A method and system for constructing a rat secretory otitis media model

Abstract

This invention belongs to the field of medical information technology and relates to a method and system for constructing a rat model of secretory otitis media. Specifically, it includes: performing modeling treatment on experimental rats and collecting initial tympanic membrane response data to construct an initial middle ear state; applying controlled perturbation to the middle ear and simultaneously collecting response data during the perturbation process, extracting dynamic response features and identifying feature points to obtain the corresponding perturbation intensity; constructing response trajectories during loading and recovery based on the perturbation intensity and extracting trajectory difference features; combining perturbation data from different directions to extract directional response features and directional response difference features; fusing the perturbation intensity, trajectory difference features, and directional response difference features to construct a middle ear fluid state determination model and calculate middle ear fluid state parameters; and screening experimental rats to determine individuals where the model construction is complete. This invention also discloses a corresponding system. This invention can achieve stability and reproducibility in constructing a secretory otitis media model.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and in particular to a method and system for constructing a rat secretory otitis media model. Background Technology

[0002] Rat secretory otitis media models are important tools for studying the pathological evolution of middle ear effusion and evaluating the efficacy of clinical drugs. In existing techniques, common methods for inducing middle ear effusion in rats include: injecting endotoxins, inflammatory factors, or pathogens into the middle ear cavity to stimulate inflammation and exudation in the superficial mucosa; directly infusing the tympanic cavity with a fluid of a specific viscosity using micropipette devices to simulate an effusion environment; and surgically ligating, mechanically obstructing, or physically embolizing the Eustachian tubes to artificially block the self-cleaning and drainage functions of the middle ear. After completing these induction procedures, the conventional experimental judgment logic is based on static observation at specific time points after model establishment. It is generally considered that as long as effusion is detected in otoscopy or anatomical observation at the preset time points, the model is considered successfully established, and this serves as the starting point for subsequent drug efficacy evaluation or pathological studies.

[0003] Traditional methods of assessment only focus on the physical existence of the fluid, but fail to confirm whether the fluid is in a stable pathological state of the middle ear. This leads to easily drainable transient effusions and persistent steady-state effusions being classified into the same model, making it difficult to guarantee the stability and reproducibility of the model construction. Summary of the Invention

[0004] To overcome the above deficiencies, the present invention provides a method and system for constructing a rat secretory otitis media model, aiming to improve the problem that the failure to confirm whether the fluid is in a stable and maintainable middle ear pathological state leads to the confusion between easily drainable transient effusion and persistently retained steady-state effusion being classified as the same type of model.

[0005] According to a first aspect of the present invention, a method for constructing a rat secretory otitis media model is provided, comprising the following steps: S1 was used to establish a model in experimental rats, and initial tympanic membrane response data were collected after modeling to construct initial middle ear state data; Based on the initial middle ear state data, S2 applies controlled perturbation to the middle ear and simultaneously collects tympanic membrane response data during the perturbation process. It extracts the dynamic response characteristics of the tympanic membrane response as it changes with the perturbation, and identifies the feature points from the dynamic response characteristics where the middle ear fluid changes from a confined state to a released state, thus obtaining the corresponding perturbation intensity. S3 constructs a trajectory based on the disturbance intensity of the tympanic membrane's response data during the loading and recovery processes to obtain the response trajectory, and extracts the trajectory difference features between the loading path and the recovery path from the response trajectory; Based on the response trajectory, S4 combines tympanic membrane response data under different directional disturbances to extract directional response features, and extracts directional response difference features between different directional responses from the directional response features. S5 fuses disturbance intensity, trajectory difference characteristics, and directional response difference characteristics to construct a middle ear fluid state determination model, and calculates middle ear fluid state parameters based on the middle ear fluid state determination model. S6 used middle ear fluid state parameters to screen experimental rats and identify individuals whose secretory otitis media model had been successfully established.

[0006] Preferably, in S1, the initial tympanic membrane response data acquired after modeling includes: After the modeling process is completed, S101 applies an initial excitation signal to the tympanic membrane and simultaneously acquires the corresponding tympanic membrane response signal. S102 preprocesses the tympanic membrane response signal, the preprocessing including noise reduction and baseline correction; S103 extracts characteristic parameters representing the vibration state of the tympanic membrane based on the preprocessed tympanic membrane response signal; S104 performs normalization processing on the feature parameters and constructs the initial middle ear state data based on the processed feature parameters.

[0007] Preferably, in S2, the controlled perturbation applied to the middle ear and the simultaneous acquisition of tympanic membrane response data during the perturbation process includes: S201 generates a disturbance control signal according to a preset change rule, and applies a continuously changing disturbance to the middle ear based on the disturbance control signal; During the process of applying the disturbance, S202 synchronously samples the tympanic membrane response signal to obtain the tympanic membrane response data sequence corresponding to the disturbance change; S203 performs time-series alignment processing on the tympanic membrane response data based on the correspondence between the tympanic membrane response data sequence and the disturbance control signal; S204 segments the aligned tympanic membrane response data to obtain multiple response data segments corresponding to the perturbation change intervals. S205 constructs dynamic response characteristics of the tympanic membrane response as a function of perturbation based on response data fragments.

[0008] Preferably, in S2, the feature points identifying the transition of middle ear fluid from a confined state to a released state from the dynamic response characteristics include: S211 Based on the dynamic response characteristics, a characteristic curve of the tympanic membrane response changing with disturbance is constructed; S212 performs local variation analysis on the characteristic curve and extracts the response change trend within different disturbance intervals; Based on the response change trend, S213 determines the candidate interval where the tympanic membrane response changes from continuous to discontinuous. S214 performs a detailed analysis of the response data within the candidate interval to determine the location where the response change occurs abruptly as the state transition point; S215 defines the perturbation location corresponding to the state transition point as the characteristic point where the middle ear fluid changes from a confined state to a released state.

[0009] Preferably, in S3, the trajectory construction of the tympanic membrane response data during the loading and recovery processes includes: S301 acquires the tympanic membrane response data sequence during the loading and recovery processes, and sorts the tympanic membrane response data sequence according to the order of perturbation changes; Based on the tympanic membrane response data sequence, S302 establishes the correspondence between disturbance intensity and tympanic membrane response, and maps each set of disturbance intensity and corresponding tympanic membrane response data into trajectory points; S303 connects the trajectory points according to the direction of disturbance change to form the loading trajectory and the recovery trajectory; S304 uses a unified coordinate representation for the loading trajectory and the recovery trajectory to obtain the response trajectory.

[0010] Preferably, in S3, the extraction of trajectory difference features between the loading path and the recovery path from the response trajectory includes: S311 acquires the loading trajectory and the recovery trajectory respectively, and performs unified parameterization processing on the loading trajectory and the recovery trajectory to establish a corresponding relationship between the two within the same disturbance range; S312 performs path matching between the loaded trajectory and the restored trajectory based on the correspondence relationship to obtain trajectory point pairs at corresponding locations; S313 calculates the path offset between the loaded trajectory and the restored trajectory based on trajectory point pairs; S314 performs cumulative processing or distribution statistical processing on the path offset to obtain trajectory difference characteristics.

