Adaptive Multi-Resolution Methods, Systems, and Products for Testing Low-Impedance Tissues

Abstract

The present invention provides an adaptive multi-resolution method, system and product for testing low-impedance tissues, which relates to the technical field of impedance measurement, and solves the technical problem of quickly obtaining sufficient impedance data while ensuring high precision as much as possible during the process of bioelectrical impedance testing. The solution is as follows: First, impedance measurement of low-impedance biological tissues is performed through a biological probe, and impedance characteristic signals are output in real time; second, dual-channel acquisition of the impedance characteristic signals is implemented; third, the FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels; finally, the composition and / or state of the low-impedance biological tissues are determined according to the selected output impedance characteristic data. The present invention realizes adaptive multi-resolution data acquisition and processing of low-impedance biological tissues through dual-channel acquisition and an intelligent data selection mechanism, and further accurately judges the composition and state of low-impedance biological tissues.

Description

Technical Field

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

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

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

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

[0005] According to an aspect of an embodiment of the present invention, an adaptive multi-resolution method for testing low-impedance tissues is disclosed. The method includes:

[0006] Performing impedance measurement on low-impedance biological tissues through a biological probe and real-time outputting an impedance characteristic signal, where the impedance characteristic signal characterizes the electrical characteristic changes of the measured low-impedance biological tissues;

[0007] Performing dual-channel acquisition on the impedance characteristic signal, so that the 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;

[0008] An FPGA master control chip dynamically selects the impedance characteristic data output corresponding to one channel according to the data difference between the two parallel data acquisition channels, where the impedance characteristic data is high-precision impedance characteristic data or impedance characteristic fast measurement data; [[ID=3B]]

[0009] Determining the composition and / or state of the low-impedance biological tissues according to the selected output impedance characteristic data.

[0010] According to an aspect of an embodiment of the present invention, both of the two parallel data acquisition channels are configured with a preprocessing module and an analog-to-digital conversion chip. The performing dual-channel acquisition on the impedance characteristic signal, so that the impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels, includes:

[0011] The sampling operation is performed on the impedance characteristic signal in parallel through the two configured parallel data acquisition channels, so that the impedance characteristic signal is synchronously transmitted to the two preprocessing modules constituting the data acquisition channels;

[0012] Through the preprocessing operation of the preprocessing module on the impedance characteristic signal, it is then transmitted to the connected analog-to-digital conversion chip to perform the analog-to-digital conversion operation;

[0013] After the analog-to-digital conversion operation is completed, the obtained impedance characteristic data is transmitted into the FPGA main control chip, and the FPGA main control chip performs pre-output control on the impedance characteristic data of the two parallel data acquisition channels.

[0014] According to an aspect of an embodiment of the present invention, before the FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels, the method further includes:

[0015] The FPGA main control chip performs adaptive filtering on the incoming impedance characteristic data to obtain impedance characteristic data with the bandwidth noise within the effective noise bandwidth in the sampling bandwidth filtered out.

[0016] According to an aspect of an embodiment of the present invention, the FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels, including:

[0017] Calculating the absolute difference value between the data of the two parallel data acquisition channels;

[0018] Comparing the absolute difference value with a preset difference threshold, and selecting the impedance characteristic data corresponding to one channel for output according to the magnitude relationship between the absolute difference value and the preset difference threshold.

[0019] According to an aspect of an embodiment of the present invention, the comparing the absolute difference value with a preset difference threshold and selecting the impedance characteristic data corresponding to one channel for output according to the magnitude relationship between the absolute difference value and the preset difference threshold includes:

[0020] Performing a numerical comparison between the absolute difference value and the preset difference threshold to obtain the magnitude relationship between the absolute difference value and the preset difference threshold;

[0021] If the magnitude relationship indicates that the absolute difference value is greater than the preset difference threshold, then select and output the impedance characteristic fast measurement data corresponding to the fast measurement channel.

[0022] According to one aspect of an embodiment of the present invention, the comparison between the absolute difference value and a preset difference threshold, and the selection of the impedance characteristic data corresponding to one channel according to the magnitude relationship between the absolute difference value and the preset difference threshold further include:

[0023] If the magnitude relationship indicates that the absolute difference value is not greater than the preset difference threshold, then select and output the high-precision impedance characteristic data corresponding to the high-resolution channel.

[0024] According to one aspect of an embodiment of the present invention, the impedance characteristic signal synchronously maps in time the multimodal changes in the electrical characteristics of the low-impedance biological tissue's spatio-temporal changes.

