A method, device and storage medium for evaluating atherosclerotic stenosis

Abstract

The embodiments of this specification provide a method, device and storage medium for assessing atherosclerotic stenosis, the method comprising: collecting cerebral blood flow velocity data and electrocardiogram signals during the process of body position change in a supine-standing test; determining the slope of the W wave rising branch and the XW angle according to the cerebral blood flow velocity data; determining the variability of the electrocardiogram signal and the peak rise amplitude of the electrocardiogram signal according to the electrocardiogram signal; determining the peak time of the W wave according to the cerebral blood flow velocity data and the electrocardiogram signal; based on a preset machine learning model, taking the variability of the electrocardiogram signal, the peak rise amplitude of the electrocardiogram signal, the peak time of the W wave, the slope of the W wave rising branch and the XW angle as input, the degree of atherosclerotic stenosis is obtained. The technical solution provided in this application is used to solve the problem that the prior art has errors in determining the degree of intracranial atherosclerotic stenosis.

Description

Technical Field

[0001] This document relates to the technical field of intracranial atherosclerosis, and in particular to an atherosclerotic stenosis assessment method, device and storage medium. Background Art

[0002] With the extension of human life span and changes in lifestyle, the incidence of cerebrovascular diseases has gradually increased, among which intracranial atherosclerotic stenosis is one of the main causes of limited cerebral blood flow.

[0003] Clinicians usually evaluate the cerebral blood flow autoregulation function based on the changes in blood pressure and the morphology of W waves, and then determine the degree of intracranial atherosclerosis stenosis. Or they can further evaluate the cerebral blood flow autoregulation function by assisting the diagnosis of orthostatic hypotension, autonomic nervous system diseases, cerebral hypoperfusion syndrome, syncope, etc., and then determine the degree of intracranial atherosclerosis stenosis.

[0004] However, subjectively judging the degree of stenosis based on the morphology of blood vessels in the image lacks some quantitative data support; assessing blood flow conditions based on blood flow velocity and spectral morphology lacks the evaluation of the autonomous regulation function of cerebral blood flow. The above defects will lead to errors in determining the degree of stenosis of intracranial atherosclerosis and may even affect the patient's treatment progress. Summary of the invention

[0005] In view of the above analysis, the present application aims to propose an atherosclerotic stenosis assessment method, device and storage medium to improve the accuracy of evaluating the degree of intracranial atherosclerotic stenosis.

[0006] In a first aspect, one or more embodiments of this specification provide a method for assessing atherosclerotic stenosis, comprising:

[0007] In the supine-standing test, the cerebral blood flow velocity data and ECG signals were collected during the body position change process;

[0008] Determine the slope of the ascending branch of the W wave and the XW angle according to the cerebral blood flow velocity data;

[0009] Determining the variability of the ECG signal and the peak rise amplitude of the ECG signal according to the ECG signal;

[0010] Determining the W wave peak time according to the cerebral blood flow velocity data and the electrocardiogram signal;

[0011] Based on a preset machine learning model, the degree of atherosclerosis stenosis is obtained by taking the ECG signal variability, the ECG signal peak rise amplitude, the W wave peak time, the W wave rising branch slope and the XW angle as input.

[0012] Further, determining the variability of the electrocardiogram signal according to the electrocardiogram signal includes:

[0013] Performing R wave detection on the electrocardiogram signal to obtain an R wave interval;

[0014] Taking the moment when the patient stands up as the starting moment, determine the first time period and the second time period respectively;

[0015] determining a maximum value of an R wave interval in the first time period;

[0016] determining a minimum value of the R wave interval in the second period;

[0017] The electrocardiogram signal variability is obtained according to the maximum value of the R wave interval and the minimum value of the R wave interval.

[0018] Further, determining the peak rise amplitude of the electrocardiogram signal according to the electrocardiogram signal includes:

[0019] Determining a heart rate variation baseline and a heart rate variation curve according to the electrocardiogram signal;

[0020] The maximum difference between the heart rate variation baseline and the heart rate variation curve is determined as the peak rise amplitude of the electrocardiogram signal.

