Adenoid hypertrophy auxiliary detection system and method based on kinetic graph fusion

Abstract

The invention provides an adenoid hypertrophy auxiliary detection system and method based on kinetic diagram fusion, and the method comprises the steps: modeling unknown nonlinear dynamics contained in a respiratory airflow velocity signal through a deterministic learning method according to respiratory airflow velocity data of left and right noses of a tester, and obtaining left and right respiratory kinetic diagrams; according to the method, a left respiratory dynamics graph and a right respiratory dynamics graph are obtained, a weight matrix is constructed to fuse the left respiratory dynamics graph and the right respiratory dynamics graph, the adenoid hypertrophy disease is diagnosed according to the morphological characteristics of the fused respiratory dynamics graphs, the constructed weight matrix can effectively reflect the pathological respiratory characteristics of adenoid hypertrophy, the influence caused by data asymmetry can be weakened, and the diagnosis accuracy of the adenoid hypertrophy disease is improved. The diagnosis precision of adenoid hypertrophy and the adaptability to adenoid hypertrophy diseases are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to adenoids auxiliary detection, and in particular relates to a system and method for auxiliary detection of adenoids hypertrophy based on kinetic image fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Adenoidal hypertrophy (AH) is the enlargement of the adenoids (pharyngeal tonsils). Its common causes are repeated episodes of acute and chronic nasopharyngitis, as well as inflammation of adjacent organs such as the nasal cavity, sinuses, and tonsils, which stimulate the pathological hyperplasia of adenoid tissue. Because the adenoids naturally shrink during adolescence, children are most often affected by adenoid diseases and pathological conditions, but some adults may also suffer from the disease. Adenoid hypertrophy reduces or blocks the passage of the upper airway and is the most common cause of upper airway obstruction in children and adolescents. Upper airway obstruction can partially contribute to health problems such as mouth breathing, nasal diseases, asthma, speech problems, and sleep apnea, and may even lead to more serious diseases such as obstructive sleep apnea, craniofacial development changes, and cognitive impairment. If adenoid hypertrophy is not treated, it usually leads to long-term health consequences. It is estimated that the prevalence of adenoid hypertrophy in the pediatric population is about 34.46% in a randomly selected representative sample.

[0004] At present, the main medical means for diagnosing adenoids hypertrophy are nasopharyngeal endoscopy and nasopharyngeal radiology, such as lateral skull X-rays. However, the above existing diagnostic methods for adenoids hypertrophy have their own clinical pain points: nasopharyngeal endoscopy is the gold standard for diagnosing adenoids hypertrophy, but the endoscopic examination operation requires the endoscope to go deep into the nasopharynx of the child, which may cause fear and physiological discomfort to the child and hinder the operation of the examination expert. In addition, the examination results of nasopharyngeal endoscopy may be affected by the subjective bias of the examiner. Nasopharyngeal radiology is an objective and non-invasive tool for evaluating the adenoids in the nasopharynx, but the easy movement of children will make it difficult to take pictures, and X-rays will also cause certain radiation damage.

[0005] The existing patent document CN117690580A proposes a detection system for adenoids hypertrophy based on respirodynamicsgram, which provides a convenient new way to evaluate nasal diseases by capturing changes in nasal airflow caused by abnormal nasal structure or related diseases. Based on a new dynamic environment machine learning technology - deterministic learning, the system can generate a respirodynamicsgram (RDG) from the airflow velocity data collected by the nasal resistance meter, and realize dynamic evaluation and diagnosis of adenoids hypertrophy by extracting features from the respirodynamicsgram.

[0006] However, there are several shortcomings in the existing adenoids hypertrophy detection system based on pneumodynamics:

[0007] 1. The respiratory dynamics graph is independently generated by collecting the airflow velocity data of the left and right nasal cavities of the child from the nasal resistance meter, which means that the respiratory dynamics graph of each nasal cavity is modeled separately. Given that there may be physiological or pathological differences between the left and right nasal cavities of the child, this difference may cause asymmetry in the airflow velocity data of the two nasal cavities. If the airflow velocity data of the left and right noses are significantly different, the separately modeled left and right nasal respiratory dynamics graphs may not consistently reflect the child's respiratory characteristics, which may affect the accurate diagnosis of the child's adenoid hypertrophy.