[0011] Preferably, in S4, extracting the directional response difference features between different directional responses from the directional response features includes: S401 acquires tympanic membrane response data under disturbance in different directions, and classifies the tympanic membrane response data according to the disturbance direction to form a subset of response data corresponding to each direction; S402 constructs response change curves corresponding to each direction based on subsets of response data from each direction; S403 performs unified parameterization on the response change curves in different directions, so that the response change curves in different directions establish a corresponding relationship within the same disturbance range. S404 performs path matching on response change curves in different directions based on correspondence to obtain response data pairs at corresponding locations; S405 calculates the path offset between responses in different directions based on response data pairs, and performs cumulative processing or distribution statistical processing on the path offset to obtain the directional response difference characteristics.

[0012] Preferably, in S5, the construction of the middle ear fluid state determination model includes: S501 acquires the disturbance intensity, the trajectory difference features, and the direction response difference features, and performs unified quantization processing on each feature to form a multi-dimensional feature parameter set; S502 establishes the correlation between disturbance intensity and trajectory difference characteristics, as well as the constraint relationship between trajectory difference characteristics and directional response difference characteristics, based on the multidimensional feature parameter set; S503 Based on the aforementioned association and constraint relationships, a set of decision rules is constructed; S504 The set of determination rules constitutes a middle ear fluid state determination model.

[0013] Preferably, in S6, the screening of experimental rats includes: S601 acquires the disturbance intensity parameter, trajectory difference parameter, and directional response difference parameter from the middle ear fluid state parameters, and performs unified expression processing on each parameter to form a set of state parameters; S602 establishes the constraint relationship between the disturbance intensity parameter and the trajectory difference parameter, as well as the correlation relationship between the trajectory difference parameter and the directional response difference parameter, based on the set of state parameters; S603 performs consistency processing on the set of state parameters based on the constraint relationship and the association relationship, and generates a state determination structure; S604 uses a state-determination structure to screen experimental rats and identify individuals whose secretory otitis media model has been successfully established.

[0014] According to a second aspect of the present invention, a rat secretory otitis media model construction system is provided, the system comprising: The modeling and initial data construction module is used to perform modeling treatment on experimental rats and collect initial tympanic membrane response data after modeling to construct initial middle ear state data. The dynamic response feature extraction module is used to apply controlled perturbation to the middle ear based on the initial middle ear state data and simultaneously collect tympanic membrane response data during the perturbation process. It extracts the dynamic response features of the tympanic membrane response as it changes with the perturbation, and identifies the feature points of the middle ear fluid transitioning from a confined state to a released state from the dynamic response features, thereby obtaining the corresponding perturbation intensity. The response trajectory construction module is used to construct a trajectory of the tympanic membrane's response data during the loading and recovery processes based on the disturbance intensity, obtain the response trajectory, and extract the trajectory difference features between the loading path and the recovery path from the response trajectory. The directional response difference extraction module is used to extract directional response features based on the response trajectory and combined with tympanic membrane response data under different directional disturbances, and to extract directional response difference features between different directional responses from the directional response features; The middle ear fluid state determination module is used to fuse disturbance intensity, trajectory difference characteristics and directional response difference characteristics to construct a middle ear fluid state determination model, and calculate middle ear fluid state parameters based on the middle ear fluid state determination model. The screening module is used to screen experimental rats based on middle ear fluid state parameters to identify individuals whose secretory otitis media model has been successfully established.

[0015] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: (1) In this invention, by introducing a discharge trigger threshold to apply controlled perturbation to the middle ear, the quantitative calculation and determination of the middle ear effusion retention capacity and discharge difficulty are realized, which solves the problem that the existing technology only uses the appearance of effusion as the basis for model construction and cannot confirm whether it is in a stable pathological state, resulting in poor model repeatability.

[0016] (2) In this invention, by introducing hysteresis memory state analysis to analyze the tympanic membrane response trajectory under reciprocating perturbation, the deep pathological coupling characteristics between middle ear fluid and local tissue structure are accurately confirmed, which solves the problem that the fluid accumulation state in the traditional scheme only shows a simple instantaneous response and is easily disturbed by external conditions, making it difficult to maintain stable pathological characteristics for a long time.

[0017] (3) In this invention, by introducing a bridging latch state to compare the response symmetry under different directional disturbances, the abnormal retention state caused by local adhesion or mechanical latching is effectively identified and eliminated, which solves the problem that the existing technology cannot distinguish between the pathological steady state of standard secretory otitis media and the local mechanical pseudo-steady state, thus leading to the distortion of drug efficacy evaluation data. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for constructing a rat secretory otitis media model proposed in this invention; Figure 2 This is an architectural diagram of a rat secretory otitis media model construction system proposed in this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In a first embodiment of the present invention, a method for constructing a rat secretory otitis media model is provided, such as... Figure 1 As shown, it includes the following steps: S1. Modeling was performed on experimental rats, and initial tympanic membrane response data were collected after modeling to construct initial middle ear state data.

[0021] In this embodiment, the initial tympanic membrane response data collected after modeling includes: After the modeling process is completed, an initial excitation signal is applied to the tympanic membrane, and the corresponding tympanic membrane response signal is acquired simultaneously. The tympanic membrane response signal is preprocessed, including noise reduction and baseline correction. Based on the preprocessed tympanic membrane response signal, feature parameters characterizing the tympanic membrane vibration state are extracted. The feature parameters are normalized, and the initial middle ear state data is constructed based on the processed feature parameters.

[0022] Specifically, the experimental rats were first subjected to a modeling process to artificially induce a fluid-filled environment in the middle ear cavity. The specific modeling methods could be selected based on experimental needs, including inflammatory stimulation induction, middle ear fluid injection, eustachian tube intervention, or a combination of these methods, to simulate the pathological manifestations of clinical secretory otitis media. After the initial modeling was completed, the system did not rely solely on traditional anatomical observation or visual assessment, but instead constructed initial middle ear state data by collecting initial tympanic membrane response data after modeling. This step serves to provide a static, unaffected physical baseline for subsequent perturbation analysis, ensuring that the subsequently calculated discharge trigger thresholds or hysteresis characteristics have a reliable comparative reference.

[0023] In the specific operational process, the acquisition begins with the application of an initial excitation signal to the tympanic membrane. This excitation signal is typically generated by a signal generation unit and can be converted into a pure tone excitation within a specific frequency range or a transient mechanical pulse excitation via an acoustic transducer. After the signal is applied to the external auditory canal of the experimental rat, it drives the tympanic membrane to produce a vibration response modulated by the internal environment of the middle ear cavity. At this time, the system uses high-precision sensors to synchronously acquire the vibration response signal of the tympanic membrane. The sensors can be laser Doppler vibrometers, which acquire displacement or velocity data at the center position of the tympanic membrane surface in a non-contact manner; or they can be miniature pressure sensors, which capture minute changes in sound pressure caused by tympanic membrane movement. The acquired raw response signal is voltage or displacement data containing time-series information, reflecting the mechanical impedance characteristics of the middle ear system at the initial moment.