[0021] Further, determining the W wave peak time according to the cerebral blood flow velocity data and the electrocardiogram signal includes:

[0022] determining the position of the W wave according to the cerebral blood flow velocity data;

[0023] Determine the time corresponding to the peak of the W wave according to the position of the W wave;

[0024] Determine the time when the patient stands up according to the electrocardiogram signal as the starting time;

[0025] The peak time of the W wave is determined according to the time corresponding to the W wave peak and the start time.

[0026] Further, determining the slope of the rising branch of the W wave according to the cerebral blood flow velocity data includes:

[0027] Determine a W wave peak extreme value and a target wave trough extreme value according to the cerebral blood flow velocity data, wherein the target wave trough extreme value is an extreme value of a wave trough adjacent to the W wave peak and appearing earlier than the W wave peak;

[0028] The slope of the rising branch of the W wave is determined according to the W wave peak extreme value and the target trough extreme value.

[0029] Further, determining the XW angle according to the cerebral blood flow velocity data includes:

[0030] Determine, according to the cerebral blood flow velocity data, the descending branch of the X wave, the ascending branch of the W wave and the target trough extreme value, wherein the target trough extreme value is the extreme value of the trough adjacent to the W wave peak and appearing earlier than the W wave peak;

[0031] The XW angle is determined according to the X wave descending branch, the target trough extreme value and the W wave ascending branch.

[0032] In a second aspect, one or more embodiments of this specification provide an atherosclerotic stenosis assessment device, including: a collection module, a data processing module, and an evaluation module;

[0033] The acquisition module is used to collect cerebral blood flow velocity data and electrocardiogram signals during the process of body position change in the supine and standing test;

[0034] The data processing module is used to determine the slope of the W wave rising branch and the XW angle according to the cerebral blood flow velocity data; determine the variability of the ECG signal and the ECG signal peak rising amplitude according to the ECG signal; determine the W wave peak time according to the cerebral blood flow velocity data and the ECG signal;

[0035] The evaluation module is used to obtain the degree of atherosclerosis stenosis based on a preset machine learning model, with the ECG signal variability, the ECG signal peak rise amplitude, the W wave peak time, the W wave rising branch slope and the XW angle as input.

[0036] Furthermore, the data processing module is used to perform R-wave detection on the ECG signal to obtain the R-wave interval; determine the first time period and the second time period respectively with the patient standing up as the starting time; determine the maximum value of the R-wave interval in the first time period; determine the minimum value of the R-wave interval in the second time period; and obtain the ECG signal variability based on the maximum value of the R-wave interval and the minimum value of the R-wave interval.

[0037] Furthermore, the data processing module is used to determine a heart rate variation baseline and a heart rate variation curve according to the electrocardiogram signal; and determine a maximum difference between the heart rate variation baseline and the heart rate variation curve as the peak rise amplitude of the electrocardiogram signal.

[0038] In a third aspect, one or more embodiments of this specification provide a storage medium, including:

[0039] Used to store computer executable instructions, which implement the method described in any one of the first aspects when executed.

[0040] Compared with the prior art, this application can at least achieve the following technical effects:

[0041] The present application experimentally screens out ECG signal variability, ECG signal peak rise amplitude, W wave peak time, W wave rise branch slope and XW angle as parameters for evaluating atherosclerotic stenosis, which can increase the accuracy of whether stenosis occurs to more than 90%, greatly improving the accuracy of evaluating the degree of intracranial atherosclerotic stenosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0043] Figure 1 A flowchart of a method for assessing atherosclerotic stenosis provided for one or more embodiments of this specification;

[0044] Figure 2 A schematic diagram of the slope of the W wave rising branch and the XW angle provided in one or more embodiments of this specification;

[0045] Figure 3 A schematic diagram of the peak rise amplitude of an ECG signal and the W-wave peak time provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0047] Clinically, the examination methods for intracranial atherosclerosis mainly include the following:

[0048] Transcranial Doppler (TCD): Ultrasound is a non-invasive method that uses ultrasound waves to view blood flow in the intracranial and carotid arteries. It can detect the thickness of the artery walls, plaque buildup, and the degree of arterial narrowing. Intracranial ultrasound is usually performed through a window in the skull, providing a direct view of the intracranial arteries.

