Methods, apparatus, media and equipment for calculating blood cell particle count

Abstract

This invention discloses a method, apparatus, medium, and device for calculating the number of blood cell particles. First, it identifies the pulse characteristics of each pulse signal in a pulse signal set. Then, based on these pulse characteristics and particle distribution, it statistically counts the pulse count of each preset category of pulse signal, thereby enabling targeted and accurate counting of pulse counts for different categories of pulse signals. Furthermore, since different pulse signals have different superposition patterns, it then performs particle count compensation on the pulse count of all preset categories of pulse signals, targeting the abnormal pulse signal characteristics generated by multiple blood cells superimposed through a gemstone aperture, to obtain the total number of particles in the blood cell sample. Therefore, this invention can selectively perform particle count compensation and accurately obtain the total number of particles in a blood cell sample.

Description

Technical Field

[0001] This invention relates to the field of technology, and in particular to a method, apparatus, medium, and device for calculating the number of blood cells. Background Technology

[0002] Fully automated hematology analyzers use electrical impedance to count blood cells, including red blood cells and platelets. After a quantitative sample is diluted with a quantitative amount of conductive solution, it is sent to the analyzer's detection unit. The detection unit has a small opening called a detection orifice. On either side of the orifice are a pair of positive and negative electrodes connected to a constant current power supply. Because cells are poor conductors, when cells in the diluted sample pass through the detection orifice under a constant negative pressure, the DC resistance between the electrodes changes, creating a pulse signal proportional to the cell volume at the electrode terminals. As cells continuously pass through the orifice, a series of electrical pulses are generated at the electrode terminals. The number of pulses is roughly equivalent to the number of cells passing through the orifice. Due to the characteristics of the electric field and flow field distribution inside and outside the aperture, only the pulse signal generated by a single cell passing through the central region of the aperture in a straight line can accurately represent the cell volume. In this paper, this type of signal is referred to as the "normal signal". However, the pulse signal generated by a single cell passing through the aperture through other paths, or the pulse signal generated by multiple cells passing through the aperture simultaneously, is affected by uneven electric field distribution or signal overlap, and the signal amplitude can no longer accurately represent the cell volume. In this paper, this type of signal is referred to as the "abnormal signal".

[0003] If the pulse signal contains only normal signals, the number of blood cells can be counted directly based on the amplitude of the pulse signal. However, under normal testing conditions, the proportion of abnormal signals may even exceed one-third, making it impossible to accurately count the number of blood cell particles passing through the testing process by directly counting the number of blood cells based on the amplitude of the pulse signal, thus affecting the calculation of blood cell concentration values. Summary of the Invention

[0004] Therefore, it is necessary to provide methods, devices, media, and equipment for calculating blood cell particle counts to solve the problem that the number of blood cell particles passing through the detection process cannot be accurately counted, which in turn affects the calculation of blood cell concentration values.

[0005] A method for calculating the number of blood cells, the method comprising:

[0006] The pulse signal set of the collected blood cell sample is obtained, multiple pulse features of each pulse signal in the pulse signal set are identified, and the particle distribution of each pulse signal is statistically analyzed; wherein, the particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature;

[0007] Based on the characteristic values ​​of different pulse characteristics, the pulse signal set is filtered into multiple preset categories of pulse signals, and the number of pulses of each preset category of pulse signal is counted according to the particle distribution.

[0008] Among all preset categories of pulse signals, the number of pulses is compensated for by the number of particles based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

[0009] In one embodiment, identifying multiple pulse features of each pulse signal in the pulse signal set includes:

[0010] Identify the peak value, full peak width, first peak width, and last peak width of each pulse signal in the pulse signal set; wherein, the full peak width indicates the time width between the start point and the end point of the same pulse signal, the first peak width indicates the time width between the peak point of the same pulse signal and the first feature point, the amplitude of the first feature point is half of the amplitude difference between the amplitude of the peak point and the amplitude of the start point, and the acquisition time of the first feature point is before the acquisition time of the peak point; the last peak width indicates the time width between the peak point of the same pulse signal and the second feature point, the amplitude of the second feature point is half of the amplitude difference between the amplitude of the peak point and the amplitude of the end point, and the acquisition time of the second feature point is after the acquisition time of the peak point.