[0008] Second, in order to overcome the shortcomings of independent modeling of the left and right nasal respiratory dynamics, a possible solution is to extract the pathological respiratory features that reflect adenoid hypertrophy in the respiratory dynamics of both sides. However, there is currently a lack of an effective method to extract these consistent features from the respiratory dynamics. Summary of the invention

[0009] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides an auxiliary detection system and method for adenoids hypertrophy based on dynamic graph fusion, which obtains the airflow velocity data of the left and right nasal cavities and models the respiratory dynamic graphs respectively, and fuses the independently modeled respiratory dynamic graphs, and then extracts the characteristics of the fused respiratory dynamic graphs to improve the accuracy of auxiliary detection of adenoids hypertrophy.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides an auxiliary detection system for adenoids hypertrophy based on kinetic image fusion, the system comprising:

[0012] A preprocessing module is used to obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing;

[0013] A respiratory dynamics diagram module is used to construct a discrete-time nonlinear system according to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, respectively, and to locally model the unknown nonlinear dynamics of the discrete-time nonlinear system using an RBF neural network based on deterministic learning theory, and to combine the modeled nonlinear dynamics data and the corresponding nasal airflow velocity data to form a respiratory dynamics trajectory, thereby obtaining left and right nasal respiratory dynamics diagrams;

[0014] A respiratory dynamics graph fusion module is used to extract nonlinear dynamics data from the left and right nasal respiratory dynamics graphs of the tester, respectively, construct a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and use the weight matrix to fuse the nonlinear dynamics data extracted from the left and right nasal respiratory dynamics graphs to obtain a fused respiratory dynamics graph;

[0015] The output module is used to obtain the auxiliary detection result of adenoids hypertrophy according to the morphology of the fused respiratory dynamics diagram.

[0016] In a second aspect, the present invention provides a method for auxiliary detection of adenoids hypertrophy based on kinetic image fusion, the method comprising:

[0017] Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing;

[0018] According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained;

[0019] Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph;

[0020] According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

[0021] In a third aspect, the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the following steps are performed:

[0022] Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing;

[0023] According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained;

[0024] Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph;

[0025] According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the following steps are performed:

[0027] Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing;

[0028] According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained;

[0029] Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph;

[0030] According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

[0031] One or more of the above technical solutions have the following beneficial effects:

[0032] In the present invention, according to the left and right nasal breathing airflow velocity data of the tester, the unknown nonlinear dynamics contained in the breathing airflow velocity signal is modeled by a determined learning method to obtain the left and right respiratory dynamics graphs; and a weight matrix is ​​constructed to fuse the left and right respiratory dynamics graphs, and adenoids hypertrophy disease is diagnosed according to the morphological characteristics of the fused respiratory dynamics graphs. Due to the physiological or pathological differences that may exist in the left and right nasal cavities, the airflow velocity data of the two sides of the nasal cavity are asymmetric. This asymmetry will be introduced into the respiratory dynamics graph with the modeling of respiratory dynamics, and finally affect the detection accuracy of adenoids hypertrophy. The constructed weight matrix can effectively reflect the pathological respiratory characteristics of adenoids hypertrophy, can weaken the above-mentioned influence caused by data asymmetry, and improve the diagnostic accuracy of adenoids hypertrophy and the adaptability to adenoids hypertrophy diseases.

[0033] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0035] Figure 1 This is an overall block diagram of an auxiliary detection system for adenoids hypertrophy in Embodiment 1 of the present invention;

[0036] Figure 2 is the respiratory airflow velocity signal after preprocessing in the first embodiment of the present invention;

[0037] Figure 3 This is an example diagram of right nasal respiratory dynamics of a patient with adenoid hypertrophy having differences in the left and right nasal cavities in Example 1 of the present invention;

[0038] Figure 4 This is an example diagram of the respiratory dynamics of the left nose of a patient with adenoid hypertrophy having differences in the left and right nasal cavities in Example 1 of the present invention;

[0039] Figure 5 This is an example diagram of the right nasal respiratory dynamics after fusion of a patient with adenoid hypertrophy who has differences in the left and right nasal cavities in Example 1 of the present invention;

[0040] Figure 6 This is an example diagram of the respiratory dynamics of the left nose after fusion of a patient with adenoid hypertrophy who has differences in the left and right nasal cavities in Example 1 of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0043] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0044] In this embodiment, all data is obtained in compliance with laws and regulations and based on the user's consent, and is used legally.