[0049] Magnetic resonance angiography (MRA): MRA uses magnetic resonance technology to produce images of blood vessels and can be used to examine the structure and blood flow of the intracranial and carotid arteries. MRA has good resolution for soft tissue and can show abnormal changes in the blood vessel wall, such as plaques and stenosis.

[0050] Computed tomography angiography (CTA): CTA combines the techniques of computed tomography and angiography to provide high-resolution images of blood vessels. It is often used to evaluate the anatomy, plaque, and degree of stenosis of the arteries.

[0051] DSA: DSA is an invasive examination method that injects contrast agent into the blood vessels and observes the morphology and blood flow of the arteries through X-ray imaging. Although it is somewhat invasive, it may still be used in some cases.

[0052] The above method has the following main problems:

[0053] 1. Although imaging and angiography technologies are used, doctors can only obtain images based on existing technologies and combine their own experience to judge whether stenosis occurs. This is highly subjective and requires high experience and expertise from the doctor.

[0054] 2. In this field, the blood flow in the cardiovascular and cerebrovascular vessels can be better observed through the supine and standing examination, so it is often used to determine symptoms such as orthostatic hypotension and syncope. However, the parameters used for symptoms such as orthostatic hypotension and syncope are not applicable to atherosclerosis. Therefore, in actual scenarios, the supine and standing examination cannot be directly used for the assessment of atherosclerotic stenosis.

[0055] In view of the above problems, the present application screens out parameters that match the supine and standing examinations, and then uses the screened parameters to evaluate intracranial atherosclerosis. The specific screening process is as follows:

[0056] Data from 281 subjects were collected, including 142 cases without vascular stenosis and 132 cases with vascular stenosis. Each subject underwent a supine test, and the trend of cerebral blood flow velocity data was monitored by TCD, and the ECG signal was obtained by single-lead ECG. The trend of heart rate change was calculated based on the ECG signal. During the examination, the subject experienced two changes in body position: (1) standing up quickly from a supine position and (2) standing for 2-3 minutes before returning to a supine position.

[0057] The focus was on studying the changes in heart rate and cerebral blood flow during the first change of body position. From the multiple characteristics describing the changes in heart rate and cerebral blood flow, the characteristics with statistical differences (P<0.05) between the non-stenosis group and the stenosis group were determined by t-test (normal distribution) or Mann Whitney U test (non-normal distribution): HRV, heart rate peak rise amplitude, W wave peak time, W wave rise slope, and XW angle.

[0058] Based on the above parameters, the present application provides a method for assessing atherosclerotic stenosis, such as Figure 1 As shown, the following steps are included:

[0059] Step 1: In the supine and standing test, collect cerebral blood flow velocity data and electrocardiogram signals during the body position change process.

[0060] In the embodiment of the present application, the lying position test process includes the following steps:

[0061] S1. Medical staff instruct patients to wear a single-lead ECG and lie flat on the test bed to observe whether the ECG signal in the software is stable.

[0062] S2. The medical staff puts a monitoring head frame on the patient and fixes a 2Mhz monitoring probe on the probe frame, one on each side, to monitor the blood flow signals of the bilateral middle cerebral arteries (50-60mm) at the same time. After detecting a stable blood flow signal, the monitoring probe is locked and fixed in position to prevent the probe from moving due to changes in body position and affecting the image quality.

[0063] S3. After wearing the monitoring device, let the patient lie in the supine position for 2-3 minutes to establish a blood flow velocity baseline.

[0064] S4. The medical staff issues instructions for the patient to stand up quickly (within 8 seconds) and remain in the standing position for 2 minutes.

[0065] S5. The medical staff issues instructions for the patient to return to the supine position and maintain the supine position for 2 minutes.