[0025] According to one aspect of an embodiment of the present invention, an adaptive multi-resolution system for testing low-impedance tissues is disclosed, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method as described above.

[0026] According to one aspect of an embodiment of the present invention, a computer program product is disclosed, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method as described in any one of the above.

[0027] Embodiments of the present invention are used to measure in real time the impedance characteristics of low-impedance biological tissues, and realize adaptive multi-resolution data acquisition and processing of low-impedance biological tissues through dual-channel acquisition and an intelligent data selection mechanism, and then accurately judge the composition and state of low-impedance biological tissues. [[ID=!--]]

[0028] Through dual-channel acquisition and dynamic channel selection, this solution can improve the data acquisition speed while maintaining high measurement accuracy. Traditional single measurement channels often need to make a trade-off between high precision and fast measurement, while this solution enables the system to automatically switch according to actual needs in different situations by flexibly selecting high-resolution channels or fast measurement channels, which can not only ensure the high precision of measurement data but also provide sufficient real-time performance to meet the dynamic monitoring requirements in clinical or experimental settings.

[0029] By using an FPGA master control chip to dynamically analyze and select the data differences between two parallel acquisition channels, the system can real-time judge and quickly switch to the most suitable measurement channel. This intelligent selection mechanism greatly enhances the system's response ability to changes in the state of low-impedance biological tissues, enabling impedance measurement not only limited to static monitoring but also adaptable to rapidly changing physiological states, providing more accurate and reliable real-time data, and being applicable to scenarios such as dynamic monitoring and clinical diagnosis.

[0030] Through the intelligent algorithm of the FPGA master control chip, it is able to automatically select the best channels and measurement parameters, eliminate unnecessary noise, and ensure that the acquired data quality is more stable and reliable. This is crucial for improving the accuracy of measurement results, especially in complex medical environments, and can significantly improve the accuracy and efficiency of diagnosis.

[0031] Based on the 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, bleeding areas, etc.). By analyzing and processing high-resolution or fast measurement data, it is possible to infer the composition, density, and health status of low-impedance biological tissues according to the impedance data characteristics. For example, in medical diagnosis, it can detect potential pathological problems in advance by real-time monitoring the impedance changes of brain tissue, heart tissue, etc., and achieve early diagnosis and warning.

[0032] Due to the adoption of the adaptive multi-resolution technology, the embodiments of the present invention can automatically adjust the working mode according to different measurement requirements and environmental conditions. For example, based on the signal processing status, when high-precision diagnosis is needed, the system automatically selects the high-resolution channel; when the implementation of high resolution takes a long time and switches to quickly obtaining a large amount of data, it automatically switches to the fast measurement channel. This flexible adaptability can be widely applied to different types of low-impedance biological tissue tests and different medical scenarios, greatly enhancing the universality and applicability of the technology.

[0033] Furthermore, the embodiments of the present invention use the FPGA master control chip to control dual-channel acquisition and data processing, and implement parallel computing and adaptive adjustment on the hardware platform. This efficient hardware architecture can effectively reduce the required processor resources, lower the overall hardware cost, and no longer require image detection. At the same time, the programmability of the FPGA master control chip enables the system to flexibly adjust the working mode according to actual needs, enhancing the scalability and customization ability of the system.

[0034] Other features and advantages of the present invention will become apparent through the following detailed description, or be learned in part through the practice of the present invention.

[0035] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present invention. Brief Description of the Drawings

[0036] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other objectives, features, and advantages of the present invention will become more apparent.

[0037] Figure 1 is a flowchart of an adaptive multi-resolution method for testing low-impedance tissues shown according to an exemplary embodiment.

[0038] Figure 2 is based on Figure 1 the method flowchart described by the steps of performing dual-channel acquisition on the impedance characteristic signal, so that the 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, as shown in the corresponding embodiment.

[0039] Figure 3 is based on Figure 1 the method flowchart described by the steps of the FPGA master control chip dynamically selecting the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels, as shown in the corresponding embodiment.

[0040] Figure 4 is based on Figure 3 the flowchart that elaborates in detail the steps of comparing the absolute difference value with a preset difference threshold and selecting the impedance characteristic data corresponding to one channel according to the magnitude relationship between the absolute difference value and the preset difference threshold, as shown in the corresponding embodiment.

[0041] Figure 5 is a lateral FIR adaptive filter structure shown according to an exemplary embodiment.

