CN113974580A - Carotid artery stenosis degree analysis method based on NIRS technology - Google Patents
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Abstract
The invention provides a carotid artery stenosis degree analysis method and system based on NIRS technology, comprising the following steps: adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region; after preprocessing, obtaining a regional blood vessel elasticity difference curve, a regional blood flow difference curve and corresponding position numbers; evaluating the high-risk stenosis probability and the CS corresponding region of a human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model; the method provided by the invention can effectively screen high-risk CS (human serum) crowds and evaluate narrow approximate areas, is simple in detection instrument, convenient in detection means and low in detection cost, and can be popularized to a large number of crowds.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a carotid artery stenosis degree analysis method and device based on an NIRS technology.
Background
The existing stroke patients and the high-risk groups of stroke exist in China, the number of the existing stroke patients is more than 1500 million, the high-risk groups of stroke is larger, reports are reported to predict that the number of the stroke patients in future 20 years in China can be increased by more than four times, carotid artery stenosis (CS) is a main reason of Stroke (ST), about 10-20% of stroke is caused by carotid artery stenosis, reasonable interventional therapy is carried out on the carotid artery stenosis, and the stroke can be effectively prevented.
Atherosclerosis (AS) is the most main cause of carotid stenosis of old people, AS can cause lipid tissues to be accumulated on the wall of a carotid blood vessel, macrophages in the blood vessel can phagocytose the lipid tissues to further form a lipid pool, meanwhile, a fiber cap is formed on the surface of the lipid pool, a lipid core and the fiber cap are simultaneously attached to the inner wall of the carotid artery, and main components of atherosclerotic plaques on the arterial wall are formed.
CS caused by AS is commonly generated at the tail end of carotid artery, the initial section of internal carotid artery, the siphonic section of internal carotid artery and the tail section of internal carotid artery are divided into the anterior cerebral artery and the middle cerebral artery, and the high-grade CS obviously reduces the blood flow supply of brain, so that transient cerebral ischemia (TIA) is frequently generated, and ischemic stroke is very easily induced, especially for patients who already suffer from stroke.
At present, the Magnetic Resonance Angiography (MRA) is the most special examination means in magnetic resonance examination, and can effectively examine the blood flow distribution condition of brain and neck vessels by imaging blood flow, the MRA technology mainly comprises two technologies, a time-of-flight (TOF) technology and a Phase Contrast (PC) technology, a two-dimensional TOF technology is sensitive to blood flow with slow or medium flow rate, is suitable for large-volume screening imaging and is mainly used for evaluating stenotic intracerebral artery stenosis, a three-dimensional TOF technology is sensitive to rapid blood flow, has high spatial resolution, can effectively distinguish pathological changes with signal loss such as aneurysm, angiostenosis and the like, can effectively evaluate blood vessels with fast flow rate such as carotid artery and intracerebral artery, and a PC technology is sensitive to extremely slow blood flow, can measure blood flow rate and mark blood flow direction, and can effectively distinguish blood vessel occlusion from extremely slow blood flow; the Angiography (CTA) based on the computed tomography is also a commonly used CS examination method with higher accuracy, and the stenosis condition of the carotid artery blood vessel can be clearly displayed by combining the CT enhancement technology and the thin layer rapid scanning technology mainly by utilizing the characteristic that X-rays cannot penetrate through a contrast medium.
However, the time consumption of single MRA examination is high, the time consumption of single MPA examination by a single person is more than 30 minutes, if contrast agent needs to be dripped, the time is longer, the examination cost is high, and the method cannot be applied to a large number of people; CTA methods are also time consuming and carry nuclear radiation; the single CTA inspection by a single person needs more than 30 minutes, the image is processed by a computer in the later period, and the total waiting time is about one hour; moreover, CTA examinations carry a certain amount of radiation, making it difficult to frequently assess the progress of CS development and the recovery of a patient after appropriate treatment; and the detection cost is very expensive, and the method cannot be applied to a large number of people.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a carotid artery stenosis degree analysis method and device based on an NIRS technology, an electronic device and a storage medium, can effectively screen high-risk CS (human coronary artery disease) populations and evaluate a narrow approximate region, is simple and easy in detection instrument, convenient in detection means and low in detection cost, and can be popularized to a large number of populations.