[0024] Because the experimental environment may contain random vibrations, electromagnetic interference from equipment, or physiological noise caused by individual respiration, directly processing the acquired raw signals can lead to feature recognition bias. Therefore, preprocessing of the response signals is necessary. The first stage of preprocessing is denoising, which is typically achieved using a discrete wavelet transform algorithm. Specifically, the response signal is decomposed into wavelet coefficients in different frequency bands. Since effective signals in the wavelet domain usually exhibit a sparse distribution with large amplitudes, while noise is uniformly distributed among small coefficients, the system shrinks the coefficients according to a preset threshold function, thereby filtering out high-frequency noise while preserving signal waveform details.

[0025] In addition to denoising, baseline correction is required to eliminate signal center axis offset caused by sensor temperature drift or long-range respiratory motion. The correction logic employs a polynomial fitting algorithm. First, a low-frequency baseline curve representing the drift trend is fitted into the signal's time series. Then, this baseline term is subtracted from the original denoised signal. Baseline correction ensures that the amplitude of the response signal consistently fluctuates around the zero mean, providing a stable computational basis for subsequent extraction of vibration amplitude parameters.

[0026] After preprocessing, the system extracts characteristic parameters representing the tympanic membrane vibration state based on the high-quality response signal. These characteristic parameters are used to quantitatively characterize the initial suppression of middle ear conduction function by the effusion after modeling. One core parameter is the tympanic membrane vibration energy value. This parameter is obtained by performing a root mean square operation on the response signal sequence. It reflects the mechanical energy loss caused by middle ear effusion under the same excitation intensity. The specific calculation formula is as follows: ; In the formula, This represents the total number of sampling points in the preprocessed signal sequence. Indicates the first The signal amplitude corresponding to each sampling point This refers to the extracted vibration energy characteristics. In addition to energy characteristics, the system can also extract the phase hysteresis of the response signal or the power spectral density at a specific frequency band as supplementary features. These features together constitute a multidimensional parameter space describing the mechanical state of the tympanic membrane. By extracting these features, the system can transform abstract waveform signals into concrete physical scalars.

[0027] The extracted feature parameters often have different physical units and significant differences in magnitude. Furthermore, individual physiological and anatomical differences can also affect the absolute values ​​of the features. To eliminate the influence of dimensions and improve the computational stability of the judgment model, the feature parameters need to be normalized. Normalization employs a range transformation algorithm to map the original feature values ​​to... The standard range between [a certain value] and [a certain value]. The specific implementation logic is as follows: ; In this formula, These are the original extracted feature parameters. This represents the minimum value of the same type of feature in a preset benchmark sample set. This represents the maximum value of the feature in the benchmark sample set. This refers to the dimensionless eigenvalues ​​after normalization.

[0028] Finally, the system constructs initial middle ear state data based on the processed and normalized feature parameters. This process essentially encapsulates multidimensional feature values ​​into a state vector describing the current pathological baseline. This vector records the mechanical equilibrium point of the experimental rats under uncontrolled perturbation and serves as the starting reference for pressure perturbation experiments in subsequent steps. By constructing initial middle ear state data, the system can achieve preliminary quantitative archiving of the modeling effects on different individuals in large-scale modeling.

[0029] S2. Based on the initial middle ear state data, a controlled perturbation is applied to the middle ear and the tympanic membrane response data during the perturbation process is collected simultaneously. The dynamic response characteristics of the tympanic membrane response as a function of the perturbation are extracted, and the characteristic points where the middle ear fluid changes from a confined state to a released state are identified from the dynamic response characteristics to obtain the corresponding perturbation intensity.

[0030] Preferably, applying controlled perturbation to the middle ear and simultaneously acquiring tympanic membrane response data during the perturbation process includes: A perturbation control signal is generated according to a preset change rule, and a continuously changing perturbation is applied to the middle ear based on the perturbation control signal. During the perturbation process, the tympanic membrane response signal is synchronously sampled to obtain a tympanic membrane response data sequence corresponding to the perturbation change. Based on the correspondence between the tympanic membrane response data sequence and the perturbation control signal, the tympanic membrane response data is time-aligned. The aligned tympanic membrane response data is segmented to obtain multiple response data segments corresponding to the perturbation change interval. Based on the response data segments, a dynamic response feature of the tympanic membrane response as a function of the perturbation is constructed.

[0031] Preferably, identifying the feature points in the dynamic response features that indicate the transition of middle ear fluid from a confined state to a released state includes: constructing a feature curve of tympanic membrane response changing with perturbation based on the dynamic response features; performing local change analysis on the feature curve to extract the response change trend within different perturbation intervals; determining candidate intervals where the tympanic membrane response changes from continuous to discontinuous based on the response change trend; performing refined analysis on the response data within the candidate intervals to determine the location where the response change abruptly occurs as the state transition point; and determining the perturbation location corresponding to the state transition point as the feature point of the transition of middle ear fluid from a confined state to a released state.

[0032] Specifically, in this embodiment, after constructing the initial middle ear state data, in order to further verify whether the middle ear effusion has evolved from a temporary physical accumulation to a state of secretory otitis media with stable pathological characteristics, the system applies controlled perturbation to the experimental rats and simultaneously collects response data. The core of this stage is to disrupt the original equilibrium state of the middle ear system through the input of externally controllable energy and observe the dynamic behavior of the middle ear fluid when it is squeezed or pulled.

[0033] In its operation, the system first generates a disturbance control signal according to a preset variation rule. This control signal, acting as a system command, drives the actuator to apply continuously varying physical disturbances to the rat's middle ear. This can be achieved by changing the air pressure in the external auditory canal through a pressure pump, or by generating minute mechanical displacement loads through a precision actuator. This disturbance is not an instantaneous impact, but rather a dynamic process that smoothly increases or changes according to a specific functional law within a preset time range. Its purpose is to detect the liquid discharge trigger threshold under different stress levels.

[0034] During the application of a disturbance, the system synchronously records the tympanic membrane response signal using a high-frequency sampling device, thereby obtaining a tympanic membrane response data sequence corresponding to the disturbance change. Since there is an unavoidable physical delay in the hardware link from the issuance of the command to the action of the actuator, and then to the sensor capturing the feedback, the system needs to perform timing alignment processing based on the correspondence between the response data sequence and the disturbance control signal.