[0066] S6. Real-time observation and display of blood flow envelope, ECG signal and changing trend curves of various parameters in the software.

[0067] Collecting the cerebral blood flow velocity data and electrocardiogram signals during the body position change process refers to collecting the data generated in step S5 or step S6.

[0068] Step 2: Determine the slope of the W wave ascending branch and the XW angle based on the cerebral blood flow velocity data.

[0069] In the embodiments of the present application, Figure 2 As shown in the figure, the OW segment is the ascending branch of the W wave. Since the ascending branch of the W wave is a curve, the slope of the ascending branch of the W wave is the average slope of the OW segment.

[0070] Specifically, according to the cerebral blood flow velocity data, the W wave peak extreme value and the target trough extreme value are determined, wherein the target trough extreme value is the extreme value of the trough adjacent to the W wave peak and appearing earlier than the W wave peak. According to the W wave peak extreme value and the target trough extreme value, the W wave rising branch slope is determined.

[0071] For example, Figure 2 In the method, according to the cerebral blood flow velocity curve, the W wave peak extreme value and the value of point O (target trough extreme value) are determined, and according to the W wave peak extreme value and the value of point O, the slope of the W wave rising branch is determined.

[0072] In the embodiments of the present application, Figure 2 As shown in the figure, the XW angle is the angle between the descending branch of the X wave and the ascending branch of the W wave (∠XOW).

[0073] Specifically, according to the cerebral blood flow velocity data, the descending branch of the X wave, the ascending branch of the W wave and the target trough extreme value are determined. The target trough extreme value is the extreme value of the trough adjacent to the W wave peak and appearing earlier than the W wave peak. According to the descending branch of the X wave, the target trough extreme value and the ascending branch of the W wave, the XW angle is determined.

[0074] For example, in Figure 2 In the process, the values ​​of the descending branch of the X wave, the ascending branch of the W wave and the O point (target trough extreme value) are determined according to the cerebral blood flow velocity curve. Then, the degree of the angle XOW is determined according to the values ​​of the descending branch of the X wave, the ascending branch of the W wave and the O point.

[0075] Step 3: According to the ECG signal, determine the ECG signal variability and the ECG signal peak rise amplitude.

[0076] In the embodiment of the present application, the calculation process of the ECG signal variability (HRV, Heart Rate Variability) is:

[0077] Performing R wave detection on the electrocardiogram signal to obtain an R wave interval;

[0078] Taking the moment when the patient stands up as the starting moment, determine the first time period and the second time period respectively;

[0079] determining a maximum value of an R wave interval in the first time period;

[0080] determining a minimum value of the R wave interval in the second period;

[0081] The electrocardiogram signal variability is obtained according to the maximum value of the R wave interval and the minimum value of the R wave interval.

[0082] Specifically, the R wave is detected on the preprocessed ECG raw signal: the R wave is detected using the Pan-Tompkins algorithm. The RR interval is calculated: the interval between two adjacent R waves. The calculation formula for HRV is:

[0083]

[0084] Among them, i corresponds to the heartbeat number of the first period, and j corresponds to the heartbeat number of the second period. That is, the first period is the longest RR interval between the 20th heartbeat and the 40th heartbeat after standing up, and the second period is the shortest RR interval between the 5th heartbeat and the 25th heartbeat after standing up.

[0085] In the embodiment of the present application, the peak rise of the ECG signal is the maximum rise of the heart rate compared to the baseline after standing up. The determination method is:

[0086] According to the ECG signal, determine the heart rate change baseline and heart rate change curve;

[0087] The maximum difference between the heart rate variation baseline and the heart rate variation curve is determined as the peak rise amplitude of the ECG signal.

[0088] For example, Figure 3 In the figure, the left side of point A is the heart rate change curve, the right side of point A is the heart rate change curve, and point B is the maximum increase in heart rate compared to the baseline after standing. The difference between the vertical coordinates of B and A is the peak increase of the ECG signal.

[0089] Step 4: Determine the W wave peak time based on the cerebral blood flow velocity data and the ECG signal.

