Femoral artery location determination method and apparatus

Abstract

The application discloses a femoral artery position determination method and device, and relates to the technical field of femoral artery puncture. The femoral artery position determination device comprises a femoral artery data acquisition module, a femoral artery signal detection module and a femoral artery position determination module. The application realizes accurate perception and judgment of the position of the femoral artery by combining image analysis with patient sign data. The femoral artery image reflected light intensity analysis model is used to pre-process and analyze the input femoral artery image and patient sign data, so that femoral artery pulse signal data are obtained. The pulse wave signal perception model is used to enhance the signal and preliminarily determine the perception range of the femoral artery. Based on the femoral artery position determination model, the sign data and image features are fused to further screen the accurate position of the femoral artery. Through the multi-level analysis model, efficient recognition and positioning of the position of the femoral artery are realized, the accuracy and reliability of detection are improved, and the application prospect is good.

Description

Technical Field

[0001] The present invention relates to the technical field of femoral artery puncture, and in particular to a method and device for determining the position of a femoral artery. Background Art

[0002] Femoral artery puncture is mainly used to obtain blood samples, perform diagnostic tests or implement interventional treatments. It is a key diagnostic and treatment method. During femoral artery puncture, if the position of the femoral artery is uncertain, serious complications may occur, including but not limited to local hematoma, retroperitoneal hematoma, pseudoaneurysm and arteriovenous fistula. These complications will not only increase the patient's pain, but may also have long-term effects on the patient's health and even endanger their life. Therefore, accurate femoral artery positioning is crucial to ensure the safety and effectiveness of the puncture operation.

[0003] Methods for determining the location of the femoral artery include surface positioning, stethoscope measurement, X-ray examination, ultrasound examination, CT angiography, and magnetic resonance imaging. However, most of these methods rely on the professional skills of medical staff to accurately locate the femoral artery. Different medical staff or different patient conditions may lead to inaccurate determination of the femoral artery location. Summary of the Invention

[0004] The present invention aims to provide a method and device for determining the position of a femoral artery, thereby improving the accuracy of determining the position of the femoral artery.

[0005] A method for determining a femoral artery position comprises the following steps:

[0006] Step S1: Femoral artery data acquisition

[0007] Obtain the target femoral artery image set P to be analyzed, P = {P1, P2, ..., P n ,…,P N}; Among them, P n The target femoral artery image P to be analyzed in the target femoral artery image set P is represented as n , N is the total number of target femoral artery images in a set of target femoral artery images to be analyzed P; obtain target patient vital sign data; input the target femoral artery image set P to be analyzed and the target patient vital sign data into the femoral artery image reflected light intensity analysis model for analysis, and obtain the femoral artery pulse signal dataset B and all preprocessed target femoral artery images to be analyzed P n ', B={B1,B2,…,B n ,…,B N The femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer. It is constructed based on a CNN model and a multi-layer perceptron and is used to analyze the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient's vital sign data.

[0008] Step S2: Femoral artery signal detection

[0009] The femoral artery pulse signal dataset B, all pre-processed target femoral artery images P n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range.

[0010] Step S3: Femoral artery position determination

[0011] The femoral artery range is determined based on the femoral artery position perception range and the femoral artery position determination model to obtain the femoral artery position determination result; the next step is performed based on the femoral artery position determination result; the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, which is constructed based on the CNN model and is used to further screen the precise range of the femoral artery.

[0012] As a preferred technical solution of the present invention, the femoral artery image reflected light intensity analysis model in step S1 includes an image preprocessing layer, a regional analysis layer, a signal extraction layer and a result output layer;

[0013] The image preprocessing layer is used to process the target femoral artery image P to be analyzed according to the target patient's vital sign data and the target femoral artery image set P to be analyzed. n Perform feature analysis to obtain the pre-processed target femoral artery image P n ';

[0014] The regional analysis layer is used to analyze the femoral artery image P according to the preprocessing target n 'Identify the signal extraction area and obtain the femoral artery signal extraction area R n ;

[0015] The signal extraction layer is used to extract region R based on the femoral artery signal n Perform signal recognition to obtain femoral artery pulse signal data B n ;

[0016] The result output layer is used to convert all femoral artery pulse signal data B n The femoral artery pulse signal dataset B is obtained by combining the femoral artery pulse signal dataset B and outputting the femoral artery pulse signal dataset B.

[0017] As a preferred technical solution of the present invention, the specific steps of constructing the image preprocessing layer include:

[0018] In the image preprocessing layer, four image preprocessing adjustment and analysis layers are constructed;

[0019] In the first image preprocessing adjustment and analysis layer, a multi-layer perceptron is used to construct several fully connected layers for extracting high-dimensional representations of patient vital sign data to obtain patient vital sign data features; in the second image preprocessing adjustment and analysis layer, a CNN network is used to construct several convolutional layers for extracting image data features of patient image data to obtain patient image data features; in the third image preprocessing adjustment and analysis layer, a neural network structure is used to construct several embedding layers for splicing patient vital sign data features and patient image data features to obtain a patient fusion feature vector; the patient fusion feature vector is input into a trained contrast loss optimization function for calculation to obtain an optimized patient fusion feature vector; in the fourth image preprocessing adjustment and analysis layer, a multi-layer perceptron is used to generate image adjustment parameters based on the optimized patient fusion feature vector, and the input patient image data is adjusted using the image adjustment parameters to obtain preprocessed patient image data;