[0035] To achieve accurate time axis matching, the system uses a cross-correlation function to calculate the hysteresis between signals. Let the control signal sequence be... The collected response signal sequence is By calculating the cross-correlation coefficient between the two. To determine the time offset: ; In the above formula, Represents discrete sampling time points. This represents the time-delayed displacement. The system searches for... When the maximum value is reached This serves as the alignment reference. The entire response signal is translated... This ensures that each perturbation energy level and its corresponding tympanic membrane response are strictly locked in the time dimension, laying the foundation for subsequent extraction of dynamic features.

[0036] The aligned tympanic membrane response data then enters the segmentation processing stage. Based on a step rule for perturbation intensity, the system divides the continuous signal stream into multiple response data segments corresponding to perturbation variation intervals. Based on these segmented data, the system constructs dynamic response characteristics of the tympanic membrane as a function of perturbation. These characteristics not only include instantaneous amplitude changes but also reflect the energy dissipation of the middle ear system under different pressure loads.

[0037] When identifying state transition points, the system first constructs a characteristic curve based on dynamic response features, with perturbation intensity on the x-axis and tympanic membrane response parameters on the y-axis. Subsequently, the system performs local change analysis on the characteristic curve, extracting the response change trend within different perturbation intervals using a sliding window algorithm. Under normal physiological conditions, this trend typically manifests as linear elastic deformation; however, for the middle ear system with fluid accumulation, the viscous resistance of the fluid in narrow spaces such as the Eustachian tube or tympanic recess causes the curve to exhibit nonlinear characteristics in specific regions.

[0038] Based on the response change trend, the system further identifies candidate intervals where the tympanic membrane response transitions from continuous to discontinuous change. Within these intervals, the middle ear fluid is at a critical point where it is transitioning from a confined state to a released state. To precisely locate this abrupt change, the system performs a refined analysis of the response data within the candidate intervals, using a first-order difference operator to calculate the abrupt change intensity of the eigenvalues. : ; In this formula, Representing the The dynamic response characteristic value of each sampling point This represents the increment in the intensity of the disturbance. When When the preset pathological mutation threshold is exceeded, the location is identified as a state transition point.

[0039] By mapping the perturbation location corresponding to the state transition point back to the original control parameter space, the characteristic point at which the middle ear fluid transitions from a confined state to a released state can be determined, and the corresponding perturbation intensity can be obtained. This perturbation intensity is the key parameter for determining whether the model has been successfully constructed—the discharge trigger threshold. If this threshold reaches the preset stability requirement, it indicates that the middle ear fluid has acquired the pathological property of being difficult to discharge spontaneously, confirming the effectiveness and repeatability of the model construction.

[0040] S3. Based on the disturbance intensity, the response data of the tympanic membrane during the loading and recovery processes are used to construct a trajectory to obtain the response trajectory, and the trajectory difference features between the loading path and the recovery path are extracted from the response trajectory.

[0041] Specifically, in S3, constructing the trajectory of the tympanic membrane's response data during the loading and recovery processes can include: Acquire the tympanic membrane response data sequence during the loading and recovery processes, and sort the tympanic membrane response data sequence according to the order of perturbation change; based on the tympanic membrane response data sequence, establish the correspondence between perturbation intensity and tympanic membrane response, and map each set of perturbation intensity and corresponding tympanic membrane response data into trajectory points; connect the trajectory points according to the direction of perturbation change to form the loading trajectory and recovery trajectory respectively; perform unified coordinate representation on the loading trajectory and recovery trajectory to obtain the response trajectory.

[0042] Preferably, extracting trajectory difference features between the loading path and the recovery path from the response trajectory includes: The loading trajectory and the recovery trajectory are acquired separately, and the loading trajectory and the recovery trajectory are uniformly parameterized to establish a correspondence between them within the same disturbance range. Based on the correspondence, the loading trajectory and the recovery trajectory are matched to obtain the trajectory point pairs at the corresponding positions. Based on the trajectory point pairs, the path offset between the loading trajectory and the recovery trajectory is calculated. The path offset is accumulated or distributed statistically processed to obtain the trajectory difference characteristics.

[0043] Specifically, in this embodiment, after determining the trigger threshold for the discharge of middle ear fluid, the system needs to further verify whether the middle ear system has changed from a transient response state to a stable pathological state with historical dependence, that is, to confirm whether a hysteretic memory state has been formed. The physical significance of this step is that if the middle ear fluid is merely a simple accumulation, its constraint on the tympanic membrane should be symmetrical during pressure loading and unloading; however, once the fluid forms a stable pathological coupling with the tympanic membrane and local structures of the middle ear, the system will exhibit obvious response path separation or recovery delay during reciprocating perturbations.

[0044] Based on the disturbance intensity range determined in the preceding steps, the researchers controlled the disturbance source to apply reciprocating micro-perturbation input to the middle ear. This process encompasses a complete loading and recovery phase. The loading phase refers to the process of increasing the disturbance intensity from the initial value to a preset upper limit, while the recovery phase refers to the process of the disturbance intensity falling back from the upper limit to the initial value. During this cycle, the system acquires the tympanic membrane response data sequence in real time during both the loading and recovery phases. To eliminate the impact of data acquisition frequency fluctuations or sudden environmental noise on the timing, the system reorders these data sequences according to the order of disturbance intensity changes, ensuring that the physical logic of the data is consistent with the direction of pressure change.

[0045] The sorted data is spatially mapped using trajectory construction units. The system establishes a bidirectional correspondence between disturbance intensity and tympanic membrane response, defining each set of measured disturbance intensity values ​​and its synchronously acquired tympanic membrane response amplitude values ​​as a multidimensional trajectory point. Subsequently, the system connects these trajectory points in the response space according to the direction of disturbance change, forming loading and recovery trajectories. To ensure comparability between the two trajectories in subsequent calculations, the system expresses the loading and recovery trajectories in a unified coordinate system, generating a complete response trajectory map. This map visually demonstrates the energy dissipation and displacement hysteresis of the middle ear system during force cycles.

[0046] Extracting the differential features between the loading and recovery paths from the response trajectory is the core of quantifying pathological coupling. The system first acquires the aforementioned loading and recovery trajectories and then resamples them using a unified parameterization method. Since the discrete point positions during the acquisition process may not be perfectly aligned on the two paths, the system utilizes a linear interpolation algorithm within the same perturbation interval. Within, with a fixed disturbance increment A new correspondence is established. This process allows the two paths to have comparable response outputs at the same physical coordinate points.

[0047] Based on this correspondence, the system performs path matching between the loading trajectory and the recovery trajectory to obtain trajectory point pairs at corresponding locations. Subsequently, the system calculates the path offset between the trajectory point pairs, which reflects the difference in response between the system's loading and recovery states under the same disturbance level. To more accurately describe this difference, a formula for calculating the path offset is introduced: ; In the formula, Indicates the first The disturbance intensity value at each sampling point and These represent the tympanic membrane response values ​​corresponding to the loaded trajectory and the recovery trajectory under the same disturbance intensity, respectively. That is the calculated path offset.