The specific technical scheme is as follows: a carotid artery stenosis degree analysis method based on NIRS technology comprises the following steps:
adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signal, dHbO2, and dHb;
calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
Specifically, the calculating, region by region, a time-frequency correlation between the TOI signal and the nTHI signal specifically includes: and (3) collecting TOI signals and nTHI signals region by using an NIRS technology according to the blood flow direction of bilateral carotid arteries, and calculating the time-frequency correlation of the nTHI signals and the TOI signals of each region.
In particular, the vascular elasticity index, in particular expressed as: the vascular elasticity index is an intuitive expression mode of the strength and weakness of vascular elasticity, wherein the calculation mode of the vascular elasticity index VEI is as follows:
specifically, calculating the time domain correlation of the nth signal, the dHb signal and the dHbO2 signal region by region specifically includes: firstly, a portable NIRS signal collector is used and respectively applied to the position of bilateral carotid arteries, brain hemodynamic parameters such as TOI, nTHI signals, dHb02 and dHb signals are collected region by region, the sampling frequency is 20Hz, and then the time domain correlation of the nTHI signals, the dHb signals and the dHb02 signals is calculated region by region.
In particular, the index of change in blood flow, in particular expressed as: collecting TOI signals, nTHI signals, dHbO2 and dHb signals region by region according to the bilateral carotid artery blood flow direction, calculating the time-frequency correlation of the nTHI signals and the TOI signals in each region and the time-frequency correlation of the nTHI signals and the dHbO2 signals, and obtaining a vascular elasticity index and a blood flow change index based on pulse waves, wherein the calculation equation of the blood flow change index BFCI is as follows:
specifically, after preprocessing the regional blood vessel elasticity index and the regional blood flow change index, a regional blood vessel elasticity difference curve, a regional blood flow difference curve and a corresponding position number are obtained, which specifically includes: the bilateral carotid artery is divided into 4 areas from bottom to top, and the left carotid artery area is marked as JL1、JL2、JL3And JL4The right carotid region is labeled JR1、JR2、JR3And JR4. By measuring the vascular elasticity index VEI and the blood flow change index BFCI at each position, a regional vascular elasticity difference curve and a regional blood flow difference curve can be obtained.
Specifically, the method for evaluating the high-risk stenosis probability and the CS corresponding region of a human by using a trained LSTM deep learning model according to an obtained regional blood vessel elasticity difference curve and regional blood flow difference curve specifically comprises the following steps: excavating the change characteristics of a blood vessel elasticity curve and a blood flow difference curve based on time by using an LSTM network, wherein the area with small change corresponds to a CS area, the output of the LSTM network is recorded as FO, the dimensionality is 1 multiplied by 16, and eight positions of bilateral carotid arteries correspond to several positions1，JL2，JL3，JL4，JR1，JR2，JR3，JR4The value of the CS probability index is 0-1, 0 represents that the CS high-risk probability is lower, and 1 represents that the CS high-risk probability is higher.
In a second aspect, the present invention provides a carotid artery stenosis degree analysis device based on NIRS technology, comprising:
a collecting unit: adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signals, dHbO2 and dHb, the sampling frequency was 20 Hz.
