Intelligent electronic otoscope control system and method based on electrical impedance detection

Abstract

The application discloses an intelligent electronic otoscope control system and method based on electrical impedance detection, which contains an ear canal impedance signal collection unit, a weak signal conditioning unit, a multi-modal data processing unit, an intelligent analysis and decision unit, a man-machine interaction presentation unit and a closed-loop automatic control unit. A low-power multi-frequency impedance sensor array collects ear canal tissue impedance data at different frequencies, which is processed by the weak signal conditioning unit. The multi-modal data processing unit, combined with an edge computing gateway, completes analog-to-digital conversion and preliminary fusion. The intelligent analysis and decision unit uses an optimized impedance detection algorithm and a multi-dimensional heterogeneous data analysis model to deeply analyze the data, judges the ear health condition, and visualizes the results through the man-machine interaction presentation unit. The closed-loop automatic control unit realizes automatic adjustment of the detection process. The system and method can accurately detect ear health and improve the efficiency and accuracy of ear disease diagnosis.

Description

Technical Field

[0001] This invention relates to the field of electronic otoscope control, and more particularly to an intelligent electronic otoscope control system and method based on electrical impedance detection. Background Technology

[0002] With the rising incidence of ear diseases, efficient and accurate ear detection technology has become an urgent need in the medical field. However, traditional otoscopy has significant limitations in practical applications and cannot meet the refined diagnostic requirements of modern medicine for ear diseases.

[0003] On the one hand, traditional otoscopes rely primarily on visual observation by doctors, and their optical structure and lighting conditions severely limit the effectiveness of the examination. The complex and narrow physiological structure of the ear canal makes it difficult for light to penetrate sufficiently, resulting in numerous blind spots deep within the ear. In actual examinations, subtle lesions are easily overlooked because they cannot be clearly visualized, leading to missed diagnoses. Furthermore, due to differences in clinical experience and observation angles, different doctors may have drastically different judgments about the same ear condition, resulting in a lack of objective and unified diagnostic standards, seriously affecting the accuracy and consistency of diagnosis.

[0004] On the other hand, traditional otoscopes can only perform morphological observations and cannot obtain information on the electrical properties of ear canal tissues. The electrical impedance data of ear canal tissues contains rich physiological and pathological information, which is crucial for the early diagnosis of ear diseases. However, traditional otoscopes lack the ability to detect these parameters. In the early stages of disease, when there are no obvious morphological changes visible to the naked eye, potential lesions cannot be detected in time. They are only noticed when the disease has progressed to a certain stage and significant morphological abnormalities appear, often missing the optimal treatment window and greatly limiting the timeliness and effectiveness of ear disease diagnosis and treatment. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides an intelligent electronic otoscope control system and method based on electrical impedance detection.

[0006] The technical solution adopted in this invention is an intelligent electronic otoscope control system based on electrical impedance detection, comprising:

[0007] The ear canal impedance signal acquisition unit includes a multi-frequency impedance sensor array, used to acquire impedance data of ear canal tissue at different frequencies.

[0008] The weak signal conditioning unit includes a preamplifier, a bandpass filter and a programmable gain amplifier connected in sequence. It receives data from the ear canal impedance signal acquisition unit and performs signal amplification and filtering.

[0009] The multimodal data processing unit, which integrates an edge computing gateway, is used to receive the processed data from the weak signal conditioning unit, perform analog-to-digital conversion, and perform preliminary data fusion processing.

[0010] The intelligent analysis and decision-making unit, equipped with an optimized impedance detection algorithm, receives the fused data from the multimodal data processing unit and performs further in-depth analysis of the data based on the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway.

[0011] The human-computer interaction presentation unit is used to receive the analysis results from the intelligent analysis and decision-making unit. The human-computer interaction presentation unit includes a touch screen and an indicator light group to visually present the ear health status detection results.

[0012] The closed-loop automatic control unit is connected to the ear canal impedance signal acquisition unit, weak signal conditioning unit, multimodal data processing unit, intelligent analysis and decision-making unit, and human-computer interaction presentation unit, respectively. Based on the analysis results of the intelligent analysis and decision-making unit, the operating parameters of different units are adjusted.

[0013] Furthermore, the optimized impedance detection algorithm processes ear canal impedance data using the following model formula:

[0014]

[0015] in, Indicates the effective ear canal impedance value; For the first The second acquisition of ear canal tissue impedance data. For the first The weighting coefficients corresponding to the data collected each time; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway performs fusion analysis on impedance data at different frequencies, and the formula is:

[0016]

[0017] in, The results of the data analysis after fusion; This indicates the results of the low-frequency ear canal impedance data analysis; This represents the analysis results of high-frequency ear canal impedance data; Let be the fusion coefficient, and The The detection frequency range of the electronic otoscope probe and the characteristics of the ear canal tissue are preset.

