Curve processing method, device, equipment and storage medium for thromboelastogram

Abstract

This application provides a thromboelastogram curve processing method, apparatus, device, and storage medium. The method comprises: obtaining a thromboelastogram curve; preprocessing the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a preprocessed curve; and processing the preprocessed curve using an adaptive filtering method to eliminate abnormal signals in the preprocessed curve to obtain a smoothed thromboelastogram curve. This solution eliminates abnormal information in the thromboelastogram curve while ensuring that useful feature information is not lost.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and in particular relates to a curve processing method, device, equipment and storage medium for thromboelastogram. Background Art

[0002] The thrombelastogram (TEG) is a specialized graph produced by a thrombelastometer that reflects the dynamic changes in blood coagulation. During operation, the reagent cup is placed on a reaction cell that rotates back and forth at a 4°45° angle, with blood contained between the cup wall and a cylindrical body. When the blood specimen is liquid, the cup's rotation cannot move the cylinder, resulting in a straight line on the tracing paper. As the blood begins to coagulate, fibrin adhesion creates resistance between the cup and the cylinder, causing the cup's rotation to move the cylinder simultaneously. As the amount of fibrin increases, the resistance increases, and the cup's movement of the cylinder changes accordingly. The cylinder's movement cuts through magnetic lines of force, generating an electric current that is converted into a digital signal. This signal is then mapped onto the tracing paper via the sensor, forming the unique thrombelastogram. During operation, the thrombelastogram platform must be stable to prevent external vibrations and other factors from causing abnormal cylinder movement and affecting the thrombelastogram. Therefore, it is of certain research significance to deal with abnormal signals that occur during the drawing of thromboelastography.

[0003] There are many types of curve smoothing algorithms available, such as mean filtering and low-pass filtering, which are the most commonly used smoothing algorithms. The mean filtering algorithm designs a data queue of length N. When a data is collected, it replaces the data at the front of the data queue to form a set containing N data. The average of these N data is then taken as the output result value. Such a filtering algorithm can effectively eliminate some noise information, but the fluctuations caused by some random noise in the thromboelastogram are large and cannot be eliminated by this type of method. The low-pass filtering algorithm allows signals from zero to the cutoff frequency to pass through without attenuation, while suppressing or even eliminating signals at other noise frequencies. However, most noise signals in the thromboelastogram are at the same frequency as the useful signal, and the low-pass filtering algorithm cannot effectively eliminate them.

[0004] Most existing smoothing algorithms are designed for use in ideal or specific environments, but they are unable to effectively and accurately eliminate noise signals in thromboelastogram curves caused by external factors such as vibration. Furthermore, thromboelastograms inherently exhibit sudden changes, and traditional smoothing algorithms can cause curve anomalies. Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a curve processing method, device, equipment and storage medium for thromboelastogram.

[0006] To solve the above technical problems, the embodiments of the present application are implemented in the following ways:

[0007] In a first aspect, the present application provides a curve processing method for a thromboelastogram, the method comprising:

[0008] Obtain thromboelastography curves;

[0009] pre-processing the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a pre-processed curve;

[0010] The preprocessed curve is processed according to an adaptive filtering method to eliminate abnormal signals in the preprocessed curve and obtain a smooth thromboelastogram curve.

[0011] In one embodiment, pre-processing the thromboelastogram curve includes:

[0012] Obtain the current value corresponding to the thromboelastogram curve;

[0013] Calculate the average value of the current value and the previous preset number of values;

[0014] All average values constitute the pre-processing curve.

[0015] In one embodiment, the pre-processed curve is processed according to an adaptive filtering method to obtain a smoothed thromboelastogram curve, including:

[0016] Obtaining the current first data value corresponding to the preprocessed curve;

[0017] Obtaining a previous second data value corresponding to the adaptive filtering method;

[0018] Determining a current second data value corresponding to the adaptive filtering method according to the current first data value, the previous second data value, and the current gain value;

[0019] All current second data values constitute a smoothed thromboelastogram curve.

[0020] In one embodiment, the current gain value is determined by:

[0021] determining a current process variance based on the current first data value;

[0022] Determine the current prediction variance based on the current process variance and the current optimal value error;

[0023] The current gain value is determined based on the current prediction variance.