[0011] The step of filtering the pulse signal set into multiple preset categories based on feature values ​​of different pulse characteristics, and counting the number of pulses in each preset category according to the particle distribution, includes:

[0012] Normal pulse signals are selected from the pulse signal set based on the characteristic values ​​of full peak width, front peak width, and back peak width, and the first pulse number of the normal pulse signals is counted according to the particle distribution.

[0013] Based on the characteristic values ​​of the full peak width, the first peak width, the last peak width, and the pulse peak value, peak abnormal pulse signals are screened from the pulse signal set, and the second pulse number of the peak abnormal pulse signals is counted according to the particle distribution.

[0014] Based on the characteristic value limitation of the full peak width, abnormal pulse signals with full peak width are screened from the pulse signal set, and the third pulse number of the abnormal pulse signals with full peak width is counted according to the particle distribution.

[0015] The pulse signals in the pulse signal set other than the normal pulse signal, the peak abnormal pulse signal, and the full peak width abnormal pulse signal are taken as the remaining abnormal pulse signals, and the fourth pulse number of the remaining abnormal pulse signals is calculated based on the total number of pulses in the pulse signal set, the first pulse number, the second pulse number, and the third pulse number.

[0016] In one embodiment, the step of filtering normal pulse signals from the pulse signal set based on the feature values ​​of full peak width, front peak width, and back peak width includes:

[0017] The pulse signals that simultaneously satisfy the following conditions are selected as normal pulse signals: the total peak width feature value is less than a preset first threshold, the front peak width feature value is less than a preset second threshold, and the rear peak width feature value is less than a preset third threshold.

[0018] In one embodiment, the step of filtering out peak-abnormal pulse signals from the pulse signal set based on the characteristic values ​​of full peak width, front peak width, back peak width, and pulse peak value includes:

[0019] The conventional full peak width region is determined based on the particle distribution of the full peak width, the conventional front peak width region is determined based on the particle distribution of the front peak width, and the conventional back peak width region is determined based on the particle distribution of the back peak width.

[0020] Pulse signals whose full peak width feature value falls within the conventional full peak width region, whose front peak width feature value falls within the conventional front peak width region, and whose rear peak width feature value falls within the conventional rear peak width region are selected as conventional pulse signals.

[0021] The average peak value of all regular pulse signals is taken as the regular peak value;

[0022] The pulse signals that satisfy the condition that the peak value is greater than a preset peak value threshold are selected as abnormal peak value pulse signals; wherein, the preset peak value threshold is the product of the normal peak value and a preset fourth threshold.

[0023] In one embodiment, determining the conventional full peak width region based on the particle distribution of the full peak width, determining the conventional front peak width region based on the particle distribution of the front peak width, and determining the conventional back peak width region based on the particle distribution of the back peak width include:

[0024] In the case of particle distribution with full peak width, the full peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional full peak width region, and the full peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional full peak width region.

[0025] In the particle distribution of the front peak width, the front peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional front peak width region, and the front peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional front peak width region.

[0026] In the particle distribution of the back peak width, the back peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional back peak width region, and the back peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional back peak width region.

[0027] In one embodiment, the step of filtering out full-peak-width anomalous pulse signals from the pulse signal set based on full-peak-width eigenvalue limitation includes:

[0028] The pulse signals that satisfy the condition that the full peak width feature value is greater than the preset fifth threshold are selected as full peak width abnormal pulse signals.

[0029] In one embodiment, the formula for calculating the total number of particles in a blood cell sample is:

[0030]

[0031] In the above formula, Num z Indicates the first pulse count;

[0032] Num p P1 indicates the second pulse count, and the peak abnormal pulse signal may be... The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P1 indicates the possible peak abnormal pulse signal. The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture;

[0033] Num w P2 indicates the third pulse number, and P2 indicates that the abnormal pulse signal with full peak width may be... The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P2 indicates that the abnormal pulse signal with full peak width may be... The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture;

[0034] Num m P3 indicates the fourth pulse count, and P3 indicates the remaining abnormal pulse signal may be... The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P3 indicates the remaining abnormal pulse signal may be... The probability of a pulse signal generated by the superposition of individual particles passing through a sapphire aperture.

[0035] A blood cell particle count counting device, the device comprising:

[0036] The particle distribution statistics module is used to acquire the pulse signal set of the collected blood cell sample, identify multiple pulse features of each pulse signal in the pulse signal set, and count the particle distribution of each pulse signal; wherein, the particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature;

[0037] The pulse count module is used to filter the pulse signal set into multiple preset categories of pulse signals based on the feature values ​​of different pulse characteristics, and to count the number of pulses of each preset category of pulse signal according to the particle distribution.