[0048] After obtaining the path offsets at each point, the system performs either cumulative processing or distribution statistical processing to generate trajectory difference feature values. Cumulative processing is typically achieved by calculating the area enclosed by the closed response curves; this area physically represents the energy lost or hysteresis effect produced by the middle ear system in a perturbation cycle. The cumulative calculation formula is as follows: ; In the formula, This represents the total number of sampling points within the disturbance interval. For the perturbation step size, This refers to the final extracted trajectory difference features, used to quantitatively characterize the significance of delayed memory states. If... If the value is significantly greater than the preset health benchmark, it indicates that the middle ear fluid and local structures have formed a deep, historically dependent, and stable coupling, and the model construction has entered a mature pathological stage.

[0049] Through quantitative analysis of this reciprocating trajectory, the system eliminated transient models that, despite the presence of fluid accumulation, exhibited simple elastic motion in their response, ensuring that the selected individuals possessed the typical physical stability of secretory otitis media. This trajectory-based judgment method provides data support for subsequent exclusion of pseudo-steady states caused by abnormal local adhesions.

[0050] S4. Based on the response trajectory, combine the tympanic membrane response data under different directional disturbances to extract directional response features, and extract the directional response difference features between different directional responses from the directional response features.

[0051] Specifically, extracting the directional response difference features between different directional responses from the directional response features includes: Acquire tympanic membrane response data under disturbances in different directions, and classify the tympanic membrane response data according to the disturbance direction to form response data subsets corresponding to each direction; construct response change curves corresponding to each direction based on the response data subsets in each direction; perform unified parameterization processing on the response change curves in different directions to establish a correspondence between the response change curves in different directions within the same disturbance interval; perform path matching on the response change curves in different directions based on the correspondence to obtain response data pairs at corresponding positions; calculate the path offset between responses in different directions based on the response data pairs, and perform cumulative processing or distribution statistical processing on the path offset to obtain directional response difference characteristics.

[0052] Specifically, based on the established response trajectory, to eliminate abnormal retention states caused by local mucus bridging, mechanical latch-up in narrow areas, or local wall adhesion, the system further introduces directional response characteristic analysis under multi-directional perturbations. The core logic of this step is that the standard pathological homeostasis of secretory otitis media should exhibit relatively continuous and somewhat symmetrical dynamic behavior under physical influences from different directions; while the pseudo-homeostasis formed by mechanical latch-up often exhibits obvious release bias or threshold inconsistency characteristics under perturbations in a specific direction. By identifying these directional response differences, the system can more accurately distinguish the true pathological nature of the model.

[0053] In its operation, the system first acquires tympanic membrane response data under perturbation in different directions. These perturbation directions typically include positive pressure loads inward along the ear canal and negative pressure loads outward. By applying perturbations alternately in both directions, the system captures the feedback of the middle ear system under different force vectors. The acquired raw data is classified according to the perturbation direction, forming corresponding response data subsets for positive and negative perturbations. Subsequently, based on the response data subsets for each direction, the system constructs response change curves for each direction. These curves, with perturbation intensity on the x-axis and tympanic membrane vibration amplitude or displacement on the y-axis, quantitatively describe the system evolution process under different mechanical actions.

[0054] To ensure comparability of response curves in different directions, the system performs unified parameterization on these curves. Since there may be slight differences in the sampling density or hardware response time between forward and reverse perturbations, the system uses a linear interpolation algorithm to remap the curves in different directions to the same perturbation intensity range, thus establishing a precise correspondence. The linear interpolation algorithm constructs a linear function between known discrete data points and calculates the corresponding response estimate based on a set equal-interval perturbation step size, ensuring that both forward and reverse curves have corresponding numerical outputs at any specified perturbation coordinate point. Based on this correspondence, the system performs path matching on the response curves in different directions, extracting forward and reverse response values ​​under the same perturbation intensity as response data pairs.

[0055] After obtaining the response data pairs, the system calculates the path offset between responses in different directions. This offset represents the physical asymmetry exhibited by the middle ear system when the direction of force changes. To quantify the degree of this asymmetry, the system uses a path offset accumulation algorithm to calculate the directional response difference characteristics: ; In the formula, Indicates the first Each parameterized disturbance intensity sampling point This represents the total number of sampling points. This represents the tympanic membrane response value under positive perturbation. This represents the tympanic membrane response value under reverse perturbation. For the perturbation step size, This refers to the calculated directional response difference characteristics.

[0056] The extracted directional response difference features are used to identify bridged latch states. If Within a preset low threshold range, the middle ear fluid behaves consistently under bidirectional perturbation, indicating no obvious local mechanical locking. This individual is likely to be classified as a standard secretory otitis media model. Conversely, if the path offset exhibits a significant cumulative effect or local extreme mutation, it indicates abnormal local adhesion or physical locking within the middle ear. Based on this, the system classifies the experimental rats, identifying and removing animals with obvious bridging locking states as abnormal constructs, or labeling them separately as special chronic retention models.

[0057] By extracting and analyzing the multi-directional response differences described above, this approach successfully extends traditional single-dimensional observation into a quantitative evaluation of spatial symmetry. This not only improves the accuracy of model selection but also ensures the reproducibility of the constructed model from a physical mechanism perspective, avoiding the misdiagnosis of localized mechanical effusion as standard secretory otitis media homeostasis.

[0058] S5. The disturbance intensity, trajectory difference characteristics and directional response difference characteristics are fused and processed to construct a middle ear fluid state determination model, and the middle ear fluid state parameters are calculated based on the middle ear fluid state determination model.

[0059] The process of constructing a middle ear fluid state determination model includes: acquiring the disturbance intensity, the trajectory difference features, and the directional response difference features, and performing unified quantization on each feature to form a multi-dimensional feature parameter set; establishing the correlation between the disturbance intensity and the trajectory difference features, as well as the constraint relationship between the trajectory difference features and the directional response difference features based on the multi-dimensional feature parameter set; constructing a determination rule set based on the correlation and the constraint relationship; and constructing the determination rule set to form the middle ear fluid state determination model.

[0060] Specifically, after measuring and analyzing the multi-dimensional physical characteristics of the middle ear system, the system enters the crucial comprehensive judgment stage, which involves constructing a middle ear fluid state judgment model through fusion processing. This process aims to transform the discrete physical features obtained in the aforementioned steps into standardized pathological indicators that can directly guide rat screening, thereby enabling the model construction to move from inducing effusion to confirming a stable pathological state.

[0061] The system first acquires three core indicators: perturbation intensity, trajectory difference characteristics, and directional response difference characteristics. Perturbation intensity represents the trigger threshold for the expulsion of middle ear fluid, used to confirm the initial retention capacity of the fluid; trajectory difference characteristics reflect the system's hysteresis memory state, used to confirm the stable coupling degree between the fluid and the middle ear structure; and directional response difference characteristics characterize the bridging latch-up state, used to identify pseudo-steady states caused by local physical adhesion. Since these three indicators have different physical dimensions and numerical ranges, the system needs to perform unified quantization processing on each feature, mapping them to a dimensionless standardized space to form a multi-dimensional feature parameter set. .