[0042] Figure 6 is based on Figure 1 the method flowchart described by the steps of determining the low-impedance biological tissue components and / or state according to the selected output impedance characteristic data, as shown in the corresponding embodiment. Detailed implementation manners

[0043] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various 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 complete and comprehensive, and the concept of the example embodiments will be fully conveyed to those skilled in the art. The drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted.

[0044] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to give a thorough understanding of the example embodiments of the present invention. However, those skilled in the art will realize that one or more of the specific details can be omitted in practicing the technical solutions of the present invention, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring the various aspects of the present invention.

[0045] Refer to Figure 1 , Figure 1 which is a flowchart of an adaptive multi - resolution method for testing low - impedance tissue shown according to an exemplary embodiment.

[0046] The adaptive multi - resolution method for testing low - impedance tissue provided by the embodiments of the present invention includes the following steps:

[0047] Step S110, measure the impedance of low - impedance biological tissue through a biological probe, and output an impedance characteristic signal in real time. The impedance characteristic signal characterizes the change in the electrical characteristics of the measured low - impedance biological tissue;

[0048] Step S120, perform two - channel acquisition on the impedance characteristic signal, so that the impedance characteristic signal is synchronously transmitted to two parallel data acquisition channels. The parallel data acquisition channels include a high - resolution channel and a fast - measurement channel;

[0049] Step S130, the FPGA main control chip dynamically selects the impedance characteristic data output corresponding to one channel according to the data difference between the two parallel data acquisition channels. The impedance characteristic data is high - precision impedance characteristic data or impedance characteristic fast - measurement data;

[0050] Step S140, determine the composition and / or state of the low - impedance biological tissue according to the selected output impedance characteristic data.

[0051] The following elaborates on these steps in detail.

[0052] First of all, it should be noted that the biological probe is various sensors or other devices used to measure the impedance of low - impedance biological tissue. Exemplarily, it can be composed of electrodes to introduce an electrical signal (current or voltage) into the low - impedance biological tissue and receive a feedback signal to output an impedance characteristic signal.

[0053] The biological probe contacts the low - impedance biological tissue, applies a weak alternating - current signal, and measures the response of the current in the low - impedance biological tissue. Different electrical characteristics (such as resistance, conductivity, capacitance, etc.) of low - impedance biological tissue will cause different ways of current passing, so the impedance characteristic signal returned by the probe can reflect the electrical characteristics of the low - impedance biological tissue.

[0054] The impedance characteristic signal is the signal collected by the biological probe, which reflects the impedance change of the measured low - impedance biological tissue. By measuring the response of the low - impedance biological tissue to currents of different frequencies, a frequency spectrum of impedance can be obtained, and this frequency spectrum characterizes the electrical characteristics of the tissue. The impedance characteristic signal can provide information about the conductivity, structural characteristics, moisture content, ion concentration, etc. of the low - impedance biological tissue.

[0055] The object of the electrical impedance measurement is low-impedance biological tissue, which has a lower electrical impedance value than 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.

[0056] In other words, non-high-impedance biological tissue is what is referred to as low-impedance biological tissue in this disclosure. It should be understood that electrical impedance refers to the degree to which biological tissue hinders the flow of electric current and is used to describe the response of biological tissue to electrical signals. Electrical impedance consists of two components: resistance and reactance. Resistance reflects the conductivity of a material, while reactance is related to frequency, as well as the capacitance and inductance of the tissue. In biological tissue, low impedance means that the tissue presents relatively little resistance to electric current, allowing current to flow easily.

[0057] Thus, for example, low-impedance biological tissues may be brain tissue, muscle tissue, blood, nerve tissue, and organs such as the liver and kidneys.

[0058] The medium or low impedance range for low-impedance biological tissue is defined as compared to the high impedance range. The high impedance range corresponds to impedance values where current flow is significantly hindered. For example, bone and adipose tissue fall within the high impedance range. Furthermore, relatively low impedance values, such as those in the medium and low impedance ranges, are considered low-impedance biological tissue.

[0059] Electrical impedance measurement is performed on low-impedance biological tissues through biological probes, and the tissue composition and status are analyzed and judged based on the subtle electrical impedance characteristics obtained, thereby providing strong data support for medical diagnosis and biophysical research.

[0060] 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.

[0061] In step S120, dual-channel acquisition refers to collecting data simultaneously through two independent channels. 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 constituent data acquisition channels.

