Adaptive multi-resolution methods, systems, and products for testing low impedance tissues

Abstract

The invention provides a self-adaptive multi-resolution method, a self-adaptive multi-resolution system and a self-adaptive multi-resolution product for testing a low-impedance tissue, relates to the technical field of electrical impedance measurement, and solves the technical problem that enough electrical impedance data can be quickly obtained while high precision is ensured as much as possible in a bioelectrical impedance testing process. Electrical impedance measurement is carried out on the low-impedance biological tissue through the biological probe, and an electrical impedance characteristic signal is output in real time; secondly, dual-channel acquisition is carried out on the electrical impedance characteristic signals; thirdly, the FPGA main control chip dynamically selects the electrical impedance characteristic data correspondingly output by one channel according to the data difference between the two parallel data acquisition channels; and finally, determining low-impedance biological tissue components and / or states according to the selected and output electrical impedance characteristic data. According to the invention, self-adaptive multi-resolution data acquisition and processing of the low-impedance biological tissue are realized through dual-channel acquisition and an intelligent data selection mechanism, so that components and states of the low-impedance biological tissue are accurately judged.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical impedance measurement, and in particular to an adaptive multi-resolution method, system and computer program product for testing low-impedance tissue. Background Art

[0002] Low-impedance biological tissue electrical impedance measurement is a technology based on electrical principles and is widely used in medical diagnosis, body composition analysis, tissue health monitoring, and biophysical research. By measuring the electrical impedance characteristics of low-impedance biological tissue, important information about the electrical characteristics, structural changes, and health status of the tissue can be obtained.

[0003] However, existing electrical impedance measurement methods usually face low measurement accuracy. There is a conflict between obtaining high-resolution and detailed data and quickly responding to dynamic changes. How to quickly obtain sufficient electrical impedance data while ensuring high accuracy as much as possible has become a technical problem that needs to be solved urgently in current bioimpedance testing technology. Summary of the invention

[0004] One object of the present invention is to quickly obtain sufficient electrical impedance data while ensuring high accuracy as much as possible.

[0005] According to one aspect of an embodiment of the present invention, an adaptive multi-resolution method for testing low-impedance tissue is disclosed, the method comprising: Measuring the electrical impedance of low-impedance biological tissue by using a biological probe, and outputting an electrical impedance characteristic signal in real time, wherein the electrical impedance characteristic signal represents a change in the electrical characteristics of the measured low-impedance biological tissue; Implementing dual-channel acquisition of the electrical impedance characteristic signal so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, wherein the parallel data acquisition channels include a high-resolution channel and a fast measurement channel; The FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by one channel according to the data difference between the two parallel data acquisition channels, wherein the electrical impedance characteristic data is high-precision electrical impedance characteristic data or electrical impedance characteristic fast measurement data; The low impedance biological tissue component and / or state is determined based on the selected output electrical impedance characteristic data.

[0006] According to one aspect of the embodiment of the present invention, the two parallel data acquisition channels are configured with a preprocessing module and an analog-to-digital conversion chip, and the electrical impedance characteristic signal is subjected to dual-channel acquisition so that the electrical impedance characteristic signal is synchronously transmitted to the two parallel data acquisition channels, including: The electrical impedance characteristic signal is sampled in parallel through two configured parallel data acquisition channels, so that the electrical impedance characteristic signal is synchronously transmitted to two pre-processing modules constituting the data acquisition channels; The preprocessing module performs a preprocessing operation on the electrical impedance characteristic signal, and then transmits the signal to the connected analog-to-digital conversion chip to perform an analog-to-digital conversion operation; After the analog-to-digital conversion operation is completed, the obtained electrical impedance characteristic data is transmitted to the FPGA main control chip, and the FPGA main control chip implements pre-output control of the electrical impedance characteristic data of the two parallel data acquisition channels.

[0007] According to one aspect of the embodiment of the present invention, before the FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by a channel according to the data difference between the two parallel data acquisition channels, the method further includes: The FPGA main control chip performs adaptive filtering on the input electrical impedance characteristic data to obtain electrical impedance characteristic data with bandwidth noise of the effective noise bandwidth within the sampling bandwidth filtered out.

[0008] According to one aspect of an embodiment of the present invention, the FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by a channel according to the data difference between two parallel data acquisition channels, including: Calculate the absolute difference between the data of two parallel data acquisition channels; A comparison is performed between the absolute difference value and a preset difference threshold, and electrical impedance characteristic data corresponding to the output of a channel is selected according to a magnitude relationship between the absolute difference value and the preset difference threshold.

[0009] According to one aspect of an embodiment of the present invention, the comparing the absolute difference value with a preset difference threshold, and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold, comprises: Performing a numerical comparison between the absolute difference value and a preset difference threshold value to obtain a magnitude relationship between the absolute difference value and the preset difference threshold value; If the magnitude relationship indicates that the absolute difference value is greater than a preset difference threshold, the electrical impedance characteristic fast measurement data corresponding to the fast measurement channel is selected for output.

[0010] According to one aspect of an embodiment of the present invention, the comparing the absolute difference value with a preset difference threshold, and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold, further comprises: If the magnitude relationship indicates that the absolute difference value is not greater than a preset difference threshold, the high-precision electrical impedance characteristic data corresponding to the high-resolution channel is selected for output.