[0018] Furthermore, the optimized impedance detection algorithm performs outlier removal processing on the collected impedance data, and the model formula is as follows:

[0019]

[0020] in, The processed effective ear canal impedance data; The first one collected from the original source Individual auditory canal tissue impedance data; The mean of the original collected data; The standard deviation of the original collected data; The outlier determination coefficient is determined based on historical data from electronic otoscopy and the physiological characteristics of the ear canal. The edge computing gateway's multidimensional heterogeneous otoscopy data analysis model extracts features from the processed data using the following formula:

[0021]

[0022] in, Features of the extracted ear canal impedance data; For effective ear canal impedance data The Feature extraction functions, For the first The weight parameters corresponding to the feature extraction function are adjusted according to the detection accuracy requirements of the electronic otoscope and the characteristics of the ear canal tissue.

[0023] Furthermore, the optimized impedance detection algorithm performs noise reduction on the ear canal impedance data using the following model formula:

[0024]

[0025] in, The data represents the ear canal impedance after noise reduction. This is the original ear canal impedance data collected; The noise reduction coefficient is set based on the noise characteristics of the electronic otoscope probe and the noise level of the ear canal environment. The Laplacian operator is used; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway uses the following formula to perform correlation analysis of different modal data:

[0026]

[0027] in, The correlation degree of different modal data; These are the first two different modal data sequences. One data point, These are the mean values ​​of two different modal data sequences, which are determined based on the detection parameters of an electronic otoscope and physiological indicators of the ear canal.

[0028] Furthermore, the optimized impedance detection algorithm normalizes the ear canal impedance data using the following model formula:

[0029]

[0030] in, These are the normalized ear canal impedance data; This is the original ear canal impedance data collected; These represent the maximum and minimum values ​​in the original collected data, respectively; the edge computing gateway's multidimensional heterogeneous otoscope data analysis model uses the following formula for data clustering analysis:

[0031]

[0032] in, For data points and The distance between them; Data points and In the Values ​​in each dimension The dimension is determined based on the detection parameters of the electronic otoscope and the characteristics of the ear canal tissue.

[0033] Furthermore, the optimized impedance detection algorithm predicts the trend of ear canal impedance data using the following model formula:

[0034]

[0035] in, For prediction The ear canal impedance value at a given time; This is the ear canal impedance data at a historical moment; The parameters for the prediction model are determined based on the detection cycle of the electronic otoscope and the physiological changes in the ear canal; the edge computing gateway multidimensional heterogeneous otoscope data analysis model uses the following formula for data classification and identification:

[0036]

[0037] Where sgn is the sign function. The classification results; The input ear canal impedance data features, The weights corresponding to the features; The weights and biases are set according to the detection standards of electronic otoscopes and the diagnostic standards for ear diseases.

[0038] Furthermore, the multimodal data processing unit includes an analog-to-digital conversion unit, a data caching unit, a data fusion unit, and an edge computing communication unit; the analog-to-digital conversion unit is used to convert the analog signal output by the weak signal conditioning unit into a digital signal; the data caching unit is used to temporarily store the digital signal after analog-to-digital conversion to match the data processing and transmission rate; the data fusion unit performs preliminary fusion processing on different types of digital signals; the edge computing communication unit transmits the fused data to the intelligent analysis and decision-making unit and receives control commands from the intelligent analysis and decision-making unit.

[0039] Furthermore, the intelligent analysis and decision-making unit includes an algorithm execution unit, a model storage unit, a data analysis unit, and a decision output unit. The algorithm execution unit runs an optimized impedance detection algorithm to analyze and process the data transmitted by the multimodal data processing unit. The model storage unit stores the edge computing gateway's multidimensional heterogeneous otoscope data analysis model and related parameters. Based on the stored model and the results after executing the algorithm, the data analysis unit performs in-depth analysis of ear health status. The decision output unit converts the analysis results into control commands and visualized data, and transmits them to the closed-loop automatic control unit and the human-machine interaction presentation unit, respectively.

[0040] Furthermore, the human-computer interaction presentation unit includes an image display unit, a data display unit, an operation input unit, and a status indicator unit; the image display unit is used to display image information inside the ear; the data display unit displays the detection data of ear health status in the form of charts; the operation input unit receives the user's operation instructions and transmits the instructions to the closed-loop automatic control unit; the status indicator unit displays the working status and detection process of the electronic otoscope through an indicator light group.

[0041] The intelligent electronic otoscope control method based on electrical impedance detection includes the following steps:

[0042] Step S1: The low-power multi-frequency impedance sensor array of the ear canal impedance signal acquisition unit acquires impedance data of ear canal tissue at different frequencies according to a preset frequency sequence.

[0043] Step S2: The acquired data is transmitted to the weak signal conditioning unit, where it is amplified by a preamplifier, filtered by a bandpass filter, and adjusted by a programmable gain amplifier.

[0044] Step S3: Transmit the conditioned signal to the multimodal data processing unit for analog-to-digital conversion, data buffering, and preliminary data fusion processing;

[0045] Step S4: Transmit the fused data to the intelligent analysis and decision-making unit, and use the optimized impedance detection algorithm and the edge computing gateway multidimensional heterogeneous otoscope data analysis model to perform in-depth analysis and processing of the data;

[0046] Step S5: Transmit the analysis results to the human-computer interaction presentation unit for visualization via a touch screen and indicator lights;

[0047] Step S6: Based on the analysis results of the intelligent analysis and decision-making unit, the closed-loop automatic control unit adjusts the acquisition frequency of the ear canal impedance signal acquisition unit, the amplification gain of the weak signal conditioning unit, the data processing parameters of the multimodal data processing unit, and the algorithm operation parameters of the intelligent analysis and decision-making unit to automate the entire detection process.