[0045] Based on the study of the continuous excitation characteristics of radial basis function (RBF) neural networks, Wang Cong's book, "Deterministic Learning Theory for identification, recognition and control", published in 2007, proposed a deterministic learning theory, in which, on the basis of satisfying the continuous excitation conditions, the radial basis function (RBF) neural network is used to model the unknown nonlinear dynamics in the nonlinear system, and the dynamic time-varying nonlinear dynamics are locally accurately modeled and stored as a constant RBF network in a time-invariant and spatially distributed manner. Furthermore, using the accurately modeled constant RBF network, some downstream tasks of deterministic learning have been proposed and developed. The systems in actual engineering applications have discrete time characteristics, so the deterministic learning theory needs to be expanded to meet actual engineering needs. In the Chinese invention patent application: A rapid diagnosis method for rotating stall of an axial compressor based on deterministic learning theory (application number CN101887479A), a scheme for applying deterministic learning theory to surge detection of aircraft engines is proposed; a method for auxiliary detection of myocardial ischemia based on deterministic learning theory (application number CN103549949A) proposes a scheme for applying deterministic learning theory to early screening of myocardial ischemia; a view-independent gait recognition method based on deterministic learning theory (application number CN104134077A) proposes a scheme for applying deterministic learning theory to gait recognition; and the deterministic learning theory is expanded in the field of temporal data modeling.

[0046] Embodiment 1

[0047] When the respiratory airflow passes through the nasal cavity, factors such as the velocity and pressure of the airflow will be affected by the nasal structure. Therefore, when the nasal structure causes pathological changes due to respiratory diseases (such as adenoids hypertrophy in this embodiment), the respiratory airflow signal can well reflect the abnormal changes in the nasal structure, that is, the respiratory airflow signal contains rich dynamic information for characterizing respiratory diseases. However, due to physiological or pathological reasons, the left and right nasal structures of the same subject may be different. This difference may cause the left and right nasal airflow signals of the same subject to show obvious differences, further leading to different diagnostic results for the same respiratory disease. The goal of fusion in this embodiment is to reduce the decrease in diagnostic accuracy caused by the difference in the left and right nasal structures as much as possible, and to improve the sensitivity and adaptability of the respiratory dynamics graph to respiratory diseases-adenoids hypertrophy.

[0048] like Figure 1 As shown, this embodiment discloses an auxiliary detection system for adenoids hypertrophy based on kinetic image fusion, including:

[0049] The preprocessing module is used to obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing.

[0050] In this embodiment, the respiratory airflow velocity signals of the left and right nasal cavities of the tester are obtained, and the obtained respiratory airflow velocity signals of the left and right nasal cavities are filtered by wavelet filtering, and then the filtered respiratory airflow velocity signals of the left and right nasal cavities are normalized. Among them, it can be considered to use a nasal resistance meter to collect the respiratory airflow velocity signals of the left and right nasal cavities of the tester.

[0051] The respiratory dynamics diagram module is used to construct discrete-time nonlinear systems based on the preprocessed respiratory airflow velocity signals in the left and right nasal cavities. Based on the deterministic learning theory, the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using the RBF neural network. The modeled nonlinear dynamics data and the corresponding nasal airflow velocity data are combined to form the respiratory dynamics trajectory to obtain the left and right nasal respiratory dynamics diagrams.

[0052] In this embodiment, if Figure 2 As shown in the figure, a discrete-time nonlinear system is constructed based on the preprocessed respiratory airflow velocity signals in the left and right nasal cavities. First, the respiratory system is modeled as the following nonlinear dynamic form:

[0053]

[0054] Where x1 is the respiratory airflow velocity signal, x2 is the rate of change of the respiratory airflow velocity signal, f(x; p s ) is the nonlinear dynamics of the respiratory system, p s is the system parameter vector, x=[x1,x2] T represents the state of the nonlinear system, the superscript T represents the transpose, and y is the system output, which is the breathing airflow velocity signal of the tester measured by the nasal resistance meter.

[0055] The nonlinear dynamics of formula (1) is subjected to Euler discretization to obtain a discrete-time nonlinear system:

[0056]

[0057] Among them, T s is the sampling period, i.e. the inverse of the sampling frequency, k is the kth sampling moment, x1(x) is the sampling signal of the respiratory airflow velocity signal, x2(k) is the sampling signal of the change rate of the respiratory airflow velocity signal, x(k) = [x1(k), x2(k)] T represents the state of the discrete-time nonlinear system, and y(k) is the sampling signal of the tester's respiratory airflow velocity signal measured by the nasal resistance meter.