[0024] In one embodiment, determining the current process variance based on the current first data value includes:

[0025] Determine whether the current first data value has obtained the Angle evaluation indicator;

[0026] If the current first data value has obtained the Angle evaluation index, the first process variance is determined to be the current process variance;

[0027] If the current first data value does not obtain the Angle evaluation index, the second process variance is determined to be the current process variance.

[0028] In one embodiment, after each second data value is obtained, the method further includes: updating the optimal value error:

[0029] The updated optimal value error is determined based on the current gain value and the current prediction variance.

[0030] In one embodiment, the update ends when the preset parameter indicator is obtained.

[0031] In a second aspect, the present application provides a curve processing device for a thromboelastogram, the device comprising:

[0032] an acquisition module, for acquiring a thromboelastogram curve;

[0033] A preprocessing module is used to preprocess the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a preprocessed curve;

[0034] The processing module is used to process the pre-processed curve according to the adaptive filtering method to eliminate abnormal signals in the pre-processed curve and obtain a smooth thromboelastogram curve.

[0035] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the curve processing method for thromboelastography according to the first aspect is implemented.

[0036] In a fourth aspect, the present application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the curve processing method for thromboelastography according to the first aspect.

[0037] It can be seen from the technical solutions provided in the above embodiments of this specification that this solution can avoid the normal mutation signal from being affected, and can eliminate various noise signals caused by external factors that appear during the drawing of the thromboelastogram curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A schematic flow chart of the curve processing method for thromboelastography provided in this application;

[0040] Figure 2 A schematic diagram of the structure of a curve processing device for thromboelastography provided in this application;

[0041] Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0042] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0043] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0044] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments described herein without departing from the scope or spirit of the present application. Other embodiments will be apparent to those skilled in the art from the present description. The present description and examples are intended to be illustrative only.

[0045] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0046] Unless otherwise specified, "parts" in this application are calculated by mass.

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0048] Reference Figure 1 , which shows a flow chart of a curve processing method for thromboelastography provided in an embodiment of the present application.

[0049] like Figure 1 As shown, the curve processing method for thromboelastography may include:

[0050] S110. Obtain a thromboelastogram curve.

[0051] Specifically, the thromboelastogram curve is a graph used to reflect the dynamic changes of blood coagulation (including the formation rate of fibrin, the dissolution state, the firmness and elasticity of the coagulation, etc.).

[0052] Since there are a lot of noise signals in the signal acquisition process, some preprocessing is required to eliminate some weak noise signals.

[0053] S120 , pre-processing the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a pre-processed curve.

[0054] Specifically, a mean filtering algorithm may be used to pre-process the thromboelastogram curve, which may include:

[0055] Obtain the current value corresponding to the thromboelastogram curve;

[0056] Calculate the average value of the current value and the previous preset number of values;

[0057] All average values constitute the pre-processing curve.

[0058] For example, a data queue of length 30 is designed. The values corresponding to every 30 thromboelastogram curves are averaged (i.e., the current value corresponding to the thromboelastogram curve is averaged with the previous 29 values). Each average value obtained replaces the corresponding current value in the thromboelastogram curve, and so on. All the average values obtained constitute the preprocessed curve, which eliminates some noise information and optimizes the thromboelastogram curve.

[0059] S130, processing the preprocessed curve according to an adaptive filtering method to eliminate abnormal signals in the preprocessed curve to obtain a smooth thromboelastogram curve, which may include:

[0060] Obtaining the current first data value corresponding to the preprocessed curve;

[0061] Obtaining a previous second data value corresponding to the adaptive filtering method;

[0062] Determining a current second data value corresponding to the adaptive filtering method according to the current first data value, the previous second data value, and the current gain value;

[0063] All current second data values constitute a smoothed thromboelastogram curve.

[0064] Specifically, the current first data value corresponding to the pre-processed curve is recorded as D(i), the previous second data value corresponding to the adaptive filtering method is recorded as x(i-1), and the current gain value is recorded as K i , the current second data value x(i) corresponding to the adaptive filtering method is obtained as:

[0065] x(i)=x(i-1)+K i *(D(i)-x(i-1))

[0066] It can be understood that according to the inherent law of the thromboelastogram, the initial data value of the adaptive filtering method is set to 0, that is, x(1)=0.

[0067] The obtained curve formed by all current second data values is the smoothed thromboelastogram curve.