[0020] The specific steps for training the image preprocessing layer include:

[0021] Collecting several groups of image preprocessing training samples; each group of image preprocessing training samples includes sample input values ​​and sample output values; wherein the sample input values ​​are patient vital sign data and femoral artery images of the patient to be processed, and the sample output values ​​are preprocessed femoral artery images of the patient; combining the several groups of image preprocessing training samples to obtain an image preprocessing training set;

[0022] The image preprocessing training set is input into the image preprocessing layer and the model training is performed with the sample output value as the target to obtain the image preprocessing layer to be evaluated; the image preprocessing layer to be evaluated is subjected to image evaluation to obtain the image preprocessing layer model evaluation result; if the image preprocessing layer model evaluation result is passed, the image preprocessing layer to be evaluated is used as the image preprocessing layer in the femoral artery image reflected light intensity analysis model; otherwise, the model training is continued using the image preprocessing training set.

[0023] As a preferred technical solution of the present invention, the femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlapping layer and a signal range output layer;

[0024] The signal processing layer is used to process the femoral artery pulse signal data B in the femoral artery pulse signal data set B. n Adaptive hybrid filter is used to filter the femoral artery pulse signal data B n Perform denoising to obtain pre-processed femoral artery pulse signal data B n ';

[0025] The signal feature matching layer is used to pre-process the femoral artery pulse signal data Bn ' and pre-process the target femoral artery image P to be analyzed n 'Perform feature matching to obtain the femoral artery enhanced marked image Z n ;

[0026] The image overlap layer is used to enhance all femoral artery images Z n Perform image overlap to obtain the femoral artery position sensing range;

[0027] The signal range output layer is used to output the femoral artery position perception range.

[0028] As a preferred technical solution of the present invention, the specific steps of constructing an adaptive hybrid filter include:

[0029] Construct a random generation of K filter configuration parameter individuals H k , k=1,2,…,K; each filter configuration parameter individual H k Contains a set of filter configuration parameters for constructing an adaptive hybrid filter; the K filter configuration parameters H k Combining the filter configuration parameters into an iterative population; constructing a signal processing simulation training set;

[0030] Dividing the filter configuration parameter iteration population iteration phase into a filter configuration parameter first iteration phase, a filter configuration parameter second iteration phase, and a filter configuration parameter third iteration phase;

[0031] The filter configuration parameters H k Perform simulation calculations to obtain the fitness S k The specific steps of the simulation calculation are:

[0032] Using the filter configuration parameter individual H k Construct a simulated adaptive hybrid filter; use the signal processing simulation training set to evaluate the model of the simulated adaptive hybrid filter and obtain the simulation adaptive hybrid filter model evaluation result; use the simulation adaptive hybrid filter model evaluation result as the filter configuration parameter individual H k The fitness S k ;

[0033] In the first iteration phase of the filter configuration parameters, the formula H is used k ′=H k +W*(H k -rand(J)) for the filter configuration parameter individual H k Perform population iteration, H k 'Indicates the update of filter configuration parameters individual H k ', W represents the adaptive adjustment coefficient, rand(J) represents the random solution; and the filter configuration parameters of the individual H are updatedk 'Recalculate fitness;

[0034] In the second iteration phase of filter configuration parameters, the formula The filter configuration parameters H k To update, G k Represents the filter configuration parameter individual H k The mutation probability, S k Represents the filter configuration parameter individual H k The fitness S k , β represents the adaptive mutation parameter; using G k The filter configuration parameters H k Update to get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate fitness;

[0035] In the third iteration phase of the filter configuration parameters, new filter configuration parameter individuals are randomly generated for the filter configuration parameter individual H k Replace it and get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate fitness;

[0036] When the population iteration ends, the filter configuration parameter individual H corresponding to the maximum output fitness k , which is the optimal filter configuration parameter individual, and the filter configuration parameters in the optimal filter configuration parameter individual are used to construct an adaptive hybrid filter.

[0037] As a preferred technical solution of the present invention, the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer;

[0038] The vital sign data acquisition layer is used to reacquire the vital sign data of the target patient to obtain updated vital sign data of the target patient; perform feature extraction on the updated vital sign data of the target patient to obtain features of the updated vital sign data of the target patient;

[0039] The feature fusion layer is used to extract the femoral artery position perception range features to obtain the femoral artery position perception range features; the updated target patient's vital sign data features and the femoral artery position perception range features are fused to obtain the fused femoral artery position perception range features;

[0040] The position determination layer is used to further determine the position based on the fused femoral artery position sensing range feature and the femoral artery position sensing range to obtain a femoral artery position determination result;

[0041] The position result output layer is used to output the femoral artery position determination result.