[0038] The particle count calculation module is used to compensate for the number of pulses in all preset categories of pulse signals, based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

[0039] 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 particle count calculation method.

[0040] A blood cell particle count calculation device is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the blood cell particle count calculation method described above.

[0041] This invention provides a method, apparatus, medium, and device for calculating the number of blood cell particles. First, it identifies the pulse characteristics of each pulse signal in a pulse signal set. Then, based on these pulse characteristics and particle distribution, it statistically counts the pulse count of each preset category of pulse signal, thereby enabling targeted and accurate counting of pulse counts for different categories of pulse signals. Furthermore, since different pulse signals have different superposition patterns, it then performs particle count compensation on the pulse count of all preset categories of pulse signals, targeting the abnormal pulse signal characteristics generated by multiple blood cells superimposed through a gemstone aperture, to obtain the total number of particles in the blood cell sample. Therefore, this invention can selectively perform particle count compensation and accurately obtain the total number of particles in a blood cell sample. Attached Figure Description

[0042] 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.

[0043] in:

[0044] Figure 1 This is a flowchart illustrating the method for calculating the number of blood cells.

[0045] Figure 2 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;

[0046] Figure 3 A flowchart illustrating the process of counting the number of pulses for each preset category of pulse signal;

[0047] Figure 4 A flowchart illustrating the process of filtering out abnormal peak pulse signals;

[0048] Figure 5 A schematic diagram of a blood cell particle count calculation device;

[0049] Figure 6 This is a block diagram of a blood cell particle count calculation device. Detailed Implementation

[0050] 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.

[0051] 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.

[0052] 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.

[0053] like Figure 1 As shown, Figure 1This is a flowchart illustrating a method for calculating the number of blood cells in one embodiment. The steps provided by the method for calculating the number of blood cells in this embodiment include:

[0054] S101, acquire the pulse signal set of the collected blood cell sample, identify multiple pulse features of each pulse signal in the pulse signal set, and count the particle distribution of each pulse signal.

[0055] Among them, the particle distribution is used to indicate the number of particles with different characteristic values ​​under a single pulse characteristic;

[0056] 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.

[0057] In this set of pulse signals, it is necessary to identify the pulse characteristics of each pulse signal, such as pulse width, pulse rise time, and pulse fall time.

[0058] In one specific embodiment, the pulse characteristics include "pulse peak value", "preceding peak width", "following peak width", and "full peak width". Multiple pulse characteristics of each pulse signal in the pulse signal set are identified in the following manner:

[0059] Identify the peak value, full peak width, first peak width, and last peak width of each pulse signal in the pulse signal set. The full peak width indicates the time width between the start and end points of the same pulse signal. The first peak width indicates the time width between the peak point and the first feature point of the same pulse signal. The amplitude of the first feature point is half the difference between the amplitude of the peak point and the amplitude of the start point, and the acquisition time of the first feature point is before the acquisition time of the peak point. The last peak width indicates the time width between the peak point and the second feature point of the same pulse signal. The amplitude of the second feature point is half the difference between the amplitude of the peak point and the amplitude of the end point, and the acquisition time of the second feature point is after the acquisition time of the peak point.

[0060] For example, with Figure 2 For example, Figure 2 (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.

[0061] Figure 2 (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”.

[0062] 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".

[0063] Finally, the particle distribution of each pulse signal is statistically analyzed, including:

[0064] (1) Statistical analysis of all pulse signals The particle number distribution under various characteristic values ​​of the pulse signal characteristic "front width", i.e., Pre i (i∈1,2,3,…P), where P represents the total number of eigenvalues ​​of the front width, Pre i This represents the total number of pulse signals whose peak value is equal to the eigenvalue of the i-th leading edge width.

[0065] (2) Statistical analysis of all pulse signals The particle number distribution under various characteristic values ​​of the pulse signal characteristic "back peak width", i.e., Sub i (i∈1,2,3,…S), where represents the total number of eigenvalues ​​of the back front width, Sub i This represents the total number of pulse signals whose peak value is equal to the width of the i-th pulse.

[0066] (3) Statistical analysis of all pulse signals The particle number distribution under various characteristic values ​​of the pulse signal characteristic "full front width", i.e., Ful i (i∈1,2,3,…F), where F represents the total number of eigenvalues ​​of the full front 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.