[0068] The current gain value can be determined in the following way:

[0069] determining a current process variance based on the current first data value;

[0070] Determine the current prediction variance based on the current process variance and the current optimal value error;

[0071] The current gain value is determined based on the current prediction variance.

[0072] Determining the current process variance according to the current first data value may include:

[0073] Determine whether the current first data value has obtained the Angle evaluation indicator;

[0074] If the current first data value has obtained the Angle evaluation index, the first process variance is determined to be the current process variance;

[0075] If the current first data value does not obtain the Angle evaluation index, the second process variance is determined to be the current process variance.

[0076] Specifically, the Angle evaluation index is one of the evaluation indexes in the thromboelastogram. This application uses the Angle evaluation index as a node in curve processing and divides the entire thromboelastogram curve drawing process into before and after calculating the Angle evaluation index value.

[0077] If the current first data value D(i) has obtained the Angle evaluation index, the first process variance P1 is determined to be the current process variance; if the current first data value D(i) has not obtained the Angle evaluation index, the second process variance P2 is determined to be the current process variance.

[0078] The current optimal value error is recorded as P i , then according to the current process variance and the current optimal value error, determine the current prediction variance Ppredict i for:

[0079] Ppredict i =P i +P1 or Ppredict=P i +P2

[0080] Current gain value K i for:

[0081] K i =Ppredict i / (Ppredict i +1)

[0082] Among them, after each current second data value x(i) is obtained, the optimal value error is updated according to the current gain value and the current prediction variance to obtain the updated optimal value error P i+1 for:

[0083] P i+1 =(1-K i )*Ppredict i

[0084] The current second data value corresponding to the adaptive filtering method is repeatedly calculated according to the above steps until the preset parameter index is obtained and the update is completed.

[0085] Specifically, the preset parameter index can be selected according to actual needs. In this embodiment, the update can be ended by selecting and obtaining EPL.

[0086] It is understandable that in the above embodiment, the optimal value error, the first process variance, and the second process variance may be adjusted according to the detection purpose and scenario of the thromboelastogram curve to control the degree of curve smoothness.

[0087] The curve processing method for thromboelastography provided in the embodiment of the present application performs a smoothing operation on the thromboelastography curve, which can effectively smooth the non-smooth curve segments in the image and effectively eliminate the noise signals therein. Moreover, the curve after the smoothing operation does not lose the required characteristic information compared to the original curve.

[0088] Reference Figure 2 , which shows a schematic structural diagram of a curve processing device for thromboelastography described according to an embodiment of the present application.

[0089] like Figure 2 As shown, the curve processing device 200 for thromboelastography may include:

[0090] An acquisition module 210 is used to acquire a thromboelastogram curve;

[0091] A preprocessing module 220 is used to preprocess the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a preprocessed curve;

[0092] The processing module 230 is configured to process the preprocessed curve according to an adaptive filtering method to eliminate abnormal signals in the preprocessed curve and obtain a smooth thromboelastogram curve.

[0093] Optionally, the pre-processing module 220 is further configured to:

[0094] Obtain the current value corresponding to the thromboelastogram curve;

[0095] Calculate the average value of the current value and the previous preset number of values;

[0096] All average values constitute the pre-processing curve.

[0097] Optionally, the processing module 230 is further configured to:

[0098] Obtaining the current first data value corresponding to the preprocessed curve;

[0099] Obtaining a previous second data value corresponding to the adaptive filtering method;

[0100] Determining a current second data value corresponding to the adaptive filtering method according to the current first data value, the previous second data value, and the current gain value;

[0101] All current second data values constitute a smoothed thromboelastogram curve.

[0102] Optionally, the processing module 230 is further configured to:

[0103] determining a current process variance based on the current first data value;

[0104] Determine the current prediction variance based on the current process variance and the current optimal value error;

[0105] The current gain value is determined based on the current prediction variance.

[0106] Optionally, the processing module 230 is further configured to:

[0107] Determine whether the current first data value has obtained the Angle evaluation indicator;

[0108] If the current first data value has obtained the Angle evaluation index, the first process variance is determined to be the current process variance;

[0109] If the current first data value does not obtain the Angle evaluation index, the second process variance is determined to be the current process variance.