[0062] Based on the obtained multidimensional feature parameter set, preferably, the system characterizes the actual state of middle ear fluid by establishing complex logical relationships. The system first establishes a correlation between perturbation intensity and trajectory difference characteristics. This relationship is used to verify whether the stability of middle ear fluid is accompanied by deep pathological coupling. In a normal secretory otitis media model, a higher discharge trigger threshold usually corresponds to significant hysteresis characteristics. If the two show a positive correlation, it enhances the credibility of the system being in a stable pathological state. Subsequently, the system further establishes a constraint relationship between trajectory difference characteristics and directional response difference characteristics. This constraint relationship acts as a filter to eliminate abnormal interference. Even if the system exhibits high hysteresis and high trajectory difference characteristics, if the directional response difference characteristics also surge synchronously, it indicates that the hysteresis may originate from asymmetric local mechanical latch-up rather than a standard fluid accumulation steady state.

[0063] Based on the aforementioned relationships and constraints, the system further constructs a set of decision rules. This set of rules is essentially a weighted discrimination logic used to score feature combinations. The set of decision rules constitutes the core algorithm of the middle ear fluid state determination model, which employs a weighted evidence fusion method to reduce the dimensionality of multidimensional data and perform summation. Middle ear fluid state parameters. The calculation logic is as follows: ; In the formula, This represents the perturbation intensity component after quantization. These represent the quantized trajectory difference feature components; This represents the quantized directional response difference feature component. and These are the correlation weight coefficients between disturbance intensity and trajectory difference, reflecting the positive contributions of retention capacity and coupling stability to model success; This is a latching constraint factor, used as a penalty term to reduce the misleading effect of local abnormal adhesion on the final judgment result.

[0064] Finally, the middle ear fluid state determination model outputs the middle ear fluid state parameters. This has become the ultimate standard for measuring the maturity of rat models of secretory otitis media. This parameter no longer merely indicates whether there is fluid accumulation in the middle ear, but rather whether the fluid accumulation in the middle ear has formed a stable pathological state with a high drainage threshold, strong coupling retardation, and exclusion of local mechanical interference. This judgment method based on multi-source data fusion provides an engineered decision-making basis for the precise screening of experimental rats, ensuring the uniformity and reproducibility of subjects in subsequent drug efficacy experiments.

[0065] S6. Based on the middle ear fluid state parameters, the experimental rats were screened to determine the individuals in whom the secretory otitis media model was successfully established.

[0066] Preferably, the screening of experimental rats may include: obtaining the disturbance intensity parameter, trajectory difference parameter, and directional response difference parameter from the middle ear fluid state parameters, and uniformly expressing each parameter to form a state parameter set; based on the state parameter set, establishing the constraint relationship between the disturbance intensity parameter and the trajectory difference parameter, as well as the correlation relationship between the trajectory difference parameter and the directional response difference parameter; performing consistency processing on the state parameter set according to the constraint relationship and the correlation relationship to generate a state determination structure; and screening experimental rats based on the state determination structure to determine individuals whose secretory otitis media model has been successfully constructed.

[0067] Specifically, in the final stage of constructing the rat model of secretory otitis media, it is necessary to conduct a final screening of experimental rats based on the calculated middle ear fluid state parameters to determine the individuals whose secretory otitis media model has been successfully constructed. This process is not a simple threshold determination, but rather a confirmation through multi-dimensional logical coupling that the fluid in the middle ear has evolved from a transient effusion into a mature model with stable pathological characteristics.

[0068] In the specific execution process, the system first acquires the disturbance intensity parameter, trajectory difference parameter, and directional response difference parameter from the middle ear fluid state parameters. These parameters represent the middle ear fluid discharge trigger threshold, system stability, and abnormal adhesion and retention state, respectively. Since these physical quantities have different dimensions and distribution ranges in the initial state, the system performs a unified expression processing on each parameter, converting them into dimensionless standard scores, thus forming a set of state parameters. This unified expression processing is achieved through standard normalization mapping, ensuring that subsequent logical operations are carried out at the same order of magnitude.

[0069] After forming the set of state parameters, the system establishes a constraint relationship between the disturbance intensity parameter and the trajectory difference parameter, and simultaneously establishes a correlation relationship between the trajectory difference parameter and the directional response difference parameter. The core significance of establishing the constraint relationship is to confirm the robustness of the pathological state, that is, when the discharge trigger threshold is high, the system should simultaneously exhibit a significant hysteresis memory state, which means that the fluid has formed a stable pathological coupling with the local structure of the middle ear. Establishing the correlation relationship is to identify abnormal mechanical latch-up states. If the trajectory difference is extremely large but the directional response difference also simultaneously shows extremely high asymmetry, it indicates that the retention may originate from local physical bridging rather than the standard secretory otitis media homeostasis.

[0070] To comprehensively evaluate the complex logical interactions described above, the system performs consistency processing on the set of state parameters based on constraints and relationships, generating a state determination structure. This consistency processing is achieved by calculating a consistency confidence index, aiming to assess whether the characteristics of each dimension collectively point to the same stable pathological conclusion. Specifically, the consistency scoring formula is introduced as follows: ; In this formula, Represents the final consistency score. This represents the quantized perturbation intensity component. This represents the quantized trajectory difference components. This represents the quantized directional response difference component. and The preset weighted sensitivity coefficient is used to balance the contribution of different features to the judgment result; The preset coupling stability benchmark value, The sign function is used to enhance and compensate for stability characteristics across the baseline. The directional response component in the denominator serves as an adjustment factor, effectively suppressing spurious steady-state scores caused by local latch-up.

[0071] The state decision structure generated through the above calculations is a digital model with multi-level decision logic, capable of reflecting the actual evolutionary stages of the rat middle ear environment. Finally, the system automatically selects experimental rats based on the state decision structure. Only when the consistency score... A rat is considered to have successfully constructed a secretory otitis media model only when it exceeds a preset completion threshold and all sub-parameters are within the preset valid logical domain in the state determination structure. This screening method, based on an engineered set of parameters and logical constraints, transforms the model construction completion standard from experience-based time-point observations to quantitative determinations with clear physical meaning. This ensures that the ultimately selected individuals can provide reproducible experimental data for middle ear pathological mechanism research and drug efficacy evaluation.

[0072] In long-term efficacy evaluation experiments for secretory otitis media, it is usually necessary to ensure that the middle ear effusion remains stable for a considerable period after modeling to observe the drug's effect on effusion clearance. However, traditional methods determine the success of the model solely through otoscopy at specific time points after modeling. This leads to the misselection of individuals in a transient state of easy drainage into the experimental group. During subsequent drug administration, the effusion in these individuals often disappears due to spontaneous swallowing or self-healing abilities, rather than as a result of drug action. This results in drastic fluctuations in experimental data, making it impossible to obtain repeatable and comparable efficacy evaluation conclusions.