[0048] Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] This invention proposes an intelligent electronic otoscope control system and method based on electrical impedance detection. The system uses a low-power multi-frequency impedance sensor array to collect impedance data of ear canal tissue at different frequencies, breaking through the limitations of optical observation, obtaining comprehensive information about the inside of the ear canal, and reducing the possibility of missed diagnoses. At the same time, the intelligent analysis and decision unit is equipped with an optimized impedance detection algorithm and an edge computing gateway multidimensional heterogeneous otoscope data analysis model to perform in-depth processing and analysis of the collected data, avoiding differences in human judgment and ensuring objective and accurate diagnostic results.

[0050] To address the challenge of traditional otoscopes being unable to acquire information on the electrical properties of ear canal tissue, this system uses electrical impedance detection as its core. It amplifies and filters the acquired weak signals through a weak signal conditioning unit, combines analog-to-digital conversion and preliminary fusion by a multimodal data processing unit, and deep computation by an intelligent analysis and decision-making unit, and can accurately analyze changes in the electrical parameters of ear canal tissue.

[0051] In the early stages of a disease, even if there are no obvious morphological changes, potential lesions can be detected based on electrical properties, enabling early diagnosis of the disease.

[0052] In addition, the human-computer interaction presentation unit visualizes the detection results, and the closed-loop automatic control unit adjusts the operating parameters of each unit according to the analysis results, thereby automating the detection process and comprehensively improving the efficiency, accuracy and intelligence of ear disease detection. Attached Figure Description

[0053] Figure 1 This is a system unit composition diagram of the present invention;

[0054] Figure 2 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0055] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1 As shown, the intelligent electronic otoscope control system based on electrical impedance detection includes an ear canal impedance signal acquisition unit, a weak signal conditioning unit, a multimodal data processing unit, an intelligent analysis and decision-making unit, a human-computer interaction presentation unit, and a closed-loop automatic control unit.

[0057] The ear canal impedance signal acquisition unit includes a low-power multi-frequency impedance sensor array, which is used to acquire impedance data of ear canal tissue at different frequencies and transmit the data to the weak signal conditioning unit.

[0058] Specifically, the ear canal impedance signal acquisition unit is the starting point for the entire intelligent electronic otoscope control system to acquire raw data, and its core component is a low-power multi-frequency impedance sensor array. This array is manufactured using advanced microelectromechanical systems (MEMS) technology and can accurately acquire impedance data of ear canal tissue at multiple different frequencies, such as within the range of 100Hz-100kHz. The significance of selecting multiple frequencies for acquisition is that currents of different frequencies have different penetration depths and response characteristics in ear canal tissue. Through multi-frequency acquisition, more comprehensive and richer information on the electrical characteristics of ear canal tissue can be obtained.

[0059] In terms of implementation, a low-power multi-frequency impedance sensor array is integrated into the front end of the otoscope probe. Its design fully considers the physiological structure and size of the ear canal to ensure safe and comfortable insertion into the ear canal for detection. The sensor array is connected to the subsequent weak signal conditioning unit through a dedicated signal transmission line, which transmits the acquired weak impedance signal quickly and stably, providing a reliable data foundation for subsequent signal processing.

[0060] The weak signal conditioning unit consists of a preamplifier, a bandpass filter, and a programmable gain amplifier connected in sequence. It receives data from the ear canal impedance signal acquisition unit, amplifies and filters the signal, and then outputs it to the multimodal data processing unit.

[0061] Specifically, the weak signal conditioning unit plays a crucial role in the entire system, consisting of a preamplifier, a bandpass filter, and a programmable gain amplifier connected in sequence. The preamplifier employs a low-noise, high-input-impedance design, its main function being to initially amplify the weak impedance signal transmitted from the ear canal impedance signal acquisition unit, thereby increasing the signal amplitude for subsequent processing. Because the original signal is extremely weak and easily affected by noise, the preamplifier's low-noise characteristics effectively reduce the impact of noise on the signal, ensuring signal quality.

[0062] The bandpass filter is used to filter the frequency of the signal output from the preamplifier, allowing only signals within a specific frequency range to pass through while filtering out noise and interference signals at other frequencies. This operation further purifies the signal, making it cleaner and improving the signal-to-noise ratio. The programmable gain amplifier can automatically adjust the gain factor according to the strength of the actual signal through its internal control circuitry, further amplifying the bandpass-filtered signal to ensure that the signal output to the multimodal data processing unit has appropriate amplitude and quality to meet the requirements of subsequent data processing.

[0063] The multimodal data processing unit integrates an edge computing gateway, receives the processed data from the weak signal conditioning unit, performs analog-to-digital conversion and preliminary data fusion processing, and transmits the processing results to the intelligent analysis and decision-making unit.

[0064] Specifically, the multimodal data processing unit, integrated with an edge computing gateway, is a crucial unit in the system for converting data from analog to digital signals and performing preliminary fusion processing. Its analog-to-digital conversion unit employs a high-precision converter, capable of converting the analog signal output from the weak signal conditioning unit into a digital signal with high resolution and a high sampling rate, ensuring accuracy and integrity during the signal conversion process. The data buffer unit, serving as a temporary data storage area, utilizes high-speed caching technology to temporarily store and buffer the digital signal after analog-to-digital conversion based on differences in data processing and transmission rates, preventing data loss or transmission congestion.