[0042] As a preferred technical solution of the present invention, the specific steps of training the position determination layer include:

[0043] Collecting several groups of femoral artery position determination training samples, each group of femoral artery position determination training samples includes training patient physical sign data, an initial femoral artery determination range, and a corresponding final femoral artery determination range; using the final femoral artery determination range as a target value for the femoral artery position determination training sample; and combining the several groups of femoral artery position determination training samples to obtain a femoral artery position determination training set;

[0044] The femoral artery position determination training set is input into the femoral artery position determination model to train the position determination layer with the target value as the target to obtain the initial position determination layer; the initial position determination layer is evaluated; if the initial position determination layer passes the model evaluation, the initial position determination layer is used as the position determination layer in the femoral artery position determination model; otherwise, the femoral artery position determination training set is used to continue model training.

[0045] A femoral artery position determination device, comprising:

[0046] The femoral artery data acquisition module includes a vital sign data acquisition unit and a femoral artery image data acquisition unit; the vital sign data acquisition unit is used to acquire the vital sign data of the target patient; the femoral artery image data acquisition unit is used to acquire the target femoral artery image set P to be analyzed, P = {P1, P2, ..., P n ,…,P N}; Among them, P n The target femoral artery image P to be analyzed in the target femoral artery image set P is represented as n , N is the total number of target femoral artery images in a set of target femoral artery images to be analyzed P;

[0047] The femoral artery signal detection module includes a femoral artery image processing unit and a signal sensing unit; the femoral artery image processing unit is used to input the target femoral artery image set P to be analyzed and the target patient's vital signs data into the femoral artery image reflected light intensity analysis model for analysis, and obtain the femoral artery pulse signal data set B and all pre-processed target femoral artery images P to be analyzed. n ', B={B1,B2,…,B n ,…,B NThe femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer. It is constructed based on a CNN model and a multi-layer perceptron and is used to analyze the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient's vital signs data. The signal perception unit is used to combine the femoral artery pulse signal dataset B, all preprocessed target femoral artery images P to be analyzed, and ... n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range.

[0048] The femoral artery position determination module includes a position determination unit; the position determination unit is used to determine the femoral artery range based on the femoral artery position perception range and the femoral artery position determination model to obtain the femoral artery position determination result; the next step is performed based on the femoral artery position determination result; the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, which is constructed based on the CNN model and is used to further screen the precise range of the femoral artery.

[0049] The present invention has the following advantages:

[0050] 1. The present invention achieves accurate perception and determination of the femoral artery position by combining image analysis with patient vital sign data. The input femoral artery image and patient vital sign data are preprocessed and analyzed using a femoral artery image reflected light intensity analysis model to obtain femoral artery pulse signal data. The pulse wave signal perception model is used to enhance the signal and preliminarily determine the perception range of the femoral artery. Based on the femoral artery position determination model, the femoral artery's precise position is further screened by fusing vital sign data with image features. Through a multi-level analysis model, efficient identification and positioning of the femoral artery position are achieved, improving the accuracy and reliability of detection, and having good application prospects.

[0051] 2. The present invention improves the accuracy and effect of image preprocessing by constructing a multi-level image preprocessing layer, effectively combining patient vital sign data and femoral artery image data; uses a multi-layer perceptron and a convolutional neural network to extract high-dimensional features of patient vital signs and image data, and fuses the two through an embedding layer; uses a contrast loss optimization function to further optimize the fused feature vector, generate image adjustment parameters, and accurately adjust the input image data, which helps to ensure high-quality preprocessing of femoral artery images, provides more reliable data support for subsequent signal extraction and femoral artery position determination, and significantly improves the accuracy and robustness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A structure schematic diagram of a femoral artery position determination device used in an embodiment of the present application.

[0053] Figure 2 A flowchart of a femoral artery position determination method used in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0055] Embodiment 1, a femoral artery position determination method, refer to Figure 2 as shown, comprising the following steps:

[0056] Step S1: femoral artery data acquisition

[0057] Acquire a target femoral artery image set P to be analyzed, P = {P1, P2, …, P n , …, P N}; wherein P n represents a target femoral artery image P n to be analyzed in the target femoral artery image set P to be analyzed, N is the total number of target femoral artery images in the target femoral artery image set P to be analyzed; acquire target patient sign data; input the target femoral artery image set P to be analyzed and the target patient sign data into a femoral artery image reflected light intensity analysis model for analysis to obtain a femoral artery pulse signal data set B and all preprocessed target femoral artery images P n ’ to be analyzed, B = {B1, B2, …, B n , …, B N}; the femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a region analysis layer, a signal extraction layer and a result output layer, and is constructed based on a CNN model and a multilayer perceptron, and is used for analyzing the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient sign data;

[0058] The target femoral artery image set to be analyzed can be acquired by using a medical optical imaging method, and the target patient sign data can be acquired by using a plurality of sensors;

[0059] The femoral artery image reflected light intensity analysis model in step S1 includes an image preprocessing layer, a region analysis layer, a signal extraction layer and a result output layer;

[0060] The image preprocessing layer is used for performing feature analysis on the target patient sign data and the target femoral artery image P n to be analyzed in the target femoral artery image set P to be analyzed to obtain a preprocessed target femoral artery image Pn ';