[0058] Since x2(k) cannot be collected by the nasal resistance meter, a high-gain observer is used to obtain it. The high-gain observer calculates the respiratory system state x(k) = [x1(k), x2(k)] T Estimate and output This output will constitute the regression trajectory of the respiratory system state in phase space; is the estimated signal obtained by observing the respiratory airflow velocity signal through a high gain observer, It is an estimated signal obtained by observing the rate of change of the respiratory airflow velocity signal through a high gain observer.

[0059] The unknown nonlinear dynamics of discrete-time nonlinear systems f(x(k); p s ), using RBF neural network for local accurate modeling.

[0060] First, the regression trajectory of the respiratory system state Input into the RBF neural network, and then use the identifier and network weight update rate in the determined learning mechanism to obtain f(x(k); p s ) is accurately modeled as follows:

[0061]

[0062] in, is a constant vector representing the network weights, represents the RBF regression subvector, N represents the number of neurons in the RBF neural network, ∈ * Modeling error for a sufficiently small network.

[0063] In this embodiment, the respiratory dynamics trajectory refers to The three-dimensional space curve composed of the respiratory dynamics trajectory is visualized to obtain the respiratory dynamics diagram.

[0064] The respiratory dynamics graph fusion module is used to extract the nonlinear dynamics data from the left and right nasal respiratory dynamics graphs of the tester, namely, the constant RBF neural network modeling, construct a weight matrix according to the intensity of the activation of the RBF neural network neurons corresponding to the left and right nasal cavities, and use the weight matrix to fuse the nonlinear dynamics data extracted from the left and right nasal respiratory dynamics graphs, namely, the constant RBF neural network, to obtain the fused respiratory dynamics graph.

[0065] In this embodiment, the nonlinear dynamics data of the left and right nasal respiratory dynamics diagrams are extracted, that is, the constant RBF neural network model is constructed, that is, the respiratory dynamics trajectory of the left nose is extracted from the left and right nasal respiratory dynamics diagrams respectively. and respiratory dynamics of the right nose The subscripts L and R represent left and right, respectively. Because a test subject actually needs to collect two respiratory airflow signals from the left and right nostrils respectively, and considering that the left and right nasal cavities of the test subject may have physiological or pathological differences, the subscripts L and R need to be introduced to distinguish them.

[0066] The weight matrix is ​​constructed based on the intensity of RBF neural network neuron activation, specifically:

[0067] First, the RBF regression matrices in the left and right nasal respiratory dynamics trajectories are concatenated to obtain

[0068]

[0069] 1≤k≤M, k is the kth data point. and Similarly, M and M′ refer to the data length of the collected left and right nose breathing airflow signals, respectively. To facilitate the understanding of the data length, assume that the sampling period T s = 0.01s to sample the left (or right) nasal breathing airflow signal of a tester, and the sampling time is 10s. Then the sampling data length of the obtained breathing airflow signal is M or M ′ =1000.

[0070] Next, calculate Take its diagonal elements to construct a diagonal matrix Get the weight matrix Its i-th diagonal element is Characterizes the intensity of activation of the i-th neuron in the RBF neural network, and s(x) is a Gaussian function.

[0071] Secondly, consider the respiratory dynamics after fusion as f F (x(k); p F ), which can be modeled by the RBF neural network as follows:

[0072]

[0073] Here the p F is the respiratory dynamics after fusion F (x(k); p F ) parameter vector, x(k) belongs to f F (x(k); p F ) has no specific meaning. The value range of x(k) is very wide. Since only the respiratory airflow signals of the left and right nasal cavities of a test subject are collected, x(k) is replaced by in the following derivation. and Indicates that the RBF network is used to approximate fF (x(k); p F ) is the weight of the neuron, and N represents the number of neurons in the RBF network.

[0074] The dynamic distance D between the fused respiratory dynamics and the respiratory dynamics of the left and right noses is as follows:

[0075]

[0076] Among them, ‖·‖ refers to the Euclidean distance norm, SxLk∈RN represents the RBF regression subvector corresponding to the left nasal cavity, represents the RBF regression subvector corresponding to the right nasal cavity, The constant vector corresponding to the left nasal cavity represents the network weight, The constant vector corresponding to the right nasal cavity represents the network weights.

[0077] Using the idea of ​​weighted least squares algorithm, the constructed weight matrix P is introduced into D′ to obtain:

[0078]

[0079] in,

[0080] Introducing the regularization measure into D′ yields:

[0081]

[0082] Among them, λ L and λ R are all regularization coefficients and are greater than 0.