[0011] According to one aspect of an embodiment of the present invention, the electrical impedance characteristic signal synchronously maps the multi-modal changes of the electrical characteristics of the low-impedance biological tissue that vary in time and space.

[0012] According to one aspect of an embodiment of the present invention, an adaptive multi-resolution system for testing low-impedance tissue is disclosed, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0013] According to one aspect of an embodiment of the present invention, a computer program product is disclosed, including a computer program, wherein when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0014] The embodiment of the present invention is used to measure the electrical impedance characteristics of low-impedance biological tissue in real time, and realizes adaptive multi-resolution data acquisition and processing of low-impedance biological tissue through dual-channel acquisition and intelligent data selection mechanism, thereby accurately judging the composition and state of low-impedance biological tissue.

[0015] Through dual-channel acquisition and dynamic channel selection, this solution can improve the speed of data acquisition while maintaining high measurement accuracy. Traditional single measurement channels often require a trade-off between high accuracy and fast measurement. This solution flexibly selects high-resolution channels or fast measurement channels, allowing the system to automatically switch in different situations according to actual needs, ensuring high accuracy of measurement data while providing sufficient real-time performance to meet dynamic monitoring needs in clinical or experimental settings.

[0016] By using the FPGA master chip to dynamically analyze and select the data differences between the two parallel acquisition channels, the system can make real-time judgments and quickly switch to the most suitable measurement channel. This intelligent selection mechanism greatly enhances the system's ability to respond to changes in the state of low-impedance biological tissues, making electrical impedance measurement not only limited to static monitoring, but also able to adapt to rapidly changing physiological states, providing more accurate and reliable real-time data, and is suitable for scenarios such as dynamic monitoring and clinical diagnosis.

[0017] Through the intelligent algorithm of the FPGA main control chip, it can automatically select the best channel and measurement parameters, eliminate unnecessary noise, and ensure that the quality of the acquired data is more stable and reliable. This is crucial to improving the accuracy of measurement results, especially in complex medical environments, and can significantly improve the accuracy and efficiency of diagnosis.

[0018] Based on the electrical impedance characteristic data, the embodiments of the present invention can help accurately identify different types of low-impedance biological tissues (such as muscle, fat, blood, brain tissue, etc.) and their pathological changes (such as tumors, inflammation, hemorrhage areas, etc.). By analyzing and processing high-resolution or fast measurement data, the composition, density and health status of low-impedance biological tissues can be inferred based on the characteristics of the electrical impedance data. For example, in medical diagnosis, by real-time monitoring of the changes in the electrical impedance of brain tissue, heart tissue, etc., potential pathological problems can be discovered in advance, and early diagnosis and early warning can be achieved.

[0019] The embodiment of the present invention adopts adaptive multi-resolution technology, and can automatically adjust the working mode according to different measurement requirements and environmental conditions. For example, based on the signal processing state, when high-precision diagnosis is required, the system automatically selects the high-resolution channel; and when the high-resolution takes a long time to achieve and it is necessary to quickly obtain a large amount of data, it automatically switches to the fast measurement channel. This flexible adaptability can be widely used in different types of low-impedance biological tissue tests and different medical scenarios, greatly improving the universality and applicability of the technology.

[0020] Furthermore, the embodiment of the present invention uses an FPGA master chip to control dual-channel acquisition and data processing, and realizes parallel computing and adaptive adjustment on the hardware platform. This efficient hardware architecture can effectively reduce the required processor resources, reduce the overall hardware cost, and no longer need to implement image detection. At the same time, the programmability of the FPGA master chip enables the system to flexibly adjust the working mode according to actual needs, enhancing the scalability and customization capabilities of the system.

[0021] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0024] Figure 1 The figure is a flow chart of an adaptive multi-resolution method for testing low impedance tissue according to an exemplary embodiment.

[0025] Figure 2 is based on Figure 1 The corresponding embodiment shows a method flow chart describing the steps of implementing dual-channel acquisition of the electrical impedance characteristic signal so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, where the parallel data acquisition channels include a high-resolution channel and a fast measurement channel.

[0026] Figure 3 is based on Figure 1 The corresponding embodiment shows a method flow chart describing the steps of dynamically selecting the electrical impedance characteristic data outputted by a channel corresponding to the FPGA main control chip according to the data difference between two parallel data acquisition channels.

[0027] Figure 4 is based on Figure 3 The corresponding embodiment shows a flowchart that describes in detail the steps of comparing the absolute difference value with a preset difference threshold and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the size relationship between the absolute difference value and the preset difference threshold.

[0028] Figure 5 A transverse FIR adaptive filter structure is shown according to an exemplary embodiment.

[0029] Figure 6 is based on Figure 1 A method flow chart describing the steps of determining low-impedance biological tissue components and / or states based on selected output electrical impedance characteristic data is shown in the corresponding embodiment. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present invention will be more comprehensive and complete and the concepts of the example embodiments will be fully conveyed to those skilled in the art. The accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0031] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present invention.

[0032] See also Figure 1 , Figure 1 The figure is a flow chart of an adaptive multi-resolution method for testing low impedance tissue according to an exemplary embodiment.