[0090] In the embodiment of the present application, the peak time of the W wave is the time value corresponding to the peak of the W wave in the cerebral blood flow velocity data minus the time value corresponding to the starting point of the heart rate peak in the electrocardiogram. The determination method is: determine the position of the W wave according to the cerebral blood flow velocity data; determine the time corresponding to the peak of the W wave according to the position of the W wave; determine the time when the patient stands up according to the electrocardiogram signal as the starting time; determine the peak time of the W wave according to the time corresponding to the peak of the W wave and the starting time.

[0091] For example, Figure 2 In the figure, the horizontal axis corresponding to the peak of the W wave represents the time. Figure 3 In the figure, the time corresponding to point A is regarded as the starting time, and the difference between the peak of wave W and point A is the peak time of wave W.

[0092] Step 5: Based on the preset machine learning model, the degree of atherosclerosis stenosis is obtained by taking the ECG signal variability, the ECG signal peak rise amplitude, the peak time of the W wave, the slope of the W wave rising branch and the XW angle as input.

[0093] In the embodiment of the present application, the machine learning model includes but is not limited to SVM, random forest, neural network, etc. In actual operation, the age and gender of the patient are also input.

[0094] In order to illustrate the technical effect of the present application, the present application tests the obtained model (random forest model), and the test results are shown in Table 1:

[0095] Table 1 Model test results

[0096] Accuracy Recall F1-score No stenosis 0.94 0.89 0.92 narrow 0.86 0.92 0.89

[0097] Among them, the accuracy is defined as P = TP / (TP+FP)

[0098] Recall definition R = TP / (TP+FN)

[0099] F1-score definition F1 = 2*P*R / (total + TP-TN)

[0100] TP: The predicted result is the label, which is actually the number of labels

[0101] FP: The predicted result is the label, not the actual number of labels

[0102] FN: The predicted result is not the label, but the number of labels.

[0103] TN: The predicted result is not the label, but the actual number is not the label

[0104] It can be seen from the above results that the present application can increase the accuracy of no stenosis to more than 90% and ensure a high stenosis accuracy.

[0105] The embodiment of the present application provides an atherosclerotic stenosis assessment device, characterized in that it includes: a collection module, a data processing module and an evaluation module;

[0106] The acquisition module is used to collect cerebral blood flow velocity data and electrocardiogram signals during the process of body position change in the supine and standing test;

[0107] The data processing module is used to determine the slope of the W wave rising branch and the XW angle according to the cerebral blood flow velocity data; determine the variability of the ECG signal and the ECG signal peak rising amplitude according to the ECG signal; determine the W wave peak time according to the cerebral blood flow velocity data and the ECG signal;

[0108] The evaluation module is used to obtain the degree of atherosclerosis stenosis based on a preset machine learning model, with the ECG signal variability, the ECG signal peak rise amplitude, the W wave peak time, the W wave rising branch slope and the XW angle as input.

[0109] In an embodiment of the present application, the data processing module is used to perform R-wave detection on the ECG signal to obtain the R-wave interval; taking the moment when the patient stands up as the starting moment, determine the first time period and the second time period respectively; determine the maximum value of the R-wave interval in the first time period; determine the minimum value of the R-wave interval in the second time period; and obtain the variability of the ECG signal based on the maximum value of the R-wave interval and the minimum value of the R-wave interval.

[0110] In an embodiment of the present application, the data processing module is used to determine the heart rate change baseline and the heart rate change curve based on the electrocardiogram signal; and determine the maximum difference between the heart rate change baseline and the heart rate change curve as the peak rise amplitude of the electrocardiogram signal.

[0111] The present application provides a storage medium, including:

[0112] Used to store computer executable instructions, which implement the method described in any of the above embodiments when executed.

[0113] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0114] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital method on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0115] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0116] The methods, devices, modules or units described in the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, 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.