[0067] S102, based on the characteristic value limitation of different pulse characteristics, the pulse signal set is filtered into multiple preset categories of pulse signals, and the number of pulses of each preset category of pulse signal is counted according to the particle distribution.

[0068] In other words, this step counts the number of pulses based on the type of pulse signal. Normal signals are generated by a single cell passing through the gem hole normally, while abnormal signals are generated by multiple cells passing through the gem hole in a superimposed manner.

[0069] In one specific embodiment, such as Figure 3 As shown, the specific steps for counting the number of pulses for each preset category of pulse signal include: S1021-S1024. The execution order can be either synchronous or sequential; no specific limitation is made here. Specifically:

[0070] S1021, based on the characteristic values ​​of full peak width, front peak width, and back peak width, normal pulse signals are selected from the pulse signal set, and the first pulse number of the normal pulse signal is counted according to the particle distribution.

[0071] Optionally, pulse signals that simultaneously satisfy the following conditions are selected as normal pulse signals: the full peak width feature value is less than a preset first threshold Th1, the front peak width feature value is less than a preset second threshold Th2, and the rear peak width feature value is less than a preset third threshold Th3.

[0072] Therefore, based on the particle distribution, select those that simultaneously satisfy... and and The number of normal pulse signals Num z Th1, Th2, and Th3 are derived from experience.

[0073] S1022, based on the characteristic values ​​of the full peak width, the first peak width, the second peak width and the pulse peak value, the peak abnormal pulse signal is screened from the pulse signal set, and the second pulse number of the peak abnormal pulse signal is counted according to the particle distribution.

[0074] In one specific embodiment, such as Figure 4 As shown, abnormal peak pulse signals are filtered out through the following steps:

[0075] S1022a, the conventional full peak width region is determined based on the particle distribution of the full peak width, the conventional front peak width region is determined based on the particle distribution of the front peak width, and the conventional back peak width region is determined based on the particle distribution of the back peak width.

[0076] Specifically, in the full peak width particle distribution case Ful i In (i∈1,2,3,…F), the full peak width feature value with the largest particle number distribution is taken as the upper limit ful1 of the normal full peak width region, and the full peak width feature value with the second largest particle number distribution is taken as the lower limit ful2 of the normal full peak width region. Therefore, the normal full peak width region is [ful1,ful2].

[0077] Particle distribution in the front peak width (Pre) i In (i∈1,2,3,…P), the eigenvalue of the front peak width with the largest particle number distribution is taken as the upper limit pre1 of the normal front peak width region, and the eigenvalue of the front peak width with the second largest particle number distribution is taken as the lower limit pre2 of the normal front peak width region. Therefore, the normal full peak width region is [pre1,pre2].

[0078] Particle distribution in the later peak width Sub i In (i∈1,2,3,…S), the peak width feature value with the largest particle number distribution is taken as the upper limit sub1 of the normal peak width region, and the peak width feature value with the second largest particle number distribution is taken as the lower limit sub2 of the normal peak width region. Therefore, the normal full peak width region is [sub1,sub2].

[0079] S1022b: Pulse signals whose full peak width feature values ​​fall within the normal full peak width region, whose front peak width feature values ​​fall within the normal front peak width region, and whose rear peak width feature values ​​fall within the normal rear peak width region are selected as normal pulse signals.

[0080] That is, selecting pulse signals that simultaneously meet the following conditions:

[0081]

[0082] S1022c uses the average peak value of all regular pulse signals as the regular peak value.

[0083] The typical peak value can be denoted as M. peak .

[0084] S1022d filters pulse signals whose peak values ​​are greater than a preset peak threshold as abnormal peak pulse signals.

[0085] The preset peak threshold is the product of the normal peak value and the preset fourth threshold. That is, it filters out values ​​that meet the criteria. The pulse signal under the condition is used as the peak abnormal pulse signal. Th4 is the fourth threshold, which is obtained empirically.

[0086] Furthermore, based on the particle distribution, the number of the second pulse Num of the peak abnormal pulse signal is counted. p .

[0087] S1023, based on the characteristic value limitation of the full peak width, filter out the full peak width abnormal pulse signal from the pulse signal set, and count the third pulse number of the full peak width abnormal pulse signal according to the particle distribution.