[0033] The adaptive multi-resolution method for testing low impedance tissue provided by the embodiment of the present invention comprises the following steps: Step S110, measuring the electrical impedance of the low-impedance biological tissue by using a biological probe, and outputting an electrical impedance characteristic signal in real time, wherein the electrical impedance characteristic signal represents a change in the electrical characteristics of the measured low-impedance biological tissue; Step S120, performing dual-channel acquisition on the electrical impedance characteristic signal, so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, the parallel data acquisition channels including a high-resolution channel and a fast measurement channel; Step S130, the FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by one channel according to the data difference between the two parallel data acquisition channels, wherein the electrical impedance characteristic data is high-precision electrical impedance characteristic data or electrical impedance characteristic fast measurement data; Step S140, determining the low-impedance biological tissue component and / or state based on the selected output electrical impedance characteristic data.

[0034] These steps are described in detail below.

[0035] First of all, it should be explained that the biological probe is a variety of sensors or other devices used to measure the electrical impedance of low-impedance biological tissue. For example, it can be composed of electrodes to introduce electrical signals (current or voltage) into low-impedance biological tissue and receive feedback signals to output electrical impedance characteristic signals.

[0036] The biological probe contacts low-impedance biological tissue, applies a weak AC signal, and measures the response of the current in the low-impedance biological tissue. The electrical properties of different low-impedance biological tissues (such as resistance, conductivity, capacitance, etc.) will cause the current to pass differently, so the electrical impedance characteristic signal returned by the probe can reflect the electrical properties of the low-impedance biological tissue.

[0037] The electrical impedance characteristic signal is a signal collected by a biological probe, which reflects the change in the electrical impedance of the low-impedance biological tissue being measured. By measuring the response of low-impedance biological tissue to currents of different frequencies, the frequency spectrum of the electrical impedance can be obtained, which characterizes the electrical properties of the tissue. The electrical impedance characteristic signal can provide information about the conductivity, structural properties, water content, ion concentration, etc. of the low-impedance biological tissue.

[0038] The object of the electrical impedance measurement is a low-impedance biological tissue, the electrical impedance value of the low-impedance biological tissue is lower than that of the high-impedance biological tissue, and the electrical impedance value of the low-impedance biological tissue is in a medium impedance range or a low impedance range.

[0039] That is to say, non-high impedance biological tissue is what the present invention calls low impedance biological tissue. It should be understood that electrical impedance refers to the degree of resistance of biological tissue to the flow of electric current, and is used to describe the response of biological tissue to electrical signals. Electrical impedance consists of two parts: resistance and reactance, in which resistance reflects the conductivity of the material, and reactance is related to frequency, capacitance and inductance of the tissue. In biological tissue, low impedance means that the biological tissue has relatively little resistance to electric current, and the current can pass easily.

[0040] Thus, by way of example, low impedance biological tissues may be brain tissue, muscle tissue, blood, nerve tissue, and organs such as liver and kidney.

[0041] The medium impedance range or low impedance range of the impedance value of low impedance biological tissue is compared with the high impedance range. The high impedance range is the impedance value range corresponding to the current flow being greatly hindered, for example, the impedance value corresponding to bone and fat tissue is in the high impedance range. In addition, biological tissues corresponding to relatively low impedance values, such as the impedance values ​​in the medium impedance range and the low impedance range, are low impedance biological tissues.

[0042] The electrical impedance measurement of low-impedance biological tissue is carried out through biological probes, and the tissue composition and state are analyzed and judged based on the measured subtle electrical impedance characteristics, thereby providing strong data support for medical diagnosis and biophysical research.

[0043] At this point, after the electrical impedance characteristic signal is output in real time for the low-impedance biological tissue through the execution of step S110, the electrical impedance characteristic signal can be subjected to dual-channel sampling through the execution of step S120, and the electrical impedance characteristic signal can be synchronously transmitted to two parallel data acquisition channels.

[0044] In step S120, dual-channel acquisition refers to acquiring data through two independent channels at the same time. The electrical impedance characteristic signals output by the biological probe in real time are sampled in parallel through the two constructed parallel data acquisition channels, so that the electrical impedance characteristic signals are synchronously transmitted to the constructed data acquisition channels.

[0045] The two parallel data acquisition channels for dual-channel acquisition include a high-resolution channel and a fast measurement channel. The high-resolution channel is used to accurately capture the details of the electrical impedance characteristic signal and provide the highest possible measurement accuracy. For example, the high-resolution channel will use a higher sampling rate and a smaller range to capture tiny changes in the electrical impedance characteristic signal, thereby obtaining more detailed data.

[0046] The fast measurement channel is used to increase the speed of data acquisition and achieve fast response. Compared with the high-resolution channel, it sacrifices some measurement accuracy, but replaces it with a higher sampling frequency and a larger range, so that the electrical impedance characteristic data can be obtained quickly.

[0047] After the two parallel data acquisition channels implement dual-channel acquisition of the electrical impedance characteristic signal, the output data will be transmitted to the FPGA main control chip, and then the output control of the channel is realized as shown in step S130.

[0048] Further explained, the parallel data acquisition channel, whether the high-resolution channel or the fast measurement channel, passes through the pre-processing module and the analog-to-digital conversion chip configured by each. In other words, the high-resolution channel and the fast measurement channel are constructed by configuring the pre-processing module and the analog-to-digital conversion chip. Between the high-resolution channel and the fast measurement channel, the two pre-processing modules are independent of each other, and the two analog-to-digital conversion chips are independent of each other, and each implements its corresponding processing process.