[0065] The data fusion unit is responsible for the initial fusion processing of different types of digital signals, such as impedance data collected at different frequencies. Through specific fusion algorithms and logic, this data is integrated into more valuable information. The edge computing communication unit utilizes edge computing technology to perform preliminary data processing and analysis locally, reducing data transmission volume and latency. Simultaneously, it rapidly and stably transmits the fused data to the intelligent analysis and decision-making unit and receives control commands from the intelligent analysis and decision-making unit, enabling bidirectional data interaction.

[0066] The intelligent analysis and decision-making unit, equipped with an optimized impedance detection algorithm, receives data from the multimodal data processing unit and performs in-depth analysis of the data based on the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway.

[0067] Specifically, the intelligent analysis and decision-making unit, equipped with an optimized impedance detection algorithm, is the core unit of the entire system for assessing ear health. The algorithm execution unit, as the carrier of the algorithm, possesses powerful computing capabilities, enabling it to efficiently run the optimized impedance detection algorithm and perform in-depth analysis and processing of data transmitted from the multimodal data processing unit. Through complex calculations and logical judgments, it extracts key features and information from the data. The model storage unit stores the multidimensional heterogeneous otoscope data analysis model and related parameters of the edge computing gateway. These models and parameters are derived from extensive experiments and data analysis, providing a scientific basis for assessing ear health.

[0068] The data analysis unit performs in-depth analysis of ear health based on the stored models and the results processed by the algorithm execution unit. By comparing and analyzing data features, it determines whether ear diseases exist, as well as the type and severity of those diseases. The decision output unit converts the analysis results from the data analysis unit into control commands and visualized data. The control commands are transmitted to the closed-loop automatic control unit to adjust the operating parameters of each unit in the system; the visualized data is transmitted to the human-machine interface unit so that users can intuitively understand the test results.

[0069] The intelligent analysis and decision-making unit is connected to the human-computer interaction presentation unit and transmits the analysis results to the human-computer interaction presentation unit; the human-computer interaction presentation unit includes a touch screen and an indicator light group, which are used to visually present the ear health status detection results.

[0070] Specifically, the human-computer interaction unit serves as a crucial window for users to interact with the intelligent electronic otoscope control system, consisting of a touchscreen display and indicator lights. The image display unit utilizes a high-resolution screen to clearly display images of the inside of the ear, helping users intuitively observe its shape and structure. The data display unit presents detailed data on ear health status using charts, text, and other formats, including ear canal impedance data and analysis results, enabling users to gain a more comprehensive and accurate understanding of their ear health.

[0071] The operation input unit receives user commands via virtual buttons and touch gestures on the touchscreen display. These commands can include various operational needs such as starting detection, stopping detection, and viewing historical records. The commands are then transmitted to the closed-loop automatic control unit, enabling user control of the system. The status indicator unit displays the real-time operating status and detection progress of the electronic otoscope through different colors and flashing states of indicator lights, indicating whether the device is powered on, whether detection is in progress, and whether detection is complete, allowing users to stay informed about the device's operation.

[0072] The closed-loop automatic control unit is connected to the ear canal impedance signal acquisition unit, weak signal conditioning unit, multimodal data processing unit, intelligent analysis and decision-making unit, and human-computer interaction presentation unit, respectively. Based on the analysis results of the intelligent analysis and decision-making unit, the operating parameters of different units are adjusted.

[0073] Specifically, the closed-loop automatic control unit plays a crucial role in coordination and control throughout the system. It is connected to the ear canal impedance signal acquisition unit, weak signal conditioning unit, multimodal data processing unit, intelligent analysis and decision-making unit, and human-machine interaction presentation unit. During system operation, the closed-loop automatic control unit receives the analysis results from the intelligent analysis and decision-making unit in real time and determines whether the operating parameters of each unit need to be adjusted based on the results.

[0074] When the intelligent analysis and decision-making unit determines that there is an abnormality in the ear's health condition and more precise detection data is needed, the closed-loop automatic control unit sends instructions to the ear canal impedance signal acquisition unit to adjust its acquisition frequency and duration to obtain more detailed ear canal impedance data. Simultaneously, it adjusts the amplification gain and filtering parameters of the weak signal conditioning unit to ensure the accuracy of signal processing. Furthermore, the closed-loop automatic control unit also controls each unit of the system according to user operation instructions received by the human-machine interface presentation unit, achieving automated operation and optimization of the entire detection process.

[0075] Preferably, the optimized impedance detection algorithm processes ear canal impedance data using the following model formula:

[0076]

[0077] in, Indicates the effective ear canal impedance value; For the first The second acquisition of ear canal tissue impedance data. For the first The weighting coefficients corresponding to the data collected each time; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway performs fusion analysis on impedance data at different frequencies, and the formula is:

[0078]

[0079] in, The results of the data analysis after fusion; This indicates the results of the low-frequency ear canal impedance data analysis; This represents the analysis results of high-frequency ear canal impedance data; Let be the fusion coefficient, and The The detection frequency range of the electronic otoscope probe and the characteristics of the ear canal tissue are preset.