[0088] Optionally, pulse signals that satisfy a full-peak width characteristic value greater than a preset fifth threshold are filtered out as full-peak width anomalous pulse signals. That is, signals that satisfy the following criteria are filtered out. The pulse signal under the condition is used as a full-peak-width abnormal pulse signal. Th5 is the fifth threshold, obtained empirically.

[0089] Furthermore, based on the particle distribution, the number of the third pulse, Num, of the full-peak width anomaly pulse signal was statistically analyzed. w .

[0090] S1024, the pulse signals in the pulse signal set other than normal pulse signals, peak abnormal pulse signals and full peak width abnormal pulse signals are taken as the remaining abnormal pulse signals, and the fourth pulse number of the remaining abnormal pulse signals is calculated based on the total number of pulses in the pulse signal set, the first pulse number, the second pulse number and the third pulse number.

[0091] Among them, the remaining abnormal pulse signal is a catch-all type of pulse signal. The number of particles in this type of pulse signal is generally divided into several types, and the probability of each case can be obtained through a large number of samples. Therefore, its pulse number is also counted here for subsequent comprehensive calculation of the particle number.

[0092] The fourth pulse number Num m Represented as:

[0093] Num m =A-Num p -Num z -Num w

[0094] S103, among all preset types of pulse signals, the number of pulses is compensated for based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

[0095] In general, the formula for calculating the total number of particles in a blood cell sample is:

[0096]

[0097] In the above formula, Numz Indicates the first pulse number, which is the total number of particles in the normal pulse signal.

[0098] Num p P1 indicates the second pulse count, and the peak abnormal pulse signal may be... The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P1 indicates the possible peak abnormal pulse signal. The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; That is, the total number of particles after the peak abnormal pulse signal is compensated for.

[0099] Num w P2 indicates the third pulse number, and P2 indicates that the abnormal pulse signal with full peak width may be... The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P2 indicates that the abnormal pulse signal with full peak width may be... The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; That is, the total number of particles after particle number compensation for the full-peak width abnormal pulse signal.

[0100] Num m P3 indicates the fourth pulse number, and P3 indicates that the remaining abnormal pulse signal may be n1. m The probability of a pulse signal generated by the superposition of individual particles passing through the sapphire aperture; 1-P3 indicates the remaining abnormal pulse signal may be... The probability of a pulse signal generated by the superposition of individual particles passing through a sapphire aperture. That is, the total number of particles after particle number compensation of the remaining abnormal pulse signal.

[0101] Optionally, the total number of particles in a blood cell sample can be calculated using the following formula in practice:

[0102] Num x =Num z +Num p *2*P1+Num p *3*(1-P1)+Num w *2*P2+

[0103] Num w *3*(1-P2)+Num m *1*P3+Num m *2*(1-P3)

[0104] The aforementioned method for calculating the number of blood cell particles first identifies the pulse characteristics of each pulse signal in the pulse signal set. Then, based on these pulse characteristics and particle distribution, it classifies and counts the pulse count of each preset category of pulse signal, thereby enabling targeted and accurate counting of pulse counts for different categories of pulse signals. Furthermore, since different pulse signals have different superposition patterns, particle count compensation is then performed on the pulse count of all preset categories of pulse signals to address the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gemstone aperture, thus obtaining the total number of particles in the blood cell sample. Therefore, this invention can selectively perform particle count compensation and accurately obtain the total number of particles in the blood cell sample.

[0105] In one embodiment, such as Figure 5 As shown, a blood cell particle count calculation device is proposed, which includes:

[0106] The particle distribution statistics module 501 is used to acquire the pulse signal set of the collected blood cell sample, identify multiple pulse features of each pulse signal in the pulse signal set, and count the particle distribution of each pulse signal; wherein, the particle distribution is used to indicate the number of particles with different feature values ​​under a single pulse feature.

[0107] The pulse count module 502 is used to filter the pulse signal set into multiple preset categories of pulse signals based on the feature value limitation of different pulse characteristics, and count the pulse count of each preset category of pulse signal according to the particle distribution.