[0049] See also Figure 2 , Figure 2 is based on Figure 1 The corresponding embodiment shows a method flow chart describing the steps of implementing dual-channel acquisition of the electrical impedance characteristic signal so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, where the parallel data acquisition channels include a high-resolution channel and a fast measurement channel.

[0050] The embodiment of the present invention provides a dual-channel acquisition method for the electrical impedance characteristic signal, so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, and the parallel data acquisition channels include a high-resolution channel and a fast measurement channel. Step S120 includes: Step S121, performing sampling operation on the electrical impedance characteristic signal in parallel through the configured two parallel data acquisition channels, so that the electrical impedance characteristic signal is synchronously transmitted to the two pre-processing modules constituting the data acquisition channels; Step S122, preprocessing the electrical impedance characteristic signal through the preprocessing module, and then transmitting it to the connected analog-to-digital conversion chip to perform analog-to-digital conversion operation; Step S123, after the analog-to-digital conversion operation is completed, the obtained electrical impedance characteristic data is transmitted to the FPGA main control chip, and the FPGA main control chip implements pre-output control on the electrical impedance characteristic data of the two parallel data acquisition channels.

[0051] These steps are described below.

[0052] For each data acquisition channel, that is, in the high-resolution channel and the fast measurement channel, a preprocessing operation is performed through the preprocessing module configured by itself. The preprocessing operation includes but is not limited to noise processing to realize the front-end analog preprocessing in data acquisition. The execution of the preprocessing operation is the first processing stage of signal acquisition.

[0053] Exemplarily, the preprocessing module is used to perform hardware filtering, amplification and other processing on the electrical impedance characteristic signal to complete the first processing stage of signal acquisition, and the analog-to-digital conversion chip is used to perform analog-to-digital conversion of the preprocessed electrical impedance characteristic signal, which is an analog signal.

[0054] The electrical impedance characteristic data is obtained through the analog-to-digital conversion operation implemented by the analog-to-digital conversion chip, and the electrical impedance characteristic data is transmitted to the FPGA main control chip to execute step S130 to realize the output control of two parallel data acquisition channels.

[0055] In step S130, the FPGA (field programmable gate array) main control chip is responsible for coordinating and controlling the selection and output tasks of the two parallel data acquisition channels, and on the other hand, it will also implement data processing processes such as adaptive filtering on the electrical impedance characteristic data transmitted to the FPGA main control chip.

[0056] The FPGA master chip can process multiple data streams at the same time, such as the electrical impedance characteristic data transmitted to the FPGA master chip synchronously by two parallel data acquisition channels. It should be clear that the FPGA master chip makes a real-time selection based on the data difference between the two parallel data acquisition channels to ensure that the most appropriate data acquisition channel is selected to output the electrical impedance characteristic data.

[0057] Exemplarily, the FPGA master chip will compare the electrical impedance characteristic data from the two data acquisition channels in real time to calculate the absolute difference value of the two channel data, that is: Diff=|Data channel1 -Data channel2 |; Data channel1 and Data channel2 These are the electrical impedance characteristic data corresponding to the two data acquisition channels.

[0058] The calculated absolute difference value is compared with a preset difference threshold to obtain a size relationship between the absolute difference value and the preset difference threshold, which indicates that the absolute difference value is greater than the preset difference threshold, or the absolute difference value is not greater than the preset difference threshold.

[0059] If the absolute difference value is greater than the preset difference threshold, it means that there is a significant difference between the data of the two parallel data acquisition channels. The fast measurement channel will be selected to output the fast measurement data of the electrical impedance characteristics in order to respond quickly and reduce the calculation and processing burden.

[0060] If the absolute difference value is not greater than the preset difference threshold, it means that the data processing difference of the two parallel data acquisition channels is not large. The high-resolution channel can be selected to output high-precision electrical impedance characteristic data to ensure the accuracy and reliability of the data.

[0061] As a result, an adaptive channel selection mechanism is implemented, which intelligently adapts to the data acquisition conditions to achieve multi-resolution data output, ensures high accuracy as much as possible, and maximizes the system operation efficiency. It is particularly suitable for multi-channel data acquisition and fusion that requires both real-time and accuracy.

[0062] See also Figure 3 , Figure 3 is based on Figure 1 The corresponding embodiment shows a method flow chart describing the steps of dynamically selecting the electrical impedance characteristic data outputted by a channel corresponding to the FPGA main control chip according to the data difference between two parallel data acquisition channels.

[0063] The step S130 of the FPGA master control chip dynamically selecting the electrical impedance characteristic data outputted by one channel according to the data difference between two parallel data acquisition channels provided by the embodiment of the present invention includes: Step S131, calculating the absolute difference value between the data of two parallel data acquisition channels; Step S132 , comparing the absolute difference value with a preset difference threshold, and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold.

[0064] For further information, see Figure 4 , Figure 4 is based on Figure 3 The corresponding embodiment shows a flowchart that describes in detail the steps of comparing the absolute difference value with a preset difference threshold and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the size relationship between the absolute difference value and the preset difference threshold.