[0110] Optionally, a curve processing device for a thromboelastogram is further configured to update an optimal value error after obtaining a current second data value:

[0111] The updated optimal value error is determined based on the current gain value and the current prediction variance.

[0112] Optionally, a curve processing device for a thromboelastogram is further used to: when a preset parameter indicator is obtained, the update ends.

[0113] This embodiment provides a curve processing device for thromboelastography, which can execute the embodiment of the above method. Its implementation principle and technical effects are similar and will not be described in detail here.

[0114] Figure 3 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 3 , which shows a structural diagram of an electronic device 300 suitable for implementing an embodiment of the present application.

[0115] like Figure 3 As shown, electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage portion 308 into a random access memory (RAM) 303. Various programs and data required for the operation of device 300 are also stored in RAM 303. CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0116] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 306 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0117] In particular, according to the embodiments of the present disclosure, the above reference Figure 1 The described process can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for executing the aforementioned curve processing method for thromboelastography. In such embodiments, the computer program can be downloaded and installed from a network via the communication portion 309 and / or installed from removable media 311.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0119] The units or modules involved in the embodiments described in this application may be implemented by software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0120] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or physical devices, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a mobile phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0121] As another aspect, the present application further provides a storage medium, which may be the storage medium included in the aforementioned apparatus in the above-mentioned embodiments, or a separate storage medium not incorporated into the apparatus. The storage medium stores one or more programs, which are used by one or more processors to execute the curve processing method for thromboelastography described in the present application.

[0122] Storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0123] It should be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0124] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

Claims

1. A curve processing method for thromboelastography, characterized in that: The method comprises: Obtain thromboelastography curves; preprocessing the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve to obtain a preprocessed curve; Processing the preprocessed curve according to an adaptive filtering method to eliminate abnormal signals in the preprocessed curve to obtain a smooth thromboelastogram curve, including: Obtaining a current first data value corresponding to the preprocessed curve; Obtaining a previous second data value corresponding to the adaptive filtering method; Determine a current second data value corresponding to the adaptive filtering method according to the current first data value, the previous second data value, and the current gain value, wherein the current gain value is determined in the following manner: determining a current process variance based on the current first data value; determining a current prediction variance based on the current process variance and the current optimal value error; Determining the current gain value according to the current prediction variance; All of the current second data values constitute the smoothed thromboelastogram curve.

2. The method according to claim 1, characterized in that The pre-processing of the thromboelastogram curve comprises: Obtaining a current value corresponding to the thromboelastogram curve; Calculate the average value of the current value and the previous preset number of values; All the average values constitute the pre-processing curve.

3. The method according to claim 1, characterized in that Determining the current process variance according to the current first data value includes: Determine whether the current first data value has obtained the Angle evaluation index; If the current first data value has obtained the Angle evaluation index, determining the first process variance as the current process variance; If the current first data value does not obtain the Angle evaluation indicator, the second process variance is determined to be the current process variance.

4. The method according to claim 1, wherein After each time the current second data value is obtained, the method further includes: updating the optimal value error: An updated optimal value error is determined according to the current gain value and the current prediction variance.

5. The method according to claim 4, characterized in that When the preset parameter indicators are obtained, the update is completed.

6. A curve processing device for thromboelastography, characterized in that: The device comprises: an acquisition module, for acquiring a thromboelastogram curve; a preprocessing module, configured to preprocess the thromboelastogram curve to eliminate some weak noise signals in the thromboelastogram curve and obtain a preprocessed curve; A processing module is used to process the preprocessed curve according to an adaptive filtering method to eliminate abnormal signals in the preprocessed curve and obtain a smoothed thrombelastogram curve, including: obtaining a current first data value corresponding to the preprocessed curve; obtaining a previous second data value corresponding to the adaptive filtering method; determining a current second data value corresponding to the adaptive filtering method based on the current first data value, the previous second data value and a current gain value, wherein the current gain value is determined in the following manner: determining a current process variance based on the current first data value; determining a current prediction variance based on the current process variance and a current optimal value error; and determining the current gain value based on the current prediction variance; all the current second data values constitute the smoothed thrombelastogram curve.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the curve processing method for thromboelastography according to any one of claims 1 to 5 is implemented.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the curve processing method for thromboelastography according to any one of claims 1 to 5 is implemented.

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