[0061] The regional analysis layer is used to analyze the femoral artery image P according to the preprocessing target n 'Identify the signal extraction area and obtain the femoral artery signal extraction area R n ;

[0062] The signal extraction layer is used to extract region R based on the femoral artery signal n Perform signal recognition to obtain femoral artery pulse signal data B n ;The signal recognition method is to extract the signal using the gray value method;

[0063] The result output layer is used to convert all femoral artery pulse signal data B n Combining to obtain a femoral artery pulse signal dataset B, and outputting the femoral artery pulse signal dataset B;

[0064] By constructing a femoral artery image reflected light intensity analysis model, pulse signal data can be efficiently extracted from femoral artery images, with significant benefits. The image preprocessing layer combines patient vital sign data with femoral artery images for feature analysis, ensuring effective image preprocessing. The regional analysis layer accurately identifies signal extraction areas, improving signal extraction accuracy. The signal extraction layer further identifies pulse signals within specific areas, ensuring signal integrity and accuracy. The result output layer combines all extracted signal data to generate a complete femoral artery pulse signal dataset. This layered processing method effectively improves the accuracy and reliability of signal extraction, providing a solid foundation for subsequent femoral artery analysis.

[0065] The specific steps to build the image preprocessing layer include:

[0066] In the image preprocessing layer, four image preprocessing adjustment and analysis layers are constructed;

[0067] In the first image preprocessing adjustment analysis layer, a plurality of fully connected layers are constructed using a multilayer perception mechanism to extract a high-dimensional representation of the patient sign data, obtaining patient sign data features; in the second image preprocessing adjustment analysis layer, a plurality of convolutional layers are constructed using a CNN network to extract image data features of the patient image data, obtaining patient image data features; in the third image preprocessing adjustment analysis layer, a plurality of embedding layers are constructed using a neural network structure to splice the patient sign data features and the patient image data features, obtaining a patient fusion feature vector; the patient fusion feature vector is input into the trained contrast loss optimization function for calculation, obtaining an optimized patient fusion feature vector; in the fourth image preprocessing adjustment analysis layer, a plurality of fully connected layers are constructed using a multilayer perception mechanism to generate image adjustment parameters according to the optimized patient fusion feature vector, and the input patient image data is adjusted using the image adjustment parameters to obtain preprocessed patient image data.

[0068] The specific steps for training the image preprocessing layer include:

[0069] A plurality of groups of image preprocessing training samples are collected; each group of image preprocessing training samples contains sample input values and sample output values; wherein the sample input values are patient sign data and to-be-processed patient femoral artery images, and the sample output values are preprocessed patient femoral artery images; the plurality of groups of image preprocessing training samples are combined to obtain an image preprocessing training set;

[0070] The image preprocessing training set is input into the image preprocessing layer for model training with the sample output values as the target, obtaining a to-be-evaluated image preprocessing layer; the to-be-evaluated image preprocessing layer is subjected to image evaluation, obtaining an image preprocessing layer model evaluation result; if the image preprocessing layer model evaluation result is passed, the to-be-evaluated image preprocessing layer is taken as the image preprocessing layer in the femoral artery image reflected light intensity analysis model; otherwise, the image preprocessing training set is used for further model training;

[0071] The multi-layer perceptron extracts high-dimensional features from the patient's vital sign data and deeply analyzes the patient's vital sign information through the fully connected layer. This ensures that richer and more accurate individualized patient features are extracted, providing effective data support for subsequent processing. This step fully utilizes the patient's physiological data, making the model more adaptable to individual differences. The CNN network extracts features from the femoral artery image data, which can capture subtle features in the image, such as light intensity changes and texture information, which are crucial for subsequent femoral artery signal extraction. The application of the convolutional layer enables the model to automatically discover key features in the image, avoiding the limitations of traditional manual feature selection. The vital sign data features and image data features are spliced ​​to form a fused feature vector. The design of the neural network embedding layer ensures that the two different dimensional data can be effectively integrated to avoid information loss and conflict. This can improve the performance of the model when processing multimodal data and make the fused feature vector more representative. The multi-layer perceptron generates image adjustment parameters based on the optimized fused feature vector and accurately adjusts the image. The calculation of the contrast loss optimization function further enhances the pertinence and accuracy of the image adjustment. This step ensures that the final output preprocessed image has the best quality, suitable for subsequent analysis and processing, and optimizes the overall performance of the model.

[0072] Through these steps, the image preprocessing layer can effectively combine patient vital signs with image features, improving the accuracy of image preprocessing and ensuring the accuracy of subsequent femoral artery pulse signal extraction. The model's multiple iterations and evaluation mechanisms also ensure its adaptability to different patients, improving the overall performance of the femoral artery image analysis model.

[0073] Step S2: Femoral artery signal detection

[0074] The femoral artery pulse signal dataset B, all pre-processed target femoral artery images P n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range.