A model establishing unit: calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
an analysis processing unit: preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
an evaluation unit: and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the processor is configured to implement the steps of the method for analyzing a stenosis degree of a carotid artery based on NIRS technology as described in any one of the above when executing a computer management program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having a computer management-like program stored thereon, characterized in that: the computer management program, when executed by a processor, implements the steps of the method for NIRS technology-based analysis of carotid stenosis intensity as described in any of the above.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
the invention utilizes the NIRS technology to detect TOI signals, nTHI signals, dHbO2 signals and dHb signals of bilateral carotid artery region by region, and establishes a bilateral carotid artery region blood flow trend model and a high-risk stenosis model by utilizing a deep learning method based on the characteristics that the blood flow at the CS position is reduced compared with the normal position and the elasticity of blood vessels at the CS position is poor, thereby effectively screening high-risk CS crowds and evaluating the approximate region of stenosis. Comparing MRA and CTA processes: 1) can be applied to screening of large-scale CS crowds. The invention adopts a non-invasive data acquisition mode, only two photoelectric probes are required to be attached to the carotid artery sides at two sides, the data sampling frequency of the instrument used by the invention is 20Hz, a plurality of pulse waves are sampled, and half of the single examination time is about 20 minutes. In addition, the price of the equipment used by the invention is much cheaper than that of the equipment used by the MRA method and the CTA method, and dozens of equipment can be arranged at a single medical point; 2) the cost of single examination is low. The method used by the invention is tens times lower than word check price of MRA method and CTA method, and can effectively reduce economic burden of patients.
Drawings
FIG. 1 is a schematic flow chart of a carotid artery stenosis degree analysis method based on NIRS technology according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an embodiment of a carotid artery stenosis degree analysis device based on NIRS technology according to an embodiment of the invention;
FIG. 3 is an exemplary illustration of experimental verification provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an embodiment of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a carotid artery stenosis degree analysis method and device based on an NIRS technology, electronic equipment and a storage medium, which can effectively screen high-risk CS (human coronary artery disease) crowds and evaluate a narrow approximate region, have simple detection instruments, convenient detection means and low detection cost, and can be popularized to a large number of crowds.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a schematic flow chart of a carotid artery stenosis degree analysis method based on NIRS technology provided by an embodiment of the present invention specifically includes the following steps:
a carotid artery stenosis degree analysis method based on NIRS technology comprises the following steps:
s101: adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signal, dHb02, and dHb;
in the embodiment, a non-invasive data acquisition mode is adopted, and only two photoelectric probes are required to be attached to the carotid artery sides at two sides; the data sampling frequency of the instrument used by the invention is 20Hz, and the instrument can be used by sampling a plurality of pulse waves, and half of the single examination time is about 20 minutes.
S102: calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
specifically, the calculating, region by region, a time-frequency correlation between the TOI signal and the nTHI signal specifically includes: and (3) collecting TOI signals and nTHI signals region by using an NIRS technology according to the blood flow direction of bilateral carotid arteries, and calculating the time-frequency correlation of the nTHI signals and the TOI signals of each region.
In particular, the vascular elasticity index, in particular expressed as: the vascular elasticity index is an intuitive expression mode of the strength and weakness of the vascular elasticity, wherein the vascular elasticity index VEI is calculated in a mode of： 
Specifically, calculating the time domain correlation of the nth signal, the dHb signal and the dHbO2 signal region by region specifically includes: firstly, a portable NIRS signal collector is used and respectively applied to the position of bilateral carotid arteries, brain hemodynamic parameters such as TOI, nTHI signals, dHbO2 and dHb signals are collected region by region, the sampling frequency is 20Hz, and then the time domain correlation of the nTHI signals, the dHb signals and the dHbO2 signals is calculated region by region.