[0108] The particle count calculation module 503 is used to compensate for the number of pulses in all preset categories of pulse signals, based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

[0109] Figure 6 An internal structural diagram of a blood cell particle count device in one embodiment is shown. Figure 6 As shown, the blood cell count calculation 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 a blood cell count calculation method. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to perform the blood cell count calculation method. Those skilled in the art will understand that… Figure 6The 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 characteristic values ​​of the blood cell particle count calculation device to which the present application is applied. The specific blood cell particle count calculation device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0110] 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 multiple pulse features of each pulse signal in the set of pulse signals; and statistically analyzing the particle distribution of each pulse signal; filtering the set of pulse signals into multiple preset categories based on the feature values ​​of different pulse features; and statistically analyzing the number of pulses in each preset category of pulse signals according to the particle distribution; and performing particle number compensation on the number of pulses in all preset categories of pulse signals for abnormal pulse signal features generated by multiple blood cells superimposed through a gemstone hole, so as to obtain the total number of particles in the blood cell sample.

[0111] A blood cell particle count calculation 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 multiple pulse features of each pulse signal in the set of pulse signals; and statistically analyzing the particle distribution of each pulse signal; filtering the set of pulse signals into multiple preset categories based on the feature values ​​of different pulse features; and statistically analyzing the number of pulses in each preset category of pulse signals according to the particle distribution; and performing particle count compensation on the pulse count for abnormal pulse signal features generated by multiple blood cells superimposed through a gemstone aperture among all preset categories of pulse signals, so as to obtain the total number of particles in the blood cell sample.

[0112] It should be noted that the above-mentioned method, apparatus, device and computer-readable storage medium for calculating blood cell particle count belong to the same general inventive concept, and the contents of the embodiments of the method, apparatus, device and computer-readable storage medium for calculating blood cell particle count are applicable to each other.

[0113] 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.

[0114] 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.

[0115] 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 calculating the number of blood cell particles, characterized in that, The method includes: A pulse signal set of collected blood cell samples is acquired, multiple pulse features of each pulse signal in the pulse signal set are identified, and the particle distribution of each pulse signal is statistically analyzed. The particle distribution indicates the number of particles with different feature values ​​under a single pulse feature. The pulse features include: pulse peak value, full peak width, first peak width, and second peak width. The full peak width indicates the time width between the start and end points of the same pulse signal. The first peak width indicates the time width between the pulse peak point and a first feature point on the same pulse signal. The amplitude of the first feature point is half the amplitude difference between the pulse peak point and the start point, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point. The second peak width indicates the time width between the pulse peak point and a second feature point on the same pulse signal. The amplitude of the second feature point is half the amplitude difference between the pulse peak point and the end point, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point. Based on the characteristic values ​​of different pulse features, the pulse signal set is filtered into multiple preset categories of pulse signals, and the number of pulses in each preset category is counted according to the particle distribution. The preset categories of pulse signals include: normal pulse signals, peak abnormal pulse signals, full-peak width abnormal pulse signals, and residual abnormal pulse signals. The residual abnormal pulse signals refer to the pulse signals in the pulse signal set other than the normal pulse signals, the peak abnormal pulse signals, and the full-peak width abnormal pulse signals. Among all preset categories of pulse signals, the number of pulses is compensated for by the number of particles based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

2. The method according to claim 1, characterized in that, The step of filtering the pulse signal set into multiple preset categories based on feature values ​​of different pulse characteristics, and counting the number of pulses in each preset category according to the particle distribution, includes: Normal pulse signals are selected from the pulse signal set based on the characteristic values ​​of full peak width, front peak width, and back peak width, and the first pulse number of the normal pulse signals is counted according to the particle distribution. Based on the characteristic values ​​of the full peak width, the first peak width, the last peak width, and the pulse peak value, peak abnormal pulse signals are screened from the pulse signal set, and the second pulse number of the peak abnormal pulse signals is counted according to the particle distribution. Based on the characteristic value limitation of the full peak width, abnormal pulse signals with full peak width are screened from the pulse signal set, and the third pulse number of the abnormal pulse signals with full peak width is counted according to the particle distribution. The pulse signals in the pulse signal set other than the normal pulse signal, the peak abnormal pulse signal, and the full peak width abnormal pulse signal are taken as the remaining abnormal pulse signals, and the fourth pulse number of the remaining abnormal pulse signals is calculated based on the total number of pulses in the pulse signal set, the first pulse number, the second pulse number, and the third pulse number.

3. The method according to claim 2, characterized in that, The process of filtering normal pulse signals from the pulse signal set based on the feature values ​​of full peak width, front peak width, and back peak width includes: The pulse signals that simultaneously satisfy the following conditions are selected as normal pulse signals: the total peak width feature value is less than a preset first threshold, the front peak width feature value is less than a preset second threshold, and the rear peak width feature value is less than a preset third threshold.