[0075] The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer;

[0076] The signal processing layer is used to process the femoral artery pulse signal data B in the femoral artery pulse signal data set B. n Adaptive hybrid filter is used to filter the femoral artery pulse signal data B n Perform denoising to obtain pre-processed femoral artery pulse signal data B n';

[0077] The signal feature matching layer is used to pre-process the femoral artery pulse signal data B n ' and pre-process the target femoral artery image P to be analyzed n 'Perform feature matching to obtain the femoral artery enhanced marked image Z n ;

[0078] The image overlap layer is used to enhance all femoral artery images Z n Perform image overlap to obtain the femoral artery position sensing range;

[0079] The signal range output layer is used to output the femoral artery position perception range;

[0080] An adaptive hybrid filter is used to denoise the femoral artery pulse signal, effectively removing noise from the signal and improving signal clarity and accuracy. The pre-processed pulse signal is more stable. By matching the denoised pulse signal data with the pre-processed femoral artery image, the relevant information of the image is enhanced, and a femoral artery enhanced marker image is obtained. This can improve the correlation between the pulse signal and the image, allowing the system to accurately identify the specific location of the femoral artery signal and improve the ability to judge the signal area. All femoral artery enhanced marker images are overlapped to form a perception range of the femoral artery position. This layer integrates information through image superposition, enhancing the positioning effect of the femoral artery position. By overlapping the data of multiple images, accidental errors can be eliminated and the accurate perception of the target area can be improved.

[0081] The specific steps of constructing an adaptive hybrid filter include:

[0082] Construct a random generation of K filter configuration parameter individuals H k , k=1,2,…,K; each filter configuration parameter individual H k Contains a set of filter configuration parameters for constructing an adaptive hybrid filter; the K filter configuration parameters H k Combining the filter configuration parameters into an iterative population; constructing a signal processing simulation training set;

[0083] Dividing the filter configuration parameter iteration population iteration phase into a filter configuration parameter first iteration phase, a filter configuration parameter second iteration phase, and a filter configuration parameter third iteration phase;

[0084] The filter configuration parameters H k Perform simulation calculations to obtain the fitness S k The specific steps of the simulation calculation are:

[0085] Using the filter configuration parameter individual H kConstruct a simulated adaptive hybrid filter; use the signal processing simulation training set to evaluate the model of the simulated adaptive hybrid filter and obtain the simulation adaptive hybrid filter model evaluation result; use the simulation adaptive hybrid filter model evaluation result as the filter configuration parameter individual H k The fitness S k ;

[0086] In the first iteration phase of the filter configuration parameters, the formula H is used k ′=H k +W*(H k -rand(J)) for the filter configuration parameter individual H k Perform population iteration, H k 'Indicates the update of filter configuration parameters individual H k ', W represents the adaptive adjustment coefficient, rand(J) represents the random solution; and the filter configuration parameters of the individual H are updated k 'Recalculate the fitness; the specific value of W is set by professional technicians according to actual conditions;

[0087] In the second iteration phase of filter configuration parameters, the formula The filter configuration parameters H k To update, G k Represents the filter configuration parameter individual H k The mutation probability, S k Represents the filter configuration parameter individual H k The fitness S k , β represents the adaptive mutation parameter; using G k The filter configuration parameters H k Update to get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate the fitness; the specific value of β is set by professional technicians based on actual conditions;

[0088] In the third iteration phase of the filter configuration parameters, new filter configuration parameter individuals are randomly generated for the filter configuration parameter individual H k Replace it and get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate fitness;

[0089] When the population iteration ends, the filter configuration parameter individual H corresponding to the maximum output fitness k , that is, the optimal filter configuration parameter individual, the filter configuration parameters in the optimal filter configuration parameter individual are used to construct an adaptive hybrid filter;

[0090] The K filter configuration parameter individuals are randomly generated to ensure the diversity of the initial values of the filter parameters, to avoid the emergence of local optimal problems by constructing an iterative population, to help improve the global exploration ability in the optimization process, and to make the finally obtained filter configuration parameters have stronger adaptability and robustness. The method divides the iterative process into three stages, and optimizes by combining the adaptive adjustment, mutation update and random replacement. In the first stage, the adaptive adjustment coefficient W is used to make the parameter individuals more random in the early exploration and to enhance the global search ability. In the second stage, the mutation probability and the adaptive mutation parameter β are introduced to make the individuals gradually converge to the optimal region. In the third stage, new individuals are randomly generated to further increase the diversity of the search and the optimization effect. After the population iteration is completed, the filter configuration parameter individual with the maximum fitness is selected to ensure the optimal performance of the filter. The finally constructed adaptive hybrid filter can dynamically adjust the parameters according to the signal characteristics, effectively improve the denoising ability and filtering effect of signal processing, and is especially suitable for processing the noise and interference in the femoral artery pulse signal.

[0091] Step S3: femoral artery position determination

[0092] The femoral artery range is determined according to the femoral artery position sensing range and the femoral artery position determination model to obtain a femoral artery position determination result. The next operation is performed based on the femoral artery position determination result. The femoral artery position determination model includes a physical data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, and is constructed based on a CNN model to further filter the accurate range of the femoral artery.

[0093] The femoral artery position determination model includes a physical data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer.