The blood flow change index is specifically expressed as: collecting TOI signals, nTHI signals, dHbO2 and dHb signals region by region according to the bilateral carotid artery blood flow direction, calculating the time-frequency correlation of the nTHI signals and the TOI signals in each region and the time-frequency correlation of the nTHI signals and the dHbO2 signals, and obtaining a vascular elasticity index and a blood flow change index based on pulse waves, wherein the calculation equation of the blood flow change index BFCI is as follows:
s103: preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
specifically, after preprocessing the regional blood vessel elasticity index and the regional blood flow change index, a regional blood vessel elasticity difference curve, a regional blood flow difference curve and a corresponding position number are obtained, which specifically includes: the bilateral carotid artery is divided into 4 areas from bottom to top, and the left carotid artery area is marked as JL1、JL2、JL3And JL4The right carotid region is labeled JR1、JR2、JR3And JR4. By measuring the vascular elasticity index VEI and the blood flow change index BFCI at each position, a regional vascular elasticity difference curve and a regional blood flow difference curve can be obtained.
S104: and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
Specifically, the method for evaluating the high-risk stenosis probability and the CS corresponding region of a human by using a trained LSTM deep learning model according to an obtained regional blood vessel elasticity difference curve and regional blood flow difference curve specifically comprises the following steps: excavating the change characteristics of a blood vessel elasticity curve and a blood flow difference curve based on time by using an LSTM network, wherein an area with small change corresponds to a CS area, the output of the LSTM network is recorded as FO, the dimensionality is 1 multiplied by 16, and eight positions JL of bilateral carotid arteries correspond to the CS area1，JL2，JL3，JL4，JR1，JR2，JR3，JR4The value of the CS probability index is 0-1, 0 represents that the CS high-risk probability is lower, and 1 represents that the CS high-risk probability is higher.
Referring to fig. 2, a second aspect of the present invention provides a carotid artery stenosis degree analysis device based on NIRS technology, comprising:
a collecting unit: adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signals, dHbO2 and dHb, the sampling frequency was 20 Hz.
A model establishing unit: calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
an analysis processing unit: preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
an evaluation unit: and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
As shown in figure 3, a non-invasive data acquisition mode is adopted, only two photoelectric probes need to be attached to the carotid artery sides at two sides, the data sampling frequency of the instrument used by the invention is 20Hz, a plurality of pulse waves are sampled, the half single examination time is about 20 minutes, and after data are collected, the high-risk stenosis probability and the CS corresponding region of a human are obtained through calculation and analysis.
As shown in fig. 4, an embodiment of the present invention provides an electronic device, which includes a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and running on the processor 420, and when the processor 420 executes the computer program 411, the following steps are implemented:
adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signal, dHb02, and dHb;
calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
In a specific implementation, when the processor 420 executes the computer program 411, any of the embodiments corresponding to fig. 1 may be implemented.
Since the electronic device described in this embodiment is a device used for implementing a data processing apparatus in the embodiment of the present invention, based on the method described in this embodiment of the present invention, a person skilled in the art can understand the specific implementation manner of the electronic device in this embodiment and various variations thereof, so that how to implement the method in this embodiment of the present invention by the electronic device is not described in detail herein, and as long as the person skilled in the art implements the device used for implementing the method in this embodiment of the present invention, the device used for implementing the method in this embodiment of the present invention belongs to the protection scope of the present invention.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating an embodiment of a computer-readable storage medium according to the present invention.
As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having a computer program 511 stored thereon, the computer program 511 implementing the following steps when executed by a processor:
adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signal, dHbO2, and dHb;
calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
In a specific implementation, the computer program 511 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present invention further provide a computer program product, where the computer program product includes computer software instructions, and when the computer software instructions are run on a processing device, the processing device is caused to execute the flow in the data processing method in the embodiment corresponding to fig. 1.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A carotid artery stenosis degree analysis method based on NIRS technology is characterized by comprising the following steps:
adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the signal-acquired brain hemodynamic parameters include, but are not limited to: TOI, nTHI signal, dHbO2, and dHb;
calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region to obtain a blood flow change index region by region;
preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
2. The method for analyzing stenosis degree of carotid artery according to claim 1, wherein the calculating time-frequency correlation between TOI signal and nTHI signal region by region specifically comprises:
and (3) collecting TOI signals and nTHI signals region by using an NIRS technology according to the blood flow direction of bilateral carotid arteries, and calculating the time-frequency correlation of the nTHI signals and the TOI signals of each region.