[0073] To address the aforementioned problems, this invention provides a rat secretory otitis media model construction system, the structure of which is as follows: Figure 2 As shown. The specific implementation process of this system is as follows: The modeling and initial data construction module is used to perform modeling treatment on experimental rats and collect initial tympanic membrane response data after modeling to construct initial middle ear state data. The dynamic response feature extraction module is used to apply controlled perturbation to the middle ear based on the initial middle ear state data and simultaneously collect tympanic membrane response data during the perturbation process. It extracts the dynamic response features of the tympanic membrane response as it changes with the perturbation, and identifies the feature points of the middle ear fluid transitioning from a confined state to a released state from the dynamic response features, thereby obtaining the corresponding perturbation intensity. The response trajectory construction module is used to construct a trajectory of the tympanic membrane's response data during the loading and recovery processes based on the disturbance intensity, obtain the response trajectory, and extract the trajectory difference features between the loading path and the recovery path from the response trajectory. The directional response difference extraction module is used to extract directional response features based on the response trajectory and combined with tympanic membrane response data under different directional disturbances, and to extract directional response difference features between different directional responses from the directional response features; The middle ear fluid state determination module is used to fuse disturbance intensity, trajectory difference characteristics and directional response difference characteristics to construct a middle ear fluid state determination model, and calculate middle ear fluid state parameters based on the middle ear fluid state determination model. The screening module is used to screen experimental rats based on middle ear fluid state parameters to identify individuals whose secretory otitis media model has been successfully established.

[0074] Specifically, the modeling and initial data construction module first performs modeling treatment on experimental rats and collects initial tympanic membrane response data after modeling through acoustic or mechanical stimulation. This module uses a built-in preprocessing algorithm to denoise and correct the baseline of the signals, extracting standardized feature parameters reflecting the basic vibration state of the tympanic membrane, thereby establishing an initial middle ear state profile for each experimental individual. This profile serves as a zero-point baseline for subsequent dynamic perturbation experiments, eliminating interference from individual physiological differences in fluid effusion determination.

[0075] The dynamic response feature extraction module, based on initial middle ear state data, applies continuously varying controlled perturbations to the middle ear via a pressure actuator. The system monitors the evolution of the tympanic membrane response in real time with the perturbation pressure, using time-series alignment to ensure precise correspondence between pressure input and response output on the time axis. The core task of this module is to identify characteristic points where middle ear fluid transitions from a confined to a released state. A first-order differential gradient detection algorithm is used to locate abrupt changes in the response curve, calculating the individual's discharge trigger threshold. In a pharmacodynamic evaluation scenario, this threshold directly reflects the difficulty of spontaneous fluid drainage; a higher threshold indicates that the model is closer to a clinically stable secretory state.

[0076] Based on the aforementioned perturbation intensity range, the response trajectory construction module drives the system to execute a complete loading and recovery loop, recording a bidirectional tympanic membrane response sequence. By mapping trajectory points in different directions to a unified coordinate system, the system constructs a closed response trajectory. This module further extracts the trajectory difference features between the loading and recovery paths to quantify the hysteresis memory effect of the middle ear system. The significance of the hysteresis effect reflects the pathological coupling depth between the effusion and local middle ear structures. For pharmacodynamic experiments, individuals with significant hysteresis characteristics indicate that their pathological state has entered a stable period and the effusion will not disappear due to slight environmental fluctuations.

[0077] The directional response difference extraction module utilizes bidirectional alternating perturbation technology to extract the differential features between responses in different directions. The system calculates the path offset between the forward and reverse response curves within the same perturbation interval by performing path matching. This step functions as a pseudo-steady-state filter, identifying the presence of localized mucus bridging or mechanical latch-up by analyzing the symmetry of the response. If the system detects significant bridging and latch-up characteristics, it indicates that the fluid retention in this individual is not due to a stable pathological mechanism, and the system excludes such individuals from efficacy evaluation experiments.

[0078] The middle ear fluid state determination module fuses the acquired disturbance intensity, trajectory difference features, and directional response difference features. This module constructs a set of determination rules by establishing correlation and constraint logic between various features. The system uses a weighted scoring model to calculate comprehensive middle ear fluid state parameters. : ; In the formula, This is the quantified discharge trigger threshold. For trajectory difference feature values, These are directional response difference characteristic values; For the corresponding contribution weight, This is the penalty factor for exception latching. This parameter... It provides a single and clear quantitative indicator for whether the model is successful or not.

[0079] The screening module performs final screening of experimental rats based on the aforementioned middle ear fluid state parameters. The system generates a final state determination structure by performing consistency processing on the set of state parameters. Only when the state determination structure shows that an individual simultaneously possesses a high discharge threshold, strong pathological coupling, and no abnormal mechanical latch-up is the secretory otitis media model considered successfully constructed. Through this engineered screening system, researchers can obtain experimental animal groups with highly consistent and stable pathological states, thereby ensuring that subsequent observation data on fluid clearance after drug administration depends entirely on drug efficacy, solving the technical challenge of drastic data fluctuations in long-acting drug efficacy evaluation.

[0080] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a rat model of secretory otitis media, characterized by, Includes the following steps: S1. Modeling treatment was performed on experimental rats, and initial tympanic membrane response data were collected after modeling to construct initial middle ear state data; wherein, the modeling treatment to produce middle ear effusion specifically refers to: injecting endotoxins, inflammatory factors or pathogens into the middle ear cavity to stimulate inflammatory exudation of the surface mucosa, or directly perfusing a liquid of a specific viscosity into the tympanic cavity using a micropipette to simulate the effusion environment; Based on the initial middle ear state data, S2 applies controlled perturbation to the middle ear and simultaneously collects tympanic membrane response data during the perturbation process. It extracts the dynamic response characteristics of the tympanic membrane response as it changes with the perturbation, and identifies the feature points from the dynamic response characteristics where the middle ear fluid changes from a confined state to a released state, thus obtaining the corresponding perturbation intensity. S3 constructs a trajectory based on the disturbance intensity of the tympanic membrane's response data during the loading and recovery processes to obtain the response trajectory, and extracts the trajectory difference features between the loading path and the recovery path from the response trajectory; S4, based on the response trajectory, combines the tympanic membrane response data under different directional disturbances to extract directional response features, and extracts the directional response difference features between different directional responses from these directional response features; S5 fuses disturbance intensity, trajectory difference characteristics, and directional response difference characteristics to construct a middle ear fluid state determination model, and calculates middle ear fluid state parameters based on the middle ear fluid state determination model. S6 uses the middle ear fluid state parameters to screen experimental rats and identify individuals whose secretory otitis media model has been successfully constructed.