[0117] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0118] It will be appreciated by those skilled in the art that one or more embodiments of this specification may be provided as methods, methods or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (methods), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0123] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0124] Computer readable 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 disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0126] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0128] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. An atherosclerotic stenosis assessment device, characterized in that: include: Acquisition module, data processing module and evaluation module; The acquisition module is used to collect cerebral blood flow velocity data and electrocardiogram signals during the process of body position change in the supine and standing test; The data processing module is used to determine the slope of the rising branch of the W wave and the XW angle according to the cerebral blood flow velocity data; determine the variability of the ECG signal and the peak rise amplitude of the ECG signal according to the ECG signal; determine the peak time of the W wave according to the cerebral blood flow velocity data and the ECG signal; wherein the peak time of the W wave is the time value corresponding to the peak of the W wave in the cerebral blood flow velocity data minus the time value corresponding to the starting point of the heart rate peak in the ECG curve; The data processing module is used to perform R wave detection on the ECG signal to obtain the R wave interval; determine the first time period and the second time period respectively with the patient standing up as the starting time; determine the maximum value of the R wave interval in the first time period; determine the minimum value of the R wave interval in the second time period; and obtain the variability of the ECG signal according to the maximum value of the R wave interval and the minimum value of the R wave interval; The evaluation module is used to obtain the degree of atherosclerosis stenosis based on a preset machine learning model, with the ECG signal variability, the ECG signal peak rise amplitude, the W wave peak time, the W wave rising branch slope and the XW angle as input.

2. The device according to claim 1, characterized in that The data processing module is used to determine the W wave peak extreme value and the target trough extreme value according to the cerebral blood flow velocity data, wherein the target trough extreme value is the extreme value of the trough adjacent to the W wave peak and appearing earlier than the W wave peak; and determine the slope of the W wave rising branch according to the W wave peak extreme value and the target trough extreme value.

3. The device according to claim 2, characterized in that The data processing module is used to determine the X-wave descending branch, the W-wave ascending branch and the target trough extreme value according to the cerebral blood flow velocity data, wherein the target trough extreme value is the extreme value of the trough adjacent to the W-wave peak and appearing earlier than the W-wave peak; and determine the XW angle according to the X-wave descending branch, the target trough extreme value and the W-wave ascending branch.

4. The device according to claim 1, characterized in that The data processing module is used to determine a heart rate variation baseline and a heart rate variation curve according to the electrocardiogram signal; and determine a maximum difference between the heart rate variation baseline and the heart rate variation curve as the peak rise amplitude of the electrocardiogram signal.

5. The device according to claim 1, characterized in that The data processing module is used to determine the position of the W wave according to the cerebral blood flow velocity data; determine the time corresponding to the peak of the W wave according to the position of the W wave; determine the time when the patient stands up according to the electrocardiogram signal as the starting time; and determine the peak time of the W wave according to the time corresponding to the peak of the W wave and the starting time.

6. A storage medium, characterized in that: include: For storing computer executable instructions, the computer executable instructions are executed in the following method: In the supine-standing test, the cerebral blood flow velocity data and ECG signals were collected during the body position change process; Determine the slope of the ascending branch of the W wave and the XW angle according to the cerebral blood flow velocity data; Determining the variability of the ECG signal and the peak rise amplitude of the ECG signal according to the ECG signal; Determining the W wave peak time according to the cerebral blood flow velocity data and the electrocardiogram signal; The peak time of the W wave is the time value corresponding to the peak of the W wave in the cerebral blood flow velocity data minus the time value corresponding to the starting point of the heart rate peak in the electrocardiogram; Determining the variability of the electrocardiogram signal according to the electrocardiogram signal includes: Performing R wave detection on the electrocardiogram signal to obtain an R wave interval; Taking the moment when the patient stands up as the starting moment, determine the first time period and the second time period respectively; determining a maximum value of an R wave interval in the first time period; determining a minimum value of the R wave interval in the second period; Obtaining the electrocardiogram signal variability according to the maximum value of the R wave interval and the minimum value of the R wave interval; Based on a preset machine learning model, the degree of atherosclerosis stenosis is obtained by taking the ECG signal variability, the ECG signal peak rise amplitude, the W wave peak time, the W wave rising branch slope and the XW angle as input.

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