[0094] The physical data acquisition layer is used to reacquire the physical data of the target patient to obtain updated physical data of the target patient. The updated physical data of the target patient is feature extracted to obtain the features of the updated physical data of the target patient. Through the physical data acquisition layer, the latest physical data of the target patient can be dynamically reacquired, and feature extraction and fusion are performed in combination with the femoral artery image data. This method ensures that the individual differences of the patient at present are always considered in the position determination process, increases the accuracy and individualization of the femoral artery position determination, and is suitable for the diversified needs of different patients.

[0095] The feature fusion layer is used to extract features of the femoral artery position perception range to obtain the femoral artery position perception range feature; the updated target patient's vital sign data features and the femoral artery position perception range features are fused to obtain the fused femoral artery position perception range feature; the feature fusion layer fuses the patient's vital sign data with the femoral artery position perception range feature to obtain a more comprehensive fusion feature; by combining vital sign and image information, the model can perform position screening in multiple dimensions, further improving the accuracy of femoral artery position determination and avoiding errors caused by single feature data;

[0096] The position determination layer is used to further determine the position based on the fused femoral artery position sensing range feature and the femoral artery position sensing range to obtain a femoral artery position determination result;

[0097] The position result output layer is used to output the femoral artery position determination result;

[0098] The specific steps for training the location determination layer include:

[0099] Collecting several groups of femoral artery position determination training samples, each group of femoral artery position determination training samples includes training patient physical sign data, an initial femoral artery determination range, and a corresponding final femoral artery determination range; using the final femoral artery determination range as a target value for the femoral artery position determination training sample; and combining the several groups of femoral artery position determination training samples to obtain a femoral artery position determination training set;

[0100] Inputting the femoral artery position determination training set into the femoral artery position determination model, training the position determination layer with the target value as the target, and obtaining an initial position determination layer; performing a model evaluation on the initial position determination layer; if the initial position determination layer passes the model evaluation, then using the initial position determination layer as the position determination layer in the femoral artery position determination model; otherwise, continuing model training using the femoral artery position determination training set;

[0101] By constructing a training set for femoral artery location determination and conducting iterative training, the model can be continuously optimized to ensure its robustness under different circumstances. The model evaluation mechanism ensures that the trained location determination layer has sufficient accuracy and stability. Models that fail the evaluation can continue training to ensure that the final results meet the expected standards. Through multi-level feature extraction, fusion, and CNN-based screening, the specific location of the femoral artery can be accurately determined. Dynamic updates of vital sign data combined with multi-dimensional feature fusion ensure the model's personalization and accuracy. Through continuous training and evaluation mechanisms, the model can provide efficient and accurate femoral artery location determination results in practical applications, facilitating the smooth implementation of subsequent medical or testing operations.

[0102] Example 2, a femoral artery position determination device, see Figure 1 Shown, including:

[0103] The femoral artery data acquisition module includes a vital sign data acquisition unit and a femoral artery image data acquisition unit; the vital sign data acquisition unit is used to acquire the vital sign data of the target patient; the femoral artery image data acquisition unit is used to acquire the target femoral artery image set P to be analyzed, P = {P1, P2, ..., P n ,…,P N}; Among them, P n The target femoral artery image P to be analyzed in the target femoral artery image set P is represented as n , N is the total number of target femoral artery images in a set of target femoral artery images to be analyzed P;

[0104] The femoral artery signal detection module includes a femoral artery image processing unit and a signal sensing unit; the femoral artery image processing unit is used to input the target femoral artery image set P to be analyzed and the target patient's vital signs data into the femoral artery image reflected light intensity analysis model for analysis, and obtain the femoral artery pulse signal data set B and all pre-processed target femoral artery images P to be analyzed. n ', B={B1,B2,…,B n ,…,B N The femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer. It is constructed based on a CNN model and a multi-layer perceptron and is used to analyze the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient's vital signs data. The signal perception unit is used to combine the femoral artery pulse signal dataset B, all preprocessed target femoral artery images P to be analyzed, and ... n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range.

[0105] The femoral artery position determination module includes a position determination unit; the position determination unit is used to determine the femoral artery range based on the femoral artery position perception range and the femoral artery position determination model to obtain the femoral artery position determination result; the next step is performed based on the femoral artery position determination result; the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, which is constructed based on the CNN model and is used to further screen the precise range of the femoral artery.

[0106] It is to be understood that all of the above modifications and alterations can be made to the above-described arrangements and that all such modifications and alterations are intended to be included within the scope of the present application. Those skilled in the art will readily appreciate that other modifications and alterations can be made to the present application without departing from the scope of the application.

Claims

1. A method for determining the position of a femoral artery, characterized in that: The following steps are involved: Step S1: Femoral artery data acquisition Obtain the target femoral artery image set P to be analyzed, P = {P1, P2, ..., P n ,…,P N }; Among them, P n The target femoral artery image P to be analyzed in the target femoral artery image set P is represented as n , N is the total number of target femoral artery images in a set of target femoral artery images to be analyzed P; obtain target patient vital sign data; input the target femoral artery image set P to be analyzed and the target patient vital sign data into the femoral artery image reflected light intensity analysis model for analysis, and obtain the femoral artery pulse signal dataset B and all preprocessed target femoral artery images to be analyzed P n ', B={B1,B2,…,B n ,…,B N The femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer. It is constructed based on a CNN model and a multi-layer perceptron and is used to analyze the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient's vital sign data. Step S2: Femoral artery signal detection The femoral artery pulse signal dataset B, all pre-processed target femoral artery images P n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range. Step S3: Femoral artery position determination The femoral artery range is determined based on the femoral artery position perception range and the femoral artery position determination model to obtain the femoral artery position determination result; the next step is performed based on the femoral artery position determination result; the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, which is constructed based on the CNN model and is used to further screen the precise range of the femoral artery.