4. The method according to claim 2, characterized in that, The process of filtering out peak-abnormal pulse signals from the pulse signal set based on the characteristic values ​​of full peak width, front peak width, back peak width, and pulse peak value includes: The conventional full peak width region is determined based on the particle distribution of the full peak width, the conventional front peak width region is determined based on the particle distribution of the front peak width, and the conventional back peak width region is determined based on the particle distribution of the back peak width. Pulse signals whose full peak width feature value falls within the conventional full peak width region, whose front peak width feature value falls within the conventional front peak width region, and whose rear peak width feature value falls within the conventional rear peak width region are selected as conventional pulse signals. The average peak value of all regular pulse signals is taken as the regular peak value; The pulse signals that satisfy the condition that the peak value is greater than a preset peak value threshold are selected as abnormal peak value pulse signals; wherein, the preset peak value threshold is the product of the normal peak value and a preset fourth threshold.

5. The method according to claim 4, characterized in that, The determination of the conventional full peak width region based on the particle distribution of the full peak width, the determination of the conventional front peak width region based on the particle distribution of the front peak width, and the determination of the conventional back peak width region based on the particle distribution of the back peak width include: In the case of particle distribution with full peak width, the full peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional full peak width region, and the full peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional full peak width region. In the particle distribution of the front peak width, the front peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional front peak width region, and the front peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional front peak width region. In the particle distribution of the back peak width, the back peak width feature value with the largest particle number distribution is taken as the upper limit of the conventional back peak width region, and the back peak width feature value with the second largest particle number distribution is taken as the lower limit of the conventional back peak width region.

6. The method according to claim 2, characterized in that, The step of filtering out full-peak-width anomalous pulse signals from the pulse signal set based on full-peak-width eigenvalue constraints includes: The pulse signals that satisfy the condition that the full peak width feature value is greater than the preset fifth threshold are selected as full peak width abnormal pulse signals.

7. The method according to claim 2, characterized in that, The formula for calculating the total number of particles in a blood cell sample is: In the above formula, Indicates the first pulse count; Indicates the second pulse number, The abnormal peak pulse signal may be The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; The abnormal peak pulse signal may be The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; Indicates the third pulse number, The abnormal pulse signal indicating full peak width may be The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; The abnormal pulse signal indicating full peak width may be The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; Indicates the fourth pulse number, The remaining abnormal pulse signal may be The probability of a pulse signal generated by the superposition of individual particles passing through a gem-like aperture; The remaining abnormal pulse signal may be The probability of a pulse signal generated by the superposition of individual particles passing through a sapphire aperture.

8. A blood cell particle count counting device, characterized in that, The device includes: The particle distribution statistics module is used to acquire the pulse signal set of the collected blood cell sample, identify multiple pulse features of each pulse signal in the pulse signal set, and statistically analyze the particle distribution of each pulse signal. The particle distribution indicates the number of particles with different feature values ​​under a single pulse feature. The pulse features include: pulse peak value, full peak width, first peak width, and second peak width. The full peak width indicates the time width between the start and end points of the same pulse signal. The first peak width indicates the time width between the pulse peak point and the first feature point. The amplitude of the first feature point is half the amplitude difference between the pulse peak point and the start point, and the acquisition time of the first feature point is before the acquisition time of the pulse peak point. The second peak width indicates the time width between the pulse peak point and the second feature point. The amplitude of the second feature point is half the amplitude difference between the pulse peak point and the end point, and the acquisition time of the second feature point is after the acquisition time of the pulse peak point. The pulse count module is used to filter the pulse signal set into multiple preset categories of pulse signals based on the feature values ​​of different pulse characteristics, and to count the number of pulses in each preset category of pulse signals according to the particle distribution. The preset categories of pulse signals include: normal pulse signals, peak abnormal pulse signals, full-peak width abnormal pulse signals, and residual abnormal pulse signals. The residual abnormal pulse signals refer to the pulse signals in the pulse signal set other than the normal pulse signals, the peak abnormal pulse signals, and the full-peak width abnormal pulse signals. The particle count calculation module is used to compensate for the number of pulses in all preset categories of pulse signals, based on the abnormal pulse signal characteristics generated by multiple blood cells superimposed through the gem hole, so as to obtain the total number of particles in the blood cell sample.

9. 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 7.

10. A blood cell particle count counting 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 7.

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