[0083] Use the least squares algorithm to calculate the optimal solution that minimizes D′ get:

[0084]

[0085] Among them, the superscript + represents the pseudo-inverse of the matrix, and the regularization coefficient λ L ,λ R The purpose is to limit the solution The solution range makes the solution process more stable.

[0086] So far, the respiratory dynamics of fusion have been obtained as

[0087] First, the respiratory dynamics after fusion Maintains breathing dynamics with both noses Because the fusion process is based on the respiratory dynamics of the left and right noses, the fusion result cannot have too much dynamic difference with the respiratory dynamics of the left and right noses. In addition, due to the weighting of the weight matrix P, the respiratory dynamics It includes the respiratory dynamics of both left and right noses that reflect the pathological respiratory characteristics of adenoid hypertrophy.

[0088] The output module is used to obtain auxiliary detection results based on the morphology of the fused respiratory dynamics diagram and the basic information of the tester.

[0089] Among them, the basic information of the tester includes age, medical history and symptoms.

[0090] According to the morphology of the fused respiratory dynamics graph, it refers to observing whether the respiratory dynamics graph of the subject is symmetrical and regular or asymmetrical and scattered; when the respiratory dynamics graph presents a symmetrical and regular butterfly-shaped graph, it means that the current subject is healthy; when the respiratory dynamics graph presents an asymmetrical and scattered graph, it means that the current subject has adenoid hypertrophy.

[0091] according to Figure 3-Figure 6 It can be seen that the respiratory dynamics of the left and right noses of the test subject before fusion are very different. The respiratory dynamics of the right nose presents a clear asymmetric and scattered shape, which is a characteristic of adenoid hypertrophy, but the respiratory dynamics of the left nose presents a nearly regular symmetrical butterfly shape. After the fusion operation, the respiratory dynamics of the left and right noses both present a clear asymmetric and scattered shape.

[0092] The present invention uses deterministic learning to accurately model the unknown respiratory dynamics contained in the respiratory airflow signal of the subject, and the respiratory dynamics diagram obtained by modeling is used to diagnose the respiratory disease-adenoids hypertrophy. In the disease diagnosis of adenoids hypertrophy, the respiratory dynamics diagram of healthy subjects is a symmetrical and regular butterfly shape, while the respiratory dynamics diagram of adenoids hypertrophy patients is an asymmetrical and scattered shape. However, for subjects with physiological or pathological differences in the left and right noses, there will be obvious differences in the respiratory dynamics diagrams of the left and right noses. This difference will seriously affect the accuracy of subsequent diagnosis using the respiratory dynamics diagram, and may cause misdiagnosis or missed diagnosis of the subject. The fusion method proposed in the present invention extracts the pathological respiratory features that reflect adenoids hypertrophy in the left and right nasal respiratory dynamics diagrams, so that the present invention has good adaptability to adenoids hypertrophy and can avoid the influence of the left and right nose differences on the accuracy of disease diagnosis. In addition, the construction of the weight matrix in the fusion method is not unique and fixed, and it can change according to different respiratory diseases, so that the proposed fusion method has a wider range of application. In summary, the present invention is non-invasive, radiation-free, easy to operate and accurate, and is particularly suitable for dynamic evaluation of adenoid hypertrophy, and can provide accurate and objective diagnostic results and reports to medical professionals.

[0093] Embodiment 2

[0094] The purpose of this embodiment is to provide a method for assisting detection of adenoids hypertrophy based on kinetic image fusion, including:

[0095] Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing;

[0096] According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained;

[0097] Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph;

[0098] According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

[0099] In further embodiments, there is also provided:

[0100] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0101] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0102] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0103] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0104] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0105] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0106] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0107] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0108] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0109] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A system for assisting detection of adenoid hypertrophy based on kinetic image fusion, characterized in that: The system comprises: A preprocessing module is used to obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing; A respiratory dynamics diagram module is used to construct a discrete-time nonlinear system according to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, respectively, and to locally model the unknown nonlinear dynamics of the discrete-time nonlinear system using an RBF neural network based on deterministic learning theory, and to combine the modeled nonlinear dynamics data and the corresponding nasal airflow velocity data to form a respiratory dynamics trajectory, thereby obtaining left and right nasal respiratory dynamics diagrams; A respiratory dynamics graph fusion module is used to extract nonlinear dynamics data from the left and right nasal respiratory dynamics graphs of the tester, respectively, construct a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and use the weight matrix to fuse the nonlinear dynamics data extracted from the left and right nasal respiratory dynamics graphs to obtain a fused respiratory dynamics graph; The output module is used to obtain the auxiliary detection result of adenoids hypertrophy according to the morphology of the fused respiratory dynamics diagram.