[0080] Specifically, the optimized impedance detection algorithm calculates the effective ear canal impedance value by taking a weighted average of different acquired data. The weighting coefficients for each acquired data are set according to actual detection needs, highlighting the influence of important data. The edge computing gateway's multidimensional heterogeneous otoscope data analysis model then fuses the impedance data analysis results at different frequencies. The fusion coefficient is determined by the detection frequency range of the electronic otoscope probe and the characteristics of the ear canal tissue. In implementation, the algorithm and model are embedded in an intelligent analysis and decision-making unit, which performs in-depth processing on the data acquired by the ear canal impedance signal acquisition unit and initially processed by the multimodal data processing unit, providing a more accurate data foundation for assessing ear health.

[0081] Preferably, the optimized impedance detection algorithm performs outlier removal processing on the collected impedance data, and the model formula is as follows:

[0082]

[0083] in, The processed effective ear canal impedance data; The first one collected from the original source Individual auditory canal tissue impedance data; The mean of the original collected data; The standard deviation of the original collected data; The outlier determination coefficient is determined based on historical data from electronic otoscopy and the physiological characteristics of the ear canal. The edge computing gateway's multidimensional heterogeneous otoscopy data analysis model extracts features from the processed data using the following formula:

[0084]

[0085] in, Features of the extracted ear canal impedance data; For effective ear canal impedance data The Feature extraction functions, For the first The weight parameters corresponding to the feature extraction function are adjusted according to the detection accuracy requirements of the electronic otoscope and the characteristics of the ear canal tissue.

[0086] Specifically, the optimized impedance detection algorithm uses specific rules to remove outliers from the collected impedance data. Based on the mean, standard deviation, and a set outlier determination coefficient of the original data, it determines the validity of the data, ensuring its reliability. The edge computing gateway's multidimensional heterogeneous otoscope data analysis model utilizes a weighted feature extraction function to extract features from the valid data. The weight parameters are adjusted according to the accuracy requirements of electronic otoscope detection and the characteristics of ear canal tissue. In practical applications, after the intelligent analysis and decision-making unit receives data from the multimodal data processing unit, the algorithm first performs outlier removal, and then the model extracts data features, providing crucial information for subsequent ear disease diagnosis and analysis.

[0087] Preferably, the optimized impedance detection algorithm performs noise reduction processing on the ear canal impedance data using the following model formula:

[0088]

[0089] in, The data represents the ear canal impedance after noise reduction. This is the original ear canal impedance data collected; The noise reduction coefficient is set based on the noise characteristics of the electronic otoscope probe and the noise level of the ear canal environment. The Laplacian operator is used; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway uses the following formula to perform correlation analysis of different modal data:

[0090]

[0091] in, The correlation degree of different modal data; These are the first two different modal data sequences. One data point, These are the mean values ​​of two different modal data sequences, which are determined based on the detection parameters of an electronic otoscope and physiological indicators of the ear canal.

[0092] Specifically, the optimized impedance detection algorithm uses specific computations to denoise the ear canal impedance data. The denoising coefficient is set based on the noise characteristics of the electronic otoscope probe and the noise level of the ear canal environment, reducing noise interference with the data. The edge computing gateway's multidimensional heterogeneous otoscope data analysis model analyzes the relationships between data by calculating the correlation degree of different modal data. The two different modal data are determined by the electronic otoscope detection parameters and ear canal physiological indicators. During system operation, the intelligent analysis and decision-making unit first uses the algorithm to denoise the data transmitted by the multimodal data processing unit, and then uses the model to perform data correlation analysis to uncover potential connections between data, assisting in the judgment of ear health status.

[0093] Preferably, the optimized impedance detection algorithm normalizes the ear canal impedance data using the following model formula:

[0094]

[0095] in, These are the normalized ear canal impedance data; This is the original ear canal impedance data collected; These represent the maximum and minimum values ​​in the original collected data, respectively; the edge computing gateway's multidimensional heterogeneous otoscope data analysis model uses the following formula for data clustering analysis:

[0096]

[0097] in, For data points and The distance between them; Data points and In the Values ​​in each dimension The dimension is determined based on the detection parameters of the electronic otoscope and the characteristics of the ear canal tissue.

[0098] Specifically, the optimized impedance detection algorithm normalizes ear canal impedance data, mapping the raw data to a specific interval for easier subsequent analysis. The edge computing gateway's multidimensional heterogeneous otoscope data analysis model performs cluster analysis by calculating the distance between data points; the dimensions are determined based on the electronic otoscope detection parameters and ear canal tissue characteristics. During implementation, after receiving data from the multimodal data processing unit, the intelligent analysis and decision-making unit first performs a normalization operation, and then the model clusters the processed data, grouping similar data into one category to provide classification references for ear disease diagnosis and identify data feature patterns.