[0065] The step S132 of comparing the absolute difference value with a preset difference threshold and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold provided in the embodiment of the present invention includes: Step S1321, performing a numerical comparison between the absolute difference value and a preset difference threshold value, and obtaining a magnitude relationship between the absolute difference value and the preset difference threshold value; Step S1322, if the magnitude relationship indicates that the absolute difference value is greater than a preset difference threshold, then selecting to output the electrical impedance characteristic fast measurement data corresponding to the fast measurement channel; Step S1323: If the magnitude relationship indicates that the absolute difference value is not greater than a preset difference threshold, then the high-precision electrical impedance characteristic data corresponding to the high-resolution channel is selected for output.

[0066] Therefore, the balance between measurement accuracy and response speed can be optimized by comparing and selecting the data collected by the dual channels in real time. When the absolute difference value is greater than the preset difference threshold, the fast measurement data of the electrical impedance characteristics of the fast measurement channel is selected for output, thereby ensuring a fast response and real-time data acquisition in the electrical impedance measurement of biological tissues, which is suitable for dynamic monitoring and emergency situations.

[0067] When the absolute difference value is not greater than a preset difference threshold, the high-precision electrical impedance characteristic data of the high-resolution channel is selected for output, thereby enabling accurate diagnosis under relatively fast response speed.

[0068] This dynamic selection mechanism enables a flexible balance between measurement accuracy and response speed, thereby improving processing efficiency while ensuring accuracy, and providing multi-resolution intelligent implementation for biological tissue electrical impedance measurement.

[0069] It should be understood that the embodiments of the present invention can adaptively adjust to different sampling conditions, thereby enhancing the robustness and stability of the electrical impedance measurement of biological tissues, and can make adjustments to different biological tissues or environments under the action of a preset difference threshold to meet various electrical impedance measurement requirements, thereby greatly improving versatility.

[0070] The execution of step S130 can realize output control for two parallel data acquisition channels, so as to finally realize the output of electrical impedance characteristic data.

[0071] In an exemplary embodiment, before step S130, the method provided by the embodiment of the present invention further includes: The incoming electrical impedance characteristic data is adaptively filtered through the FPGA main control chip to obtain electrical impedance characteristic data with the effective noise bandwidth within the sampling bandwidth filtered out.

[0072] In the previous steps, the electrical impedance characteristic data was collected through parallel data acquisition channels. This data usually contains the electrical impedance response of low-impedance biological tissue at different frequencies and contains noise components. The noise may come from external interference, circuit noise, sensor error and other factors. To ensure the reliability of the data, the noise must be filtered out in subsequent processing.

[0073] The FPGA main control chip implements adaptive filtering on the incoming electrical impedance characteristic data through the configured delay device, transverse FIR adaptive filter and accumulator.

[0074] Exemplarily, the transverse FIR adaptive filter adopts a direct form FIR transverse structure, and the finite number of storage units determined by the delay level can be attributed to a finite ukir response or a transverse FIR adaptive filter.

[0075] Specifically, the incoming electrical impedance characteristic data is delayed by several delay units, and the delay time can be continuous. The output of the delay unit is multiplied with a set of stored weight coefficients in sequence, and the products are added to obtain the output, that is, the output is the total area of ​​the input data and the stored weight coefficients or impulse responses.

[0076] It should be understood that the filter structure only includes zero points, so if a cut-off frequency characteristic is to be obtained, a large number of delay units are required, and the filter structure will always be stable and can provide a linear phase characteristic.

[0077] See also Figure 5 As shown, Figure 5 The structure of a transverse FIR adaptive filter is shown according to an exemplary embodiment. The electrical impedance characteristic data output by the transverse FIR adaptive filter further filters the broadband noise within the effective noise bandwidth within the sampling bandwidth, thereby improving the sampling accuracy.

[0078] The FPGA-based adaptive filtering algorithm can effectively filter out broadband noise during data acquisition through delay processing and lateral FIR adaptive filter, and can achieve real-time noise suppression without relying on a priori noise reference input.

[0079] The electrical impedance characteristic signal output by the biological probe synchronously maps the multimodal changes in the electrical characteristics of low-impedance biological tissues in time and space.

[0080] Bioprobes are used to detect and measure the electrical properties of biological tissues (especially low-impedance biological tissues, such as brain tissue, heart, etc.) under different time and space conditions. The electrical impedance properties of low-impedance biological tissues are usually closely related to factors such as the water content, cell structure, electrolyte concentration, and metabolic state of the tissue.

[0081] When the biological probe contacts these low-impedance biological tissues, it interacts with the low-impedance biological tissues with current or voltage signals within a certain frequency range, thereby obtaining electrical impedance characteristics. By applying a specific electric field and measuring the response of the biological tissue at different frequencies, a series of electrical impedance characteristic data can be obtained, which reflects the changes in the electrical characteristics of the low-impedance biological tissues.

[0082] The electrical impedance characteristics of low-impedance biological tissues not only change over time, but are also affected by spatial position, that is, the electrical impedance characteristics of low-impedance characteristic tissues have temporal and spatial characteristics.

[0083] For example, the electrical impedance of low-impedance biological tissue may fluctuate over time as metabolism, blood flow, cell activity, or other physiological processes of the low-impedance biological tissue change. For example, during physiological activities such as brain wave activity, heartbeat cycle, breathing, etc., the electrical properties of the tissue may change instantaneously.