2. The method according to claim 1, wherein, In step S1, the initial tympanic membrane response data acquired after modeling includes: After the modeling process is completed, S101 applies an initial excitation signal to the tympanic membrane and simultaneously acquires the corresponding tympanic membrane response signal. S102 preprocesses the tympanic membrane response signal, the preprocessing including noise reduction and baseline correction; S103 extracts characteristic parameters representing the vibration state of the tympanic membrane based on the preprocessed tympanic membrane response signal; S104 performs normalization processing on the feature parameters and constructs the initial middle ear state data based on the processed feature parameters.

3. The method of claim 1, wherein the method is characterized by, In step S2, the controlled perturbation applied to the middle ear and the simultaneous acquisition of tympanic membrane response data during the perturbation process includes: S201 generates a disturbance control signal according to a preset change rule, and applies a continuously changing disturbance to the middle ear based on the disturbance control signal; During the process of applying the disturbance, S202 synchronously samples the tympanic membrane response signal to obtain the tympanic membrane response data sequence corresponding to the disturbance change; S203 performs time-series alignment processing on the tympanic membrane response data based on the correspondence between the tympanic membrane response data sequence and the disturbance control signal; S204 segments the aligned tympanic membrane response data to obtain multiple response data segments corresponding to the perturbation change intervals. S205 constructs dynamic response characteristics of the tympanic membrane response as a function of disturbance based on the response data fragment.

4. The method of claim 1, wherein the method is characterized by, In step S2, identifying the feature points from the dynamic response characteristics that indicate the transition of middle ear fluid from a confined state to a released state includes: S211 Based on the dynamic response characteristics, a characteristic curve of the tympanic membrane response changing with disturbance is constructed; S212 performs local variation analysis on the characteristic curve and extracts the response change trend within different disturbance intervals; Based on the response change trend, S213 determines the candidate interval where the tympanic membrane response changes from continuous to discontinuous. S214 performs a detailed analysis of the response data within the candidate interval to determine the location where the response change occurs abruptly as the state transition point; S215 defines the perturbation location corresponding to the state transition point as the characteristic point where the middle ear fluid changes from a confined state to a released state.

5. The method of claim 1, wherein the method is characterized by, In step S3, the trajectory construction of the tympanic membrane response data during the loading and recovery processes includes: S301 acquires the tympanic membrane response data sequence during the loading and recovery processes, and sorts the tympanic membrane response data sequence according to the order of perturbation changes; Based on the tympanic membrane response data sequence, S302 establishes the correspondence between disturbance intensity and tympanic membrane response, and maps each set of disturbance intensity and corresponding tympanic membrane response data into trajectory points; S303 connects the trajectory points according to the direction of disturbance change to form the loading trajectory and the recovery trajectory; S304 uses a unified coordinate representation for the loading trajectory and the recovery trajectory to obtain the response trajectory.

6. The method for constructing a rat secretory otitis media model according to claim 1, characterized in that, In step S3, extracting the trajectory difference features between the loading path and the recovery path from the response trajectory includes: S311 acquires the loading trajectory and the recovery trajectory respectively, and performs unified parameterization processing on the loading trajectory and the recovery trajectory to establish a corresponding relationship between the two within the same disturbance range; S312 performs path matching between the loaded trajectory and the restored trajectory based on the correspondence relationship to obtain trajectory point pairs at corresponding locations; S313 calculates the path offset between the loaded trajectory and the restored trajectory based on trajectory point pairs; S313 performs cumulative processing or distribution statistical processing on the path offset to obtain trajectory difference characteristics.

7. The method of claim 1, wherein the method is characterized by, In step S4, extracting the directional response difference features between different directional responses from the directional response features includes: S401 acquires tympanic membrane response data under disturbance in different directions, and classifies the tympanic membrane response data according to the disturbance direction to form a subset of response data corresponding to each direction; S402 constructs response change curves corresponding to each direction based on subsets of response data from each direction; S403 performs unified parameterization on the response change curves in different directions, so that the response change curves in different directions establish a corresponding relationship within the same disturbance range. S404 performs path matching on response change curves in different directions based on correspondence to obtain response data pairs at corresponding locations; S405 calculates the path offset between responses in different directions based on response data pairs, and performs cumulative processing or distribution statistical processing on the path offset to obtain the directional response difference characteristics.

8. The method of claim 1, wherein the method is characterized by, In step S5, constructing the middle ear fluid state determination model includes: S501 acquires the disturbance intensity, the trajectory difference features, and the direction response difference features, and performs unified quantization processing on each feature to form a multi-dimensional feature parameter set; S502 establishes the correlation between disturbance intensity and trajectory difference characteristics, as well as the constraint relationship between trajectory difference characteristics and directional response difference characteristics, based on the multidimensional feature parameter set; S503 Based on the aforementioned association and constraint relationships, a set of decision rules is constructed; S504 The set of determination rules constitutes a middle ear fluid state determination model.

9. The method of claim 1, wherein the method is characterized by, In step S6, the screening of experimental rats includes: S601 acquires the disturbance intensity parameter, trajectory difference parameter, and directional response difference parameter from the middle ear fluid state parameters, and performs unified expression processing on each parameter to form a set of state parameters; S602 establishes the constraint relationship between the disturbance intensity parameter and the trajectory difference parameter, as well as the correlation relationship between the trajectory difference parameter and the directional response difference parameter, based on the set of state parameters; S603 performs consistency processing on the set of state parameters based on the constraint relationship and the association relationship, and generates a state determination structure; S604 uses a state-determination structure to screen experimental rats and identify individuals whose secretory otitis media model has been successfully established.

10. A system for constructing a rat model of otitis media with effusion, characterized by, A method for constructing a rat secretory otitis media model according to any one of claims 1-9, the system comprising: The modeling and initial data construction module is used to perform modeling treatment on experimental rats and collect initial tympanic membrane response data after modeling to construct initial middle ear state data. The dynamic response feature extraction module is used to apply controlled perturbation to the middle ear based on the initial middle ear state data and simultaneously collect tympanic membrane response data during the perturbation process. It extracts the dynamic response features of the tympanic membrane response as it changes with the perturbation, and identifies the feature points of the middle ear fluid transitioning from a confined state to a released state from the dynamic response features, thereby obtaining the corresponding perturbation intensity. The response trajectory construction module is used to construct a trajectory of the tympanic membrane's response data during the loading and recovery processes based on the disturbance intensity, obtain the response trajectory, and extract the trajectory difference features between the loading path and the recovery path from the response trajectory. The directional response difference extraction module is used to extract directional response features based on the response trajectory and combined with tympanic membrane response data under different directional disturbances, and to extract directional response difference features between different directional responses from the directional response features; The middle ear fluid state determination module is used to fuse disturbance intensity, trajectory difference characteristics, and directional response difference characteristics to construct a middle ear fluid state determination model, and calculate middle ear fluid state parameters based on the model; and The screening module is used to screen experimental rats based on middle ear fluid state parameters to identify individuals whose secretory otitis media model has been successfully established.

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