2. A method for determining the position of a femoral artery according to claim 1, characterized in that: The femoral artery image reflected light intensity analysis model in step S1 includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer; The image preprocessing layer is used to process the target femoral artery image P to be analyzed according to the target patient's vital sign data and the target femoral artery image set P to be analyzed. n Perform feature analysis to obtain the pre-processed target femoral artery image P n '; The regional analysis layer is used to analyze the femoral artery image P according to the preprocessing target n 'Identify the signal extraction area and obtain the femoral artery signal extraction area R n ; The signal extraction layer is used to extract region R based on the femoral artery signal n Perform signal recognition to obtain femoral artery pulse signal data B n ; The result output layer is used to convert all femoral artery pulse signal data B n The femoral artery pulse signal dataset B is obtained by combining the femoral artery pulse signal dataset B and outputting the femoral artery pulse signal dataset B.

3. A method for determining the position of a femoral artery according to claim 2, characterized in that: The specific steps to build the image preprocessing layer include: In the image preprocessing layer, four image preprocessing adjustment and analysis layers are constructed; In the first image preprocessing adjustment and analysis layer, a multi-layer perceptron is used to construct several fully connected layers for extracting high-dimensional representations of patient vital sign data to obtain patient vital sign data features; in the second image preprocessing adjustment and analysis layer, a CNN network is used to construct several convolutional layers for extracting image data features of patient image data to obtain patient image data features; in the third image preprocessing adjustment and analysis layer, a neural network structure is used to construct several embedding layers for splicing patient vital sign data features and patient image data features to obtain a patient fusion feature vector; the patient fusion feature vector is input into a trained contrast loss optimization function for calculation to obtain an optimized patient fusion feature vector; in the fourth image preprocessing adjustment and analysis layer, a multi-layer perceptron is used to generate image adjustment parameters based on the optimized patient fusion feature vector, and the input patient image data is adjusted using the image adjustment parameters to obtain preprocessed patient image data; The specific steps for training the image preprocessing layer include: Collecting several groups of image preprocessing training samples; each group of image preprocessing training samples includes sample input values ​​and sample output values; wherein the sample input values ​​are patient vital sign data and femoral artery images of the patient to be processed, and the sample output values ​​are preprocessed femoral artery images of the patient; combining the several groups of image preprocessing training samples to obtain an image preprocessing training set; The image preprocessing training set is input into the image preprocessing layer and the model training is performed with the sample output value as the target to obtain the image preprocessing layer to be evaluated; the image preprocessing layer to be evaluated is subjected to image evaluation to obtain the image preprocessing layer model evaluation result; if the image preprocessing layer model evaluation result is passed, the image preprocessing layer to be evaluated is used as the image preprocessing layer in the femoral artery image reflected light intensity analysis model; otherwise, the model training is continued using the image preprocessing training set.

4. A method for determining the position of a femoral artery according to claim 3, characterized in that: The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer; The signal processing layer is used to process the femoral artery pulse signal data B in the femoral artery pulse signal data set B. n Adaptive hybrid filter is used to filter the femoral artery pulse signal data B n Perform denoising to obtain pre-processed femoral artery pulse signal data B n '; The signal feature matching layer is used to pre-process the femoral artery pulse signal data B n ' and pre-process the target femoral artery image P to be analyzed n 'Perform feature matching to obtain the femoral artery enhanced marked image Z n ; The image overlap layer is used to enhance all femoral artery images Z n Perform image overlap to obtain the femoral artery position sensing range; The signal range output layer is used to output the femoral artery position perception range.