[0084] The electrical properties of low-impedance biological tissues will also vary at different spatial locations. For example, the electrical impedance of different regions of the brain and different parts of the heart will vary depending on the structure and function of the low-impedance biological tissue. In addition, the cell density, tissue type, water content, etc. inside the low-impedance biological tissue will also vary in space.

[0085] The changes in the electrical impedance characteristic signals output by the biological probe in time and space show a variety of different patterns, namely multimodal changes. The electrical impedance characteristic data corresponding to multimodal changes can reflect the complex physiological processes in low-impedance biological tissues. By analyzing these multimodal data, more accurate biological sign monitoring, early diagnosis of diseases, and health status assessment can be achieved, which has important medical value and application prospects.

[0086] Therefore, low-impedance biological tissue components and / or states can be accurately determined through electrical impedance characteristic data.

[0087] See also Figure 6 , Figure 6 is based on Figure 1 A method flow chart describing the steps of determining low-impedance biological tissue components and / or states based on selected output electrical impedance characteristic data is shown in the corresponding embodiment.

[0088] The step S140 of determining the low-impedance biological tissue component and / or state according to the selected output electrical impedance characteristic data provided by the embodiment of the present invention includes: Step S141, extracting features from the time-synchronized electrical impedance characteristic data to obtain local features that map the temporal and spatial changes of the electrical characteristics of the low-impedance biological tissue; Step S142, capturing the relationship between the feature dimensions mapped by each modality between the local features, and transforming and fusing the local features by capturing the obtained relationship to obtain a high-order feature expression; Step S143, performing high-order feature expression of low-impedance biological tissue component and / or state prediction to obtain description information of the measured low-impedance biological tissue, where the description information is used to describe the component and / or dynamic change of the measured low-impedance biological tissue.

[0089] These steps are described in detail below.

[0090] First of all, it should be clear that the electrical impedance characteristic data obtained by the biological probe is collected synchronously in time and space. The electrical impedance characteristic data describes the changes in the electrical characteristics of low-impedance biological tissues at different time and space locations. In practical applications, these data may include electrical impedance responses at different frequencies, reflecting the structure and functional state of biological tissues.

[0091] The electrical impedance characteristic data is multimodal data formed by the data structure of the time dimension change and the space dimension change of the electrical impedance data. In the execution of step S141, the total convolution layer of CNN is used to extract local features, and its convolution kernel slides on the data of different modes to automatically identify and extract the local changes, abnormal features and key features in the electrical impedance characteristic data, and combine them to obtain the local features of the feature changes of the low impedance characteristic tissue in space and time.

[0092] Exemplarily, the execution process of step S141 includes: adaptively performing convolution operations in each mode on the time-synchronized electrical impedance characteristic data to extract local changes, abnormal features and key features mapped in the space of low-impedance biological tissue respectively; The local changes, abnormal features and key features are combined to obtain local features that describe the characteristic changes of low-impedance biological tissue in time and space.

[0093] The execution of step S141 can extract local changes, abnormal features and key features of low-impedance biological tissue by adaptively performing convolution operations in each mode on the time-synchronized electrical impedance characteristic data, and combine these features into local features that describe the characteristic changes of low-impedance biological tissue in time and space.

[0094] By performing convolution operations in different modes, features in the time domain, frequency domain, and spatial domain can be extracted respectively. This multi-level, multi-dimensional feature extraction method can more comprehensively capture the changes in the electrical properties of low-impedance biological tissues, especially the subtle changes in dynamics and spatial distribution.

[0095] Convolution operations help to efficiently identify local changes and abnormal features in signals. In particular, when processing nonlinear changes, mutations or pathological signals of biological tissues, convolutional neural networks (CNNs) can effectively extract detailed features, allowing the system to more sensitively detect early signs of disease.

[0096] Through adaptive convolution operations, the processing mode can be dynamically adjusted according to the electrical impedance characteristics of different biological tissues and at different time points. This adaptive characteristic enables the system to adjust its analysis strategy in real time according to different physiological or pathological states, improving the accuracy of detection and diagnosis.

[0097] Changes in the electrical properties of low-impedance biological tissues are often affected by multiple factors (such as cell density, tissue hydration status, metabolic activity, etc.). The use of convolution operations can better adapt to these complex data changes and capture the complex patterns and laws hidden behind the data.

[0098] The timely capture of abnormal features is helpful for the early diagnosis of diseases. For example, through convolution operations, changes in electrical impedance characteristics caused by pathological conditions such as tumors, inflammation, and hypoxia can be discovered, thereby providing early warnings in the early stages of the disease and helping doctors take more precise intervention measures.

[0099] This process can efficiently and accurately extract the spatiotemporal characteristics of low-impedance biological tissues by adaptively performing convolution operations, and provide strong support for tissue health monitoring, disease diagnosis, treatment evaluation and personalized medicine by fusing local changes, abnormal features and key features.

[0100] In an exemplary embodiment, the execution process of step S142 includes: The multi-head attention mechanism is used to capture the relationship between the feature dimensions mapped by each modality, and obtain the relationship description features between the feature dimensions mapped by each modality in time and space; The high-order feature expression of the measured low-impedance biological tissue is obtained by performing nonlinear transformation and deep integration of local features through relational description features.