[0099] Preferably, the optimized impedance detection algorithm predicts the trend of ear canal impedance data using the following model formula:

[0100]

[0101] in, For prediction The ear canal impedance value at a given time; This is the ear canal impedance data at a historical moment; The parameters for the prediction model are determined based on the detection cycle of the electronic otoscope and the physiological changes in the ear canal; the edge computing gateway multidimensional heterogeneous otoscope data analysis model uses the following formula for data classification and identification:

[0102]

[0103] Where sgn is the sign function. The classification results; The input ear canal impedance data features, The weights corresponding to the features; The weights and biases are set according to the detection standards of electronic otoscopes and the diagnostic standards for ear diseases.

[0104] Specifically, the optimized impedance detection algorithm predicts trends based on historical ear canal impedance data. The prediction model parameters are determined according to the electronic otoscope testing cycle and the physiological changes in the ear canal, allowing for advance prediction of ear health trends. The edge computing gateway's multidimensional heterogeneous otoscope data analysis model classifies and identifies data through specific operations, with weights and biases set by electronic otoscope testing standards and ear disease diagnostic standards. In practical operation, the intelligent analysis and decision-making unit uses the algorithm to predict trends in the data transmitted by the multimodal data processing unit, then classifies it using the model to determine whether the ear is diseased and the type of disease, providing prospective and definitive conclusions for ear disease diagnosis.

[0105] Preferably, the multimodal data processing unit includes an analog-to-digital conversion unit, a data caching unit, a data fusion unit, and an edge computing communication unit; the analog-to-digital conversion unit is used to convert the analog signal output by the weak signal conditioning unit into a digital signal; the data caching unit is used to temporarily store the digital signal after analog-to-digital conversion to match the data processing and transmission rate; the data fusion unit performs preliminary fusion processing on different types of digital signals; the edge computing communication unit transmits the fused data to the intelligent analysis and decision-making unit and receives control commands from the intelligent analysis and decision-making unit.

[0106] Specifically, the multimodal data processing unit comprises multiple functional units. The analog-to-digital conversion unit uses high-precision devices to convert the analog signal output from the weak signal conditioning unit into a digital signal with high resolution and sampling rate, ensuring signal conversion quality. The data caching unit employs high-speed caching technology to temporarily store digital signals based on differences in data processing and transmission rates, preventing data loss or transmission congestion. The data fusion unit uses specific algorithmic logic to initially fuse different types of digital signals, integrating data information. The edge computing communication unit utilizes edge computing technology to initially process and analyze data locally, reducing transmission volume and latency, enabling bidirectional data interaction with the intelligent analysis and decision-making unit, and providing preprocessed data for subsequent in-depth analysis.

[0107] Preferably, the intelligent analysis and decision-making unit includes an algorithm execution unit, a model storage unit, a data analysis unit, and a decision output unit; the algorithm execution unit runs an optimized impedance detection algorithm to analyze and process the data transmitted by the multimodal data processing unit; the model storage unit stores the edge computing gateway multidimensional heterogeneous otoscope data analysis model and related parameters; the data analysis unit performs in-depth analysis of ear health status based on the stored model and the results after algorithm execution; the decision output unit converts the analysis results into control commands and visualized data, and transmits them to the closed-loop automatic control unit and the human-machine interaction presentation unit, respectively.

[0108] Specifically, the intelligent analysis and decision-making unit has clearly defined functions for each component. The algorithm execution unit, leveraging its powerful computing capabilities, runs an optimized impedance detection algorithm to deeply analyze the data transmitted from the multimodal data processing unit, extracting key feature information. The model storage unit stores the multidimensional heterogeneous otoscope data analysis model and parameters from the edge computing gateway, providing a scientific basis for analysis. Based on the stored model and algorithm processing results, the data analysis unit deeply analyzes ear health status and determines disease-related information. The decision output unit transforms the analysis results into control commands and visualized data, transmitting them to the closed-loop automatic control unit and the human-machine interaction presentation unit, respectively, to achieve system control and adjustment and result display, completing the analysis and judgment process for ear health status.

[0109] Preferably, the human-computer interaction presentation unit includes an image display unit, a data display unit, an operation input unit, and a status indicator unit; the image display unit is used to display image information inside the ear; the data display unit displays the detection data of ear health status in the form of charts; the operation input unit receives the user's operation instructions and transmits the instructions to the closed-loop automatic control unit; the status indicator unit displays the working status and detection process of the electronic otoscope through an indicator light group.

[0110] Specifically, the human-computer interaction unit works collaboratively across all its components. The image display unit uses a high-resolution screen to clearly display images of the inside of the ear, helping users to intuitively observe the ear's shape and structure. The data display unit presents detailed ear health test data in various formats, enabling users to fully understand their ear health status. The operation input unit receives user operation commands, such as start and stop testing, via a touch screen and transmits them to the closed-loop automatic control unit, enabling users to control the system. The status indicator unit uses different colors and flashing states of indicator lights to display the electronic otoscope's operation and testing progress in real time, allowing users to keep track of the equipment's operating status and improving the user experience and ease of operation.

[0111] like Figure 2 As shown, the intelligent electronic otoscope control method based on electrical impedance detection includes the following steps:

[0112] Step S1: The low-power multi-frequency impedance sensor array of the ear canal impedance signal acquisition unit acquires impedance data of ear canal tissue at different frequencies according to a preset frequency sequence.

[0113] Step S2: The acquired data is transmitted to the weak signal conditioning unit, where it is amplified by a preamplifier, filtered by a bandpass filter, and adjusted by a programmable gain amplifier.