[0062] The two parallel data acquisition channels for realizing dual-channel acquisition include a high-resolution channel and a fast measurement channel. Among them, the high-resolution channel is used to accurately capture the details of the impedance characteristic signal and provide the highest possible measurement accuracy. Exemplarily, the high-resolution channel will adopt a higher sampling rate and a smaller range to capture the tiny changes in the impedance characteristic signal, so as to obtain finer data.

[0063] The fast measurement channel is used to improve the speed of data acquisition and achieve fast response. Compared with the high-resolution channel, it sacrifices a part of the measurement accuracy, and instead has a higher sampling frequency and a larger range, so as to quickly obtain the impedance characteristic data.

[0064] After the two parallel data acquisition channels perform dual-channel acquisition on the impedance characteristic signal, the output data will be transmitted to the FPGA main control chip, and then the output control of the channel will be realized as shown in step S130.

[0065] Furthermore, for the parallel data acquisition channels, whether it is the high-resolution channel or the fast measurement channel, they both pass through their respective configured preprocessing modules and analog-to-digital conversion chips. In other words, the high-resolution channel and the fast measurement channel are constructed by configuring the preprocessing module and the analog-to-digital conversion chip. Between the high-resolution channel and the fast measurement channel, the two preprocessing 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.

[0066] Please also refer to Figure 2 , Figure 2 which is Figure 1 a method flowchart corresponding to the embodiment shown for performing dual-channel acquisition on the impedance characteristic signal, so that the impedance characteristic signal is synchronously transmitted to the two parallel data acquisition channels, and the parallel data acquisition channels include the high-resolution channel and the fast measurement channel steps for description.

[0067] Step S120 of the dual-channel acquisition of the impedance characteristic signal provided by the embodiment of the present invention, so that the impedance characteristic signal is synchronously transmitted to the two parallel data acquisition channels, and the parallel data acquisition channels include the high-resolution channel and the fast measurement channel, includes:

[0068] Step S121, performing a sampling operation on the impedance characteristic signal in parallel through the two configured parallel data acquisition channels, so that the impedance characteristic signal is synchronously transmitted to the two preprocessing modules constituting the data acquisition channel;

[0069] Step S122, performing a preprocessing operation on the impedance characteristic signal through the preprocessing module, and then transmitting it to the connected analog-to-digital conversion chip to perform an analog-to-digital conversion operation;

[0070] In 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 performs pre-output control on the electrical impedance characteristic data of the two parallel data acquisition channels.

[0071] These steps are explained below.

[0072] For each data acquisition channel, that is, in the high-resolution channel and the fast measurement channel, preprocessing operations are performed through the preprocessing module configured by itself. The preprocessing operations include but are not limited to noise processing to achieve front-end analog preprocessing in data acquisition. The execution of preprocessing operations is the first processing stage of signal acquisition.

[0073] 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 then hand it over to the analog-to-digital conversion chip to perform analog-to-digital conversion of the preprocessed electrical impedance characteristic signal, which is an analog signal.

[0074] 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 output control of two parallel data acquisition channels.

[0075] 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.

[0076] The FPGA master chip can process multiple data streams simultaneously, such as the electrical impedance characteristic data transmitted simultaneously to the FPGA master chip by two parallel data acquisition channels. It should be clear that the FPGA master chip makes a real-time selection based on the data differences between the two parallel data acquisition channels to ensure that the most appropriate data acquisition channel is selected for outputting the electrical impedance characteristic data.

[0077] For example, the FPGA master chip compares the electrical impedance characteristic data from the two data acquisition channels in real time to calculate the absolute difference between the two channel data, namely:

[0078] Diff=|Data channel1 -Data channel2 |;

[0079] Data channel1 and Data channel2 These are the electrical impedance characteristic data corresponding to the two data acquisition channels.

[0080] The calculated absolute difference value is compared with a preset difference threshold to obtain the magnitude relationship between the absolute difference value and the preset difference threshold, and this magnitude relationship 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.

[0081] The fact that the absolute difference value is greater than the preset difference threshold means that there are significant differences in the data of the two parallel data acquisition channels. The fast measurement channel will be selected to output the impedance characteristic fast measurement data for quick response and reduce the calculation and processing burden.

[0082] If the absolute difference value is not greater than the preset difference threshold, it means that the data processing differences between the two parallel data acquisition channels are not significant. The high-resolution channel can be selected to output high-precision impedance characteristic data to ensure the accuracy and reliability of the data.

[0083] Thus, an adaptive channel selection mechanism is implemented, which intelligently adapts to the data acquisition situation to achieve multi-resolution data output, ensuring high precision as much as possible and maximizing the system operation efficiency. It is especially suitable for multi-channel data acquisition and fusion that requires both real-time performance and accuracy.