2. The adenoids hypertrophy auxiliary detection system based on kinetic image fusion according to claim 1, characterized in that: The respiratory dynamics diagram module includes: A modeling unit, for inputting the regression trajectory of the respiratory system state into a constant RBF neural network, and locally modeling the unknown nonlinear dynamics of the discrete-time nonlinear system by using the identifier and the network weight update rate in the determined learning mechanism; The visualization unit is used to visualize the respiratory dynamics trajectory composed of the modeled nonlinear dynamics data and the corresponding nasal airflow velocity data to obtain a respiratory dynamics diagram.

3. The adenoids hypertrophy auxiliary detection system based on kinetic image fusion according to claim 1, characterized in that: The respiratory dynamics graph fusion module comprises: A construction unit, used for concatenating the RBF regression matrices in the left and right nasal respiratory dynamics trajectories, and constructing a weight matrix according to the concatenated matrices; A calculation unit, used to construct a dynamic distance between the fused breathing dynamics and the left and right nasal breathing dynamics; The solving unit is used to introduce the weight matrix by using the weighted least squares algorithm, solve the problem with the minimum dynamic distance as the goal, and obtain a fused respiratory dynamics diagram.

4. The adenoids hypertrophy auxiliary detection system based on dynamic graph fusion as claimed in claim 3, characterized in that: The construction unit comprises: A splicing subunit, used to splice the RBF regression matrices in the left and right nasal respiratory dynamics trajectories to obtain a splicing matrix; A calculation subunit, used for calculating the product of the splicing matrix and the transposed matrix of the splicing matrix to obtain a product matrix; The construction subunit is used to extract the diagonal elements of the product matrix to construct a diagonal matrix to obtain a weight matrix.

5. The adenoids hypertrophy auxiliary detection system based on dynamic graph fusion as claimed in claim 3, characterized in that: The solving unit comprises: An introducing subunit is used to introduce the weight matrix and regularization into the dynamic distance using a weighted least squares algorithm; The solving subunit is used to solve the problem by using the least squares algorithm with the minimum dynamic distance as the goal, so as to obtain a fused respiratory dynamics diagram.

6. The adenoids hypertrophy auxiliary detection system based on kinetic image fusion as claimed in claim 1, characterized in that: The preprocessing module comprises: The filter subunit is used to process the acquired respiratory airflow velocity signals in the left and right nasal cavities of the tester by using wavelet filtering; The normalization subunit is used to normalize the filtered respiratory airflow velocity signal.

7. The adenoids hypertrophy auxiliary detection system based on kinetic image fusion according to claim 1, characterized in that: The output module includes: The first output subunit is used to present a symmetrical and regular butterfly-shaped graph according to the fused respiratory dynamics graph, indicating that the current test subject is healthy; The second output subunit is used to indicate that the current test subject is a patient with adenoid hypertrophy when asymmetric dispersion is presented according to the fused respiratory dynamics graph.

8. A method for auxiliary detection of adenoids hypertrophy based on kinetic image fusion, characterized in that: The method comprises: Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing; According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained; Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph; According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the following steps: Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing; According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained; Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph; According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

10. An electronic device, characterized in that: The system comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions are executed by the processor to implement the following steps: Obtain the respiratory airflow velocity signals in the left and right nasal cavities of the tester and perform preprocessing; According to the preprocessed respiratory airflow velocity signals in the left and right nasal cavities, discrete-time nonlinear systems are respectively constructed, and the unknown nonlinear dynamics of the discrete-time nonlinear system are locally modeled using an RBF neural network based on deterministic learning theory. The modeled nonlinear dynamic data and the corresponding nasal airflow velocity data are combined to form a respiratory dynamics trajectory, and the left and right nasal respiratory dynamics diagrams are obtained; Extracting nonlinear dynamic data from the left and right nasal respiratory dynamics graphs of the tester respectively, constructing a weight matrix according to the intensity of activation of RBF neural network neurons corresponding to the left and right nasal cavities, and fusing the nonlinear dynamic data extracted from the left and right nasal respiratory dynamics graphs using the weight matrix to obtain a fused respiratory dynamics graph; According to the morphology of the fused respiratory dynamics graph, the auxiliary detection result of adenoids hypertrophy is obtained.

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