[0114] Step S3: Transmit the conditioned signal to the multimodal data processing unit for analog-to-digital conversion, data buffering, and preliminary data fusion processing;

[0115] Step S4: Transmit the fused data to the intelligent analysis and decision-making unit, and use the optimized impedance detection algorithm and the edge computing gateway multidimensional heterogeneous otoscope data analysis model to perform in-depth analysis and processing of the data;

[0116] Step S5: Transmit the analysis results to the human-computer interaction presentation unit for visualization via a touch screen and indicator lights;

[0117] Step S6: Based on the analysis results of the intelligent analysis and decision-making unit, the closed-loop automatic control unit adjusts the acquisition frequency of the ear canal impedance signal acquisition unit, the amplification gain of the weak signal conditioning unit, the data processing parameters of the multimodal data processing unit, and the algorithm operation parameters of the intelligent analysis and decision-making unit to automate the entire detection process.

[0118] This invention relates to an intelligent electronic otoscope control system and method based on electrical impedance detection. The system abandons the single visual observation method, replacing traditional optical observation with a low-power multi-frequency impedance sensor array. The sensor array can collect impedance data of ear canal tissue from multiple frequency dimensions, overcoming the limitations of light caused by the complex structure of the ear canal, comprehensively acquiring information about the internal ear canal, and avoiding missed diagnoses due to blind spots. Simultaneously, the intelligent analysis and decision-making unit, equipped with an optimized impedance detection algorithm and an edge computing gateway multi-dimensional heterogeneous otoscope data analysis model, can standardize and objectively process and analyze the collected data. The algorithm ensures data reliability through noise reduction and outlier removal; the model, based on parameters trained on a large amount of data, judges the health status of the ear, reducing human interference and making the diagnostic results more objective and consistent.

[0119] To address the limitation of traditional otoscopes in detecting the electrical properties of ear canal tissue, this system utilizes electrical impedance detection as its core technology. Impedance data acquired by the ear canal impedance signal acquisition unit is processed by a preamplifier, bandpass filter, and programmable gain amplifier in a weak signal conditioning unit, effectively improving signal quality. A multimodal data processing unit further performs analog-to-digital conversion and fusion of the signal, providing a high-quality data foundation for the intelligent analysis and decision-making unit. Through optimized impedance detection algorithms and data analysis models, the system can deeply mine the physiological and pathological information of the ear contained in the impedance data. Even in the early stages of ear diseases, before significant morphological changes are observed, subtle changes in electrical properties can detect potential lesions in a timely manner, enabling early diagnosis and significantly improving the timeliness of ear disease treatment. Furthermore, a human-computer interaction unit displays the test results in a visual format, and a closed-loop automatic control unit automates the detection process, comprehensively improving the efficiency, accuracy, and intelligence of ear disease detection.

[0120] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0121] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart electronic otoscope control system based on electrical impedance detection, characterized in that, include: The ear canal impedance signal acquisition unit includes a multi-frequency impedance sensor array, used to acquire impedance data of ear canal tissue at different frequencies. The weak signal conditioning unit includes a preamplifier, a bandpass filter and a programmable gain amplifier connected in sequence. It receives data from the ear canal impedance signal acquisition unit and performs signal amplification and filtering. The multimodal data processing unit, which integrates an edge computing gateway, is used to receive the processed data from the weak signal conditioning unit, perform analog-to-digital conversion, and perform preliminary data fusion processing. The intelligent analysis and decision-making unit, equipped with an optimized impedance detection algorithm, receives the fused data from the multimodal data processing unit and further analyzes the data in depth based on the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway. The optimized impedance detection algorithm processes the ear canal impedance data using the following model formula: in, Indicates the effective ear canal impedance value; For the first The second acquisition of ear canal tissue impedance data. For the first The weighting coefficients corresponding to the data collected each time; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway performs fusion analysis on impedance data at different frequencies, and the formula is: in, The results of the data analysis after fusion; This indicates the results of the low-frequency ear canal impedance data analysis; This represents the analysis results of high-frequency ear canal impedance data; Let be the fusion coefficient, and The The detection frequency range of the electronic otoscope probe and the characteristics of the ear canal tissue are preset accordingly; The optimized impedance detection algorithm performs outlier removal on the collected impedance data. The model formula is as follows: in, The processed effective ear canal impedance data; The first one collected from the original source Individual auditory canal tissue impedance data; The mean of the original collected data; The standard deviation of the original collected data; The outlier determination coefficient is determined based on historical data from electronic otoscopy and the physiological characteristics of the ear canal. The edge computing gateway's multidimensional heterogeneous otoscopy data analysis model extracts features from the processed data using the following formula: in, Features of the extracted ear canal impedance data; For effective ear canal impedance data The Feature extraction functions, For the first The weight parameters corresponding to the feature extraction function are adjusted according to the detection accuracy requirements of the electronic otoscope and the characteristics of the ear canal tissue; The human-computer interaction presentation unit is used to receive the analysis results from the intelligent analysis and decision-making unit. The human-computer interaction presentation unit includes a touch screen and an indicator light group to visually present the ear health status detection results. The closed-loop automatic control unit is connected to the ear canal impedance signal acquisition unit, weak signal conditioning unit, multimodal data processing unit, intelligent analysis and decision-making unit, and human-computer interaction presentation unit, respectively. Based on the analysis results of the intelligent analysis and decision-making unit, the operating parameters of different units are adjusted.

2. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 1, characterized in that, The optimized impedance detection algorithm performs noise reduction on ear canal impedance data using the following model formula: in, The data represents the ear canal impedance after noise reduction. This is the original ear canal impedance data collected; The noise reduction coefficient is set based on the noise characteristics of the electronic otoscope probe and the noise level of the ear canal environment. The Laplacian operator is used; the multidimensional heterogeneous otoscope data analysis model of the edge computing gateway uses the following formula to perform correlation analysis of different modal data: in, The correlation degree of different modal data; These are the first two different modal data sequences. One data point, These are the mean values ​​of two different modal data sequences, which are determined based on the detection parameters of an electronic otoscope and physiological indicators of the ear canal.

3. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 2, characterized in that, The optimized impedance detection algorithm uses the following model formula to normalize the ear canal impedance data: in, These are the normalized ear canal impedance data; This is the original ear canal impedance data collected; These represent the maximum and minimum values ​​in the original collected data, respectively; the edge computing gateway's multidimensional heterogeneous otoscope data analysis model uses the following formula for data clustering analysis: in, For data points and The distance between them; Data points and In the Values ​​in each dimension The dimension is determined based on the detection parameters of the electronic otoscope and the characteristics of the ear canal tissue.

4. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 3, characterized in that, The optimized impedance detection algorithm predicts the trend of ear canal impedance data using the following model formula: in, For prediction The ear canal impedance value at a given time; This is the ear canal impedance data at a historical moment; The parameters for the prediction model are determined based on the detection cycle of the electronic otoscope and the physiological changes in the ear canal; the edge computing gateway multidimensional heterogeneous otoscope data analysis model uses the following formula for data classification and identification: Where sgn is the sign function. The classification results; The input ear canal impedance data features, The weights corresponding to the features; The weights and biases are set according to the detection standards of electronic otoscopes and the diagnostic standards for ear diseases.

5. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 1, characterized in that, The multimodal data processing unit includes an analog-to-digital conversion unit, a data caching unit, a data fusion unit, and an edge computing communication unit. The analog-to-digital conversion unit converts the analog signal output by the weak signal conditioning unit into a digital signal. The data caching unit temporarily stores the digital signal after analog-to-digital conversion to match the data processing and transmission rates. The data fusion unit performs preliminary fusion processing on different types of digital signals. The edge computing communication unit transmits the fused data to the intelligent analysis and decision-making unit and receives control commands from the intelligent analysis and decision-making unit.

6. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 1, characterized in that, The intelligent analysis and decision-making unit includes an algorithm execution unit, a model storage unit, a data analysis unit, and a decision output unit; the algorithm execution unit runs an optimized impedance detection algorithm to analyze and process the data transmitted by the multimodal data processing unit; The model storage unit stores the multidimensional heterogeneous otoscope data analysis model and related parameters of the edge computing gateway; The data analysis unit performs in-depth analysis of ear health based on the stored model and the results of the executed algorithm; the decision output unit converts the analysis results into control commands and visualized data, and transmits them to the closed-loop automatic control unit and the human-machine interaction presentation unit, respectively.

7. The intelligent electronic otoscope control system based on electrical impedance detection according to claim 1, characterized in that, The human-computer interaction presentation unit includes an image display unit, a data display unit, an operation input unit, and a status indication unit; the image display unit is used to display image information of the inside of the ear; The data display unit presents the detection data of ear health status in the form of charts; the operation input unit receives the user's operation commands and transmits the commands to the closed-loop automatic control unit; the status indication unit displays the working status and detection process of the electronic otoscope through indicator lights.

8. A smart electronic otoscope control method based on electrical impedance detection, applied to the smart electronic otoscope control system as described in any one of claims 1-7, characterized in that, Includes the following steps: Step S1: The multi-frequency impedance sensor array of the ear canal impedance signal acquisition unit acquires impedance data of ear canal tissue at different frequencies according to a preset frequency sequence. Step S2: The acquired data is transmitted to the weak signal conditioning unit, where it is amplified by a preamplifier, filtered by a bandpass filter, and adjusted by a programmable gain amplifier. Step S3: Transmit the conditioned signal to the multimodal data processing unit for analog-to-digital conversion, data buffering, and preliminary data fusion processing; Step S4: Transmit the fused data to the intelligent analysis and decision-making unit, and use the optimized impedance detection algorithm and the edge computing gateway multidimensional heterogeneous otoscope data analysis model to perform in-depth analysis and processing of the data; Step S5: Transmit the analysis results to the human-computer interaction presentation unit for visualization via a touch screen and indicator lights; Step S6: Based on the analysis results of the intelligent analysis and decision-making unit, the closed-loop automatic control unit adjusts the acquisition frequency of the ear canal impedance signal acquisition unit, the amplification gain of the weak signal conditioning unit, the data processing parameters of the multimodal data processing unit, and the algorithm operation parameters of the intelligent analysis and decision-making unit to automate the entire detection process.

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