[0084] Please also refer to Figure 3 , Figure 3 which is based on Figure 1 The method flowchart corresponding to the embodiment shows the steps of the impedance characteristic data output corresponding to one channel dynamically selected by the FPGA main control chip according to the data difference between the two parallel data acquisition channels.

[0085] Step S130 of the impedance characteristic data output corresponding to one channel dynamically selected by the FPGA main control chip according to the data difference between the two parallel data acquisition channels provided by the embodiment of the present invention includes:

[0086] Step S131, calculating the absolute difference value between the data of the two parallel data acquisition channels;

[0087] Step S132, comparing the absolute difference value with the preset difference threshold, and selecting the impedance characteristic data output corresponding to one channel according to the magnitude relationship between the absolute difference value and the preset difference threshold.

[0088] Further, refer to Figure 4 , Figure 4 which is based on Figure 3 The flowchart corresponding to the embodiment shows a detailed description of the steps of comparing the absolute difference value with the preset difference threshold and selecting the impedance characteristic data output corresponding to one channel according to the magnitude relationship between the absolute difference value and the preset difference threshold.

[0089] Step S132 of comparing the absolute difference value with a preset difference threshold and selecting the impedance characteristic data corresponding to one channel for output according to the magnitude relationship between the absolute difference value and the preset difference threshold provided by the embodiment of the present invention includes:

[0090] Step S1321, perform a numerical comparison between the absolute difference value and the preset difference threshold to obtain the magnitude relationship between the absolute difference value and the preset difference threshold;

[0091] Step S1322, if the magnitude relationship indicates that the absolute difference value is greater than the preset difference threshold, then select and output the fast measurement data of the impedance characteristic corresponding to the fast measurement channel;

[0092] Step S1323, if the magnitude relationship indicates that the absolute difference value is not greater than the preset difference threshold, then select and output the high-precision impedance characteristic data corresponding to the high-resolution channel.

[0093] Thus, through the comparison and selection performed in real time on the data collected by the two channels, the balance between the measurement accuracy and the response speed is optimized. When the absolute difference value is greater than the preset difference threshold, the fast measurement data of the impedance characteristic of the fast measurement channel is selected and output, ensuring fast response and real-time data acquisition in the impedance measurement of biological tissues, and is applicable to dynamic monitoring and emergency situations.

[0094] When the absolute difference value is not greater than the preset difference threshold, the high-precision impedance characteristic data of the high-resolution channel is selected and output, and thus accurate diagnosis can be achieved under the condition that the response speed is relatively fast.

[0095] Through this dynamic selection mechanism, the measurement accuracy and the response speed can be flexibly balanced, thereby improving the processing efficiency while ensuring the accuracy, and providing an intelligent implementation with multiple resolutions for the impedance measurement of biological tissues.

[0096] [[ID=,21]]It should be understood that, based on the embodiment of the present invention, it can be adaptively adjusted for different sampling situations, enhancing the robustness and stability of the impedance measurement of biological tissues, and can be adjusted for different biological tissues or environments under the action of the preset difference threshold to meet various impedance measurement requirements, greatly improving the versatility.

[0097] The execution of step S130 enables the output control for two parallel data acquisition channels to finally achieve the output of the impedance characteristic data.

[0098] In an exemplary embodiment, before step S130, the method provided by the embodiment of the present invention further includes:

[0099] The FPGA master control chip performs adaptive filtering on the incoming impedance characteristic data to obtain impedance characteristic data with the effective noise bandwidth within the sampling bandwidth filtered out.

[0100] In the previous steps, impedance characteristic data was collected through the parallel data acquisition channels. This data usually contains the impedance responses of low-impedance biological tissues at different frequencies and includes noise components. The noise may come from factors such as external interference, circuit noise, and sensor errors. To ensure the reliability of the data, the noise must be filtered out in subsequent processing.

[0101] The FPGA master control chip performs adaptive filtering on the incoming impedance characteristic data, which is achieved through the configured delay unit, transversal FIR adaptive filter, and accumulator.

[0102] Exemplarily, the transversal FIR adaptive filter adopts the direct form of the FIR transversal structure. The finite number of storage units determined by the delay stages can be attributed to the finite impulse response or the transversal FIR adaptive filter.

[0103] Specifically, the incoming impedance characteristic data is delayed by several delay units. The delay time can be continuous. The outputs of the delay units are successively multiplied by a stored set of weight coefficients, 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 response.