5. A method for determining the position of a femoral artery according to claim 4, characterized in that: The specific steps of constructing an adaptive hybrid filter include: Construct a random generation of K filter configuration parameter individuals H k , k=1,2,…,K; each filter configuration parameter individual H k Contains a set of filter configuration parameters for constructing an adaptive hybrid filter; the K filter configuration parameters H k Combining the filter configuration parameters into an iterative population; constructing a signal processing simulation training set; Dividing the filter configuration parameter iteration population iteration phase into a filter configuration parameter first iteration phase, a filter configuration parameter second iteration phase, and a filter configuration parameter third iteration phase; The filter configuration parameters H k Perform simulation calculations to obtain the fitness S k The specific steps of the simulation calculation are: Using the filter configuration parameter individual H k Construct a simulated adaptive hybrid filter; use the signal processing simulation training set to evaluate the model of the simulated adaptive hybrid filter and obtain the simulation adaptive hybrid filter model evaluation result; use the simulation adaptive hybrid filter model evaluation result as the filter configuration parameter individual H k The fitness S k ; In the first iteration phase of the filter configuration parameters, the formula H is used k ′=H k +W*(H k -rand(J)) for the filter configuration parameter individual H k Perform population iteration, H k 'Indicates the update of filter configuration parameters individual H k ', W represents the adaptive adjustment coefficient, rand(J) represents the random solution; and the filter configuration parameters of the individual H are updated k 'Recalculate fitness; In the second iteration phase of filter configuration parameters, the formula The filter configuration parameters H k To update, G k Represents the filter configuration parameter individual H k The mutation probability, S k Represents the filter configuration parameter individual H k The fitness S k , β represents the adaptive mutation parameter; using G k The filter configuration parameters H k Update to get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate fitness; In the third iteration phase of the filter configuration parameters, new filter configuration parameter individuals are randomly generated for the filter configuration parameter individual H k Replace it and get the new filter configuration parameter individual H k , and configure the new filter parameters for the individual H k Recalculate fitness; When the population iteration ends, the filter configuration parameter individual H corresponding to the maximum output fitness k , which is the optimal filter configuration parameter individual, and the filter configuration parameters in the optimal filter configuration parameter individual are used to construct an adaptive hybrid filter.

6. A method for determining the position of a femoral artery according to claim 5, characterized in that: The femoral artery location determination model includes a vital sign data acquisition layer, a feature fusion layer, a location determination layer, and a location result output layer; The vital sign data acquisition layer is used to reacquire the vital sign data of the target patient to obtain updated vital sign data of the target patient; Extracting features of the updated target patient's vital sign data to obtain updated target patient's vital sign data features; The feature fusion layer is used to extract the features of the femoral artery position perception range to obtain the femoral artery position perception range features; Perform feature fusion on the updated target patient's vital sign data features and the femoral artery position perception range features to obtain a fused femoral artery position perception range feature; The position determination layer is used to further determine the position based on the fused femoral artery position sensing range feature and the femoral artery position sensing range to obtain a femoral artery position determination result; The position result output layer is used to output the femoral artery position determination result.

7. A method for determining the position of a femoral artery according to claim 6, characterized in that: The specific steps for training the location determination layer include: Collecting several groups of femoral artery position determination training samples, each group of femoral artery position determination training samples includes training patient physical sign data, an initial femoral artery determination range, and a corresponding final femoral artery determination range; using the final femoral artery determination range as a target value for the femoral artery position determination training sample; and combining the several groups of femoral artery position determination training samples to obtain a femoral artery position determination training set; The femoral artery position determination training set is input into the femoral artery position determination model to train the position determination layer with the target value as the target to obtain the initial position determination layer; the initial position determination layer is evaluated; if the initial position determination layer passes the model evaluation, the initial position determination layer is used as the position determination layer in the femoral artery position determination model; otherwise, the femoral artery position determination training set is used to continue model training.

8. A femoral artery position determination device, characterized in that: The device uses a femoral artery position determination method according to any one of claims 1 to 7, comprising: The femoral artery data acquisition module includes a vital sign data acquisition unit and a femoral artery image data acquisition unit; the vital sign data acquisition unit is used to acquire the vital sign data of the target patient; the femoral artery image data acquisition unit is used to acquire the target femoral artery image set P to be analyzed, P = {P1, P2, ..., P n ,…,P N }; Among them, P n The target femoral artery image P to be analyzed in the target femoral artery image set P is represented as n , N is the total number of target femoral artery images in a set of target femoral artery images to be analyzed P; The femoral artery signal detection module includes a femoral artery image processing unit and a signal sensing unit; the femoral artery image processing unit is used to input the target femoral artery image set P to be analyzed and the target patient's vital signs data into the femoral artery image reflected light intensity analysis model for analysis, and obtain the femoral artery pulse signal data set B and all pre-processed target femoral artery images P to be analyzed. n ', B={B1,B2,…,B n ,…,B N The femoral artery image reflected light intensity analysis model includes an image preprocessing layer, a regional analysis layer, a signal extraction layer, and a result output layer. It is constructed based on a CNN model and a multi-layer perceptron and is used to analyze the femoral artery pulse signal of the target femoral artery image set P to be analyzed in combination with the target patient's vital signs data. The signal perception unit is used to combine the femoral artery pulse signal dataset B, all preprocessed target femoral artery images P to be analyzed, and ... n The femoral artery pulse wave signal is input into the femoral artery pulse wave signal perception model for analysis to obtain the femoral artery position perception range. The femoral artery pulse wave signal perception model includes a signal processing layer, a signal feature matching layer, an image overlap layer, and a signal range output layer. Based on the improved optimization algorithm, a filter is constructed to further enhance the femoral artery pulse signal and preliminarily divide the femoral artery range. The femoral artery position determination module includes a position determination unit; the position determination unit is used to determine the femoral artery range based on the femoral artery position perception range and the femoral artery position determination model to obtain the femoral artery position determination result; the next step is performed based on the femoral artery position determination result; the femoral artery position determination model includes a vital sign data acquisition layer, a feature fusion layer, a position determination layer and a position result output layer, which is constructed based on the CNN model and is used to further screen the precise range of the femoral artery.

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