3. The method for analyzing stenosis degree of carotid artery according to claim 1, wherein the vascular elasticity index is specifically expressed as:
blood vessel elastic fingerThe number is an intuitive expression mode of the elasticity of the blood vessel, wherein the calculation mode of the VEI is as follows:
4. the method for analyzing the stenosis degree of carotid artery based on NIRS technology as claimed in claim 1, wherein the calculating the time-domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal region by region specifically comprises:
the portable NIRS signal collector is respectively applied to the positions of bilateral carotid arteries, brain hemodynamic parameters such as TOI, nTHI signals, dHb02 and dHb signals are collected region by region, and the sampling frequency is 20 Hz;
the time domain correlation of the nTHI signal, the dHb signal and the dHbO2 signal is calculated region by region.
5. The method for analyzing stenosis degree of carotid artery according to claim 1, wherein the index of blood flow variation is specifically expressed as:
collecting TOI signals, nTHI signals, dHbO2 and dHb signals region by region according to the bilateral carotid artery blood flow direction, calculating the time-frequency correlation of the nTHI signals and the TOI signals in each region and the time-frequency correlation of the nTHI signals and the dHbO2 signals, and obtaining a vascular elasticity index and a blood flow change index based on pulse waves, wherein the calculation equation of the blood flow change index BFCI is as follows:
6. the method for analyzing the stenosis degree of carotid artery according to claim 1, wherein the preprocessing the regional blood vessel elasticity index and the blood flow variation index to obtain the regional blood vessel elasticity difference curve and the regional blood flow difference curve and the corresponding position numbers comprises:
the left and right carotid arteries are connected with each otherDivided into 4 regions from bottom to top, the left carotid region labeled JL1、JL2Tea table3And JL4The right carotid region is labeled JR1、JR2、JR3And JR4；
The vascular elasticity index VEI and the blood flow change index BFCI are measured at each location to obtain a regional vascular elasticity difference curve and a regional blood flow difference curve.
7. The method for analyzing the stenosis degree of carotid artery according to claim 1 or 6, wherein the method for evaluating the high-risk stenosis probability and the CS corresponding region of human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model specifically comprises:
mining the change characteristics of a blood vessel elasticity curve and a blood flow difference curve based on time by using an LSTM network, wherein an area with small change corresponds to a CS area, the output of the LSTM network is recorded as FO, the dimensionality is 1 multiplied by 16, and eight positions JL of left and right carotid arteries are corresponded to1，JL2，JL3，JL4，JR1，JR2，JR3，JR4The value of the CS probability index is 0-1, 0 represents that the CS high-risk probability is lower, and 1 represents that the CS high-risk probability is higher.
8. A carotid artery stenosis degree analysis device based on NIRS technology, comprising:
a collecting unit: adopting a portable NIRS signal collector to collect signal cerebral hemodynamic parameters region by region at the positions of bilateral carotid arteries respectively; the acquisition signal brain hemodynamic parameters comprise: TOI, nTHI signals, dHb02 and dHb, wherein the sampling frequency is 20 Hz;
a model establishing unit: calculating time-frequency correlation between the TOI signal and the nTHI signal region by region to obtain a blood vessel elasticity index region by region, and calculating time-domain correlation between the nTHI signal, the dHb signal and the dHb02 signal region by region to obtain a blood flow change index region by region;
an analysis processing unit: preprocessing the blood vessel elasticity index and the blood flow change index region by region to obtain a region blood vessel elasticity difference curve, a region blood flow difference curve and corresponding position numbers;
an evaluation unit: and (3) evaluating the high-risk stenosis probability and the CS corresponding region of the human according to the obtained regional blood vessel elasticity difference curve and regional blood flow difference curve by using the trained LSTM deep learning model.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program may implement the method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
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