected from the set of pulse signals to be corrected, and a volume histogram is plotted based on the set of normal pulse signals.

2. The method according to claim 1, characterized in that, The step of filtering the set of pulse signals to be corrected from the set of pulse signals based on the local width threshold includes: In the set of pulse signals, pulse signals that meet the first screening criterion and / or the second screening criterion are selected as the set of pulse signals to be corrected; wherein, the first screening criterion is that the front peak width feature value is less than or equal to the front peak width threshold, the second screening criterion is that the back peak width feature value is less than or equal to the back peak width threshold, and when the identified local width feature includes both the front peak width and the back peak width, the first screening criterion and the second screening criterion must be met simultaneously.

3. The method according to claim 1, characterized in that, The formula for filtering is: In the above formula, To correct the particle distribution of the pulse signal set, The particle distribution of the pulse signal set to be corrected is represented by F, which indicates the total number of pulses in the pulse signal set. Indicator mean, Indicative standard deviation.

4. The method according to claim 1, characterized in that, The step of filtering out a set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold includes: In the set of pulse signals to be corrected, pulse signals that meet the third screening criterion are taken as the set of normal pulse signals; wherein, the third screening criterion is that the full peak width feature value is less than or equal to the full peak width threshold.

5. A blood cell volume histogram statistical device, characterized in that, The device includes: A feature recognition module is used to acquire a set of pulse signals from a collected blood cell sample, identify the local width feature and global width feature of each pulse signal in the set, and statistically analyze the particle distribution of the local width feature. The particle distribution indicates the number of particles at different feature values ​​under the pulse feature. Identifying the local width feature and global width feature of each pulse signal in the set includes: identifying the full peak width of each pulse signal as the global width feature; wherein the full peak width indicates the time width between the start and end points of the pulse signal; identifying the initial peak width of each pulse signal as the local width feature; and / or, identifying... The peak width of each pulse signal in the pulse signal set is used as a local width feature; wherein, the peak width indicates the time width between the pulse peak point and the first feature point on the same pulse signal, the amplitude of the first feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the starting point, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point; the peak width indicates the time width between the pulse peak point and the second feature point on the same pulse signal, the amplitude of the second feature point is half of the amplitude difference between the amplitude of the pulse peak point and the amplitude of the ending point, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point; A local width threshold determination module is used to determine a local width threshold for a normal pulse signal on the local width feature based on the particle distribution of the local width feature. The determination includes: obtaining a first front peak width feature value with the highest particle number distribution and a second front peak width feature value with the second highest particle number distribution based on the particle distribution of the front peak width; using the larger of the first and second front peak width feature values ​​as the front peak width threshold; and / or, obtaining a first back peak width feature value with the highest particle number distribution and a second back peak width feature value with the second highest particle number distribution based on the particle distribution of the back peak width; using the larger of the first and second back peak width feature values ​​as the back peak width threshold; and / or, obtaining a first back peak width feature value with the highest particle number distribution and a second back peak width feature value with the second highest particle number distribution based on the particle distribution of the back peak width; using the larger of the first and second back peak width feature values ​​as the back peak width threshold; The first filtering module is used to filter out a set of pulse signals to be corrected from the set of pulse signals based on the local width threshold, and to statistically analyze the particle distribution of the global width feature in the set of pulse signals to be corrected. A global width threshold determination module is used to filter the set of pulse signals to be corrected to obtain a set of corrected pulse signals, and to determine the global width threshold of the normal pulse signal on the global width feature based on the particle distribution of the global width feature, including: in the set of corrected pulse signals, taking the full peak width feature value corresponding to the upper limit of a preset signal interval as the full peak width threshold, and using it as the global width threshold; The second filtering module is used to filter out a set of normal pulse signals from the set of pulse signals to be corrected based on the global width threshold. A plotting module is used to plot a volume histogram based on the normal pulse signal set.

6. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 4.

7. A blood cell volume histogram statistical device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 4.

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