[0101] The multi-head attention mechanism focuses on local features from multiple angles, and then obtains the relationship description features between the modalities corresponding to the local features and the feature dimensions in time and space. By paying attention to the interactions between features, it extracts deeper cross-modal information, namely, relationship description features.

[0102] In the time domain, frequency domain and spatial domain, the mapping of local features in each feature dimension reflects the electrical characteristics of low-impedance organisms at different levels. On this basis, the nonlinear changes and attempted fusion of local features will realize cross-modal association learning, and then accurately capture the existing associations and fuse them into unified relationship description features. These relationship description features will provide valuable information about the relationship between tissue electrical characteristics in different dimensions (time, space, frequency, etc.), providing a basis for subsequent feature processing and diagnosis.

[0103] Local features may show certain regularities in a single dimension, but there may be complex nonlinear relationships between them, so nonlinear transformation is needed to extract deeper features.

[0104] Nonlinear transformation is used to process feature relationships that cannot be explained in a linear way. For example, by introducing activation functions (such as ReLU, sigmoid, tanh, etc.), data can be nonlinearly mapped and the ability to capture complex patterns can be enhanced.

[0105] Nonlinear transformation enables the expression of local features to more comprehensively reflect the actual state of low-impedance biological tissues, especially when the electrical impedance characteristic data is highly complex and dynamically changing. The transformed features can better reveal the health, pathology, and other states of biological tissues.

[0106] The deep integration of local features is to integrate local features at multiple levels through a deep learning network. During the integration process, multiple levels of transformation, activation and fusion operations will be applied to each local feature to capture deeper feature expressions.

[0107] The transformation from low-order features to high-order features is achieved through the integration of features at different levels. For example, some shallow features may contain local changes in electrical properties, while high-order features may reveal deeper biological tissue states and dynamic changes. Deep integration ensures that local features are comprehensively considered, thereby obtaining more comprehensive high-order features.

[0108] After deep integration, high-order feature expressions can be obtained, which is not just a simple merger of local features, but through complex nonlinear processing and cross-layer feature fusion, high-dimensional features that can fully reflect the state of low-impedance biological tissues can be generated. These high-order features can describe the electrical properties of low-impedance biological tissues, including the health status, metabolism, cell density, etc. of the tissue.

[0109] In an exemplary embodiment, after step S140, the method provided by the embodiment of the present invention further includes: The adaptation description information updates the acquisition frequency of the biological probe, and the acquisition frequency is used to control the acquisition of multimodal data.

[0110] As mentioned above, the biological probe collects the electrical properties of low-impedance biological tissues in real time to reflect the health status, metabolic status, pathological changes and other information of low-impedance biological tissues. However, different biological states, different tissue sites and different pathological processes have different requirements for data acquisition frequency. Too high or too low acquisition frequency will affect the validity and processing efficiency of the data. Therefore, dynamically adjusting the acquisition frequency according to the description information of the current low-impedance biological tissue state so that the acquisition frequency matches the actual needs is the key to improving data quality and diagnostic efficiency.

[0111] The description information is used to describe the composition and / or dynamic changes of the measured low-impedance biological tissue, and the dynamic changes include but are not limited to the functional state or health state of the low-impedance biological tissue. The described composition of the low-impedance biological tissue is used for the hierarchical structure of the biological probe in the low-impedance biological tissue to achieve the positioning of the biological probe in the low-impedance characteristic tissue.

[0112] As the biological probe moves in low-impedance biological tissues and the state of low-impedance biological tissues changes over different time periods, for example, in the early stages of a disease, the electrical properties of the tissue change slowly, while in the acute stage of the disease, the changes may increase significantly. Therefore, the acquisition frequency needs to be flexibly adjusted to avoid wasting resources and ensure the timeliness and accuracy of the data.

[0113] By adapting the description information, the frequency of data collection can be dynamically adjusted to ensure that data collection meets real-time requirements while avoiding the collection of excessive useless data.

[0114] As mentioned above, data of low-impedance biological tissues is collected through biological probes to obtain real-time impedance characteristic data such as impedance, temperature, and pressure. After preprocessing (such as filtering, denoising, etc.), these data are analyzed through algorithms (such as state analysis methods based on machine learning or rules) to analyze the current state of low-impedance tissues.

[0115] This can identify whether low-impedance biological tissue has acute lesions or abnormal conditions. For example, in the acute phase of certain diseases, the electrical impedance characteristic signal of the tissue changes greatly, while in the recovery phase, the change is small. In short, the need for acquisition frequency can be determined based on these analysis results.

[0116] When it is determined based on the descriptive information that the low-impedance biological tissue is in the stage of acute lesions, rapid changes, or dynamic responses (such as the acute stage of tumors and inflammation), the acquisition frequency needs to be increased. This is because in this case, the electrical impedance characteristic signal changes rapidly, and a higher time resolution is required to capture subtle changes.

[0117] For example, the acquisition frequency of a biological probe may be increased from once per second to five times per second, or in certain acute pathological conditions, the frequency may even be increased to ten times per second or more.

[0118] If it is determined based on the description information that the electrical characteristics of low-impedance biological tissues change relatively steadily, or are in a recovery period or healthy state (such as stable metabolic activity or no obvious abnormality), the acquisition frequency can be reduced. This can save computing resources and improve efficiency.

[0119] For example, in a healthy state, the sampling frequency can be reduced to once per minute, or, depending on actual needs, to twice per second.

[0120] This not only improves resource utilization, but also ensures that sufficient key information is captured, improving the effectiveness of monitoring, diagnosis and treatment. This dynamic and intelligent acquisition frequency adjustment method is of great significance to the real-time, sensitivity and efficiency of biomedical data acquisition.

[0121] It should be further explained that the electrical impedance characteristic signal is the output of structural measurement of different levels within the measured low-impedance biological tissue.

[0122] For example, in low-impedance biological tissues such as brain tissue, the output electrical impedance characteristic signal can be used to locate the current level of the biological probe, for example, whether it is the hemorrhage area of ​​the brain tissue, or the gray matter, white matter, cerebrospinal fluid, etc., thereby accurately locating the diagnosis and treatment without the assistance of other equipment.

[0123] It is further clarified that the electrical impedance characteristic data obtained corresponding to the electrical impedance characteristic signal can be data representing the rate of change obtained during the acquisition and processing process, such as the rate of change corresponding to the previous time and the current time, so as to be further applicable to low-impedance biological tissues and enhance the accuracy of measurement.

[0124] In an exemplary embodiment, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned method.

[0125] In an exemplary embodiment, the present invention further provides a computer program product, comprising a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0126] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present invention.

[0127] In an exemplary embodiment of the present invention, a computer program medium is further provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the method described in the above method embodiment.

[0128] According to one embodiment of the present invention, a program product for implementing the method in the above method embodiment is also provided, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited to this. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0129] The program product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0130] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0132] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0133] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0134] In addition, although the steps of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0135] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation method of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation method of the present invention.

[0136] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the appended claims.

Claims

1. An adaptive multi-resolution method for testing low impedance tissue, characterized in that: The method comprises: Measuring the electrical impedance of low-impedance biological tissue by using a biological probe, and outputting an electrical impedance characteristic signal in real time, wherein the electrical impedance characteristic signal represents a change in the electrical characteristics of the measured low-impedance biological tissue; Implementing dual-channel acquisition of the electrical impedance characteristic signal so that the electrical impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, wherein the parallel data acquisition channels include a high-resolution channel and a fast measurement channel; The FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by one channel according to the data difference between the two parallel data acquisition channels, wherein the electrical impedance characteristic data is high-precision electrical impedance characteristic data or electrical impedance characteristic fast measurement data; The low impedance biological tissue component and / or state is determined based on the selected output electrical impedance characteristic data.

2. The method according to claim 1, characterized in that The two parallel data acquisition channels both implement dual-channel acquisition of the electrical impedance characteristic signal by configuring a preprocessing module and an analog-to-digital conversion chip, so that the electrical impedance characteristic signal is synchronously transmitted to the two parallel data acquisition channels, including: The electrical impedance characteristic signal is sampled in parallel through two configured parallel data acquisition channels, so that the electrical impedance characteristic signal is synchronously transmitted to two pre-processing modules constituting the data acquisition channels; The preprocessing module performs a preprocessing operation on the electrical impedance characteristic signal, and then transmits the signal to the connected analog-to-digital conversion chip to perform an analog-to-digital conversion operation; After the analog-to-digital conversion operation is completed, the obtained electrical impedance characteristic data is transmitted to the FPGA main control chip, and the FPGA main control chip implements pre-output control of the electrical impedance characteristic data of the two parallel data acquisition channels.

3. The method according to claim 1, characterized in that Before the FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by a channel according to the data difference between the two parallel data acquisition channels, the method further includes: The FPGA main control chip performs adaptive filtering on the input electrical impedance characteristic data to obtain electrical impedance characteristic data with bandwidth noise of the effective noise bandwidth within the sampling bandwidth filtered out.

4. The method according to claim 1, characterized in that The FPGA main control chip dynamically selects the electrical impedance characteristic data outputted by a channel according to the data difference between the two parallel data acquisition channels, including: Calculate the absolute difference between the data of two parallel data acquisition channels; A comparison is performed between the absolute difference value and a preset difference threshold, and electrical impedance characteristic data corresponding to the output of a channel is selected according to a magnitude relationship between the absolute difference value and the preset difference threshold.

5. The method according to claim 4, characterized in that The comparing the absolute difference value with a preset difference threshold value, and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold value, comprises: Performing a numerical comparison between the absolute difference value and a preset difference threshold value to obtain a magnitude relationship between the absolute difference value and the preset difference threshold value; If the magnitude relationship indicates that the absolute difference value is greater than a preset difference threshold, the electrical impedance characteristic fast measurement data corresponding to the fast measurement channel is selected for output.

6. The method according to claim 5, characterized in that The comparing the absolute difference value with a preset difference threshold value, and selecting the electrical impedance characteristic data outputted corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold value, further comprises: If the magnitude relationship indicates that the absolute difference value is not greater than a preset difference threshold, the high-precision electrical impedance characteristic data corresponding to the high-resolution channel is selected for output.

7. The method according to claim 1, characterized in that The electrical impedance characteristic signal synchronously maps the multi-modal changes of the electrical characteristics of the low-impedance biological tissue that vary in time and space.

8. An adaptive multi-resolution system for testing low impedance tissue, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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