[0104] It should be understood that this filtering structure only contains zeros. Therefore, to obtain the cut-off frequency characteristics, a large number of delay units are required, and this filtering structure will always be stable and can provide a linear phase characteristic.

[0105] Refer to Figure 5 as shown in Figure 5 is the transversal FIR adaptive filter structure shown according to an exemplary embodiment. The impedance characteristic data output through the transversal FIR adaptive filter further filters the broadband noise within the effective noise bandwidth within the sampling bandwidth, thereby improving the sampling accuracy.

[0106] The adaptive filtering algorithm based on FPGA can effectively filter out broadband noise during the data acquisition process through delay processing and the transversal FIR adaptive filter, and can achieve real-time noise suppression without relying on a priori noise reference input.

[0107] The impedance characteristic signal output by the biological probe synchronously maps the multimodal changes in the electrical characteristics of the spatio-temporal changes of low-impedance biological tissues in time.

[0108] Biological probes 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 impedance characteristics of low-impedance biological tissues are usually closely related to factors such as tissue water content, cell structure, electrolyte concentration, and metabolic state.

[0109] When the biological probe comes into contact with 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 impedance characteristics. By applying a specific electric field and measuring the response of the biological tissue at different frequencies, a series of impedance characteristic data can be obtained, and these data reflect the changes in the electrical properties of the low-impedance biological tissues.

[0110] The impedance characteristics of low-impedance biological tissues not only change over time but are also affected by spatial position, that is, the impedance characteristics of low-impedance characteristic tissues have spatio-temporal characteristics.

[0111] Exemplarily, as the metabolism, blood flow, cell activity, or other physiological processes of low-impedance biological tissues change, the impedance of low-impedance biological tissues fluctuates over time. For example, during physiological activities such as brain wave activity, heartbeat cycle, and breathing, the electrical properties of tissues change instantaneously.

[0112] The electrical properties of low-impedance biological tissues also vary at different spatial positions. For example, different regions of the brain, different parts of the heart, etc., the impedance varies with the structure and function of the low-impedance biological tissues. In addition, the cell density, tissue type, water content, etc. inside the low-impedance biological tissues also vary spatially.

[0113] The changes in the impedance characteristic signals output by the biological probe in time and space show various different patterns, that is, multimodal changes. The impedance characteristic data corresponding to the multimodal changes can reflect the complex physiological processes within the low-impedance biological tissues. By analyzing these multimodal data, more accurate biological sign monitoring, early disease diagnosis, and health status assessment can be achieved, which has important medical value and application prospects.

[0114] Therefore, the components and / or states of low-impedance biological tissues can be accurately determined through impedance characteristic data.

[0115] Please also refer to Figure 6 , Figure 6 is a method flowchart for describing the steps of determining the components and / or states of low-impedance biological tissues based on the impedance characteristic data selected according to the corresponding embodiments shown. Figure 1

[0116] ​Step S140 of determining the low-impedance biological tissue components and / or states according to the impedance characteristic data output by selection provided by the embodiments of the present invention includes:

[0117] Step S141, performing feature extraction on the impedance characteristic data synchronized in time to obtain local features mapping the spatio-temporal changes of the electrical characteristics of the low-impedance biological tissue;

[0118] Step S142, capturing the relationships between the feature dimensions mapped by each modality among the local features, and performing transformation and fusion of the local features through the captured relationships to obtain a high-order feature representation;

[0119] Step S143, predicting the low-impedance biological tissue components and / or states of the high-order feature representation to obtain the description information of the measured low-impedance biological tissue, where the description information is used to describe the components and / or dynamic changes of the measured low-impedance biological tissue.

[0120] The following elaborates on these steps in detail.

[0121] First, it should be clear that the impedance characteristic data obtained through the biological probe is collected through time synchronization and space synchronization. The impedance characteristic data describes the changes of the electrical characteristics of the low-impedance biological tissue at different spatio-temporal positions. In practical applications, this data may include impedance responses at different frequencies, reflecting the structural and functional states of the biological tissue.

[0122] The impedance characteristic data is multi-modal data formed by the data structures of the impedance data changes in the time dimension and the space dimension. In the execution of step S141, the total convolutional layer of the CNN is used for local feature extraction, and its convolutional kernel slides on the data of different modalities, automatically identifying and extracting the local changes, abnormal features, and key features in the impedance characteristic data, and combining them to obtain the local features of the spatio-temporal feature changes of the low-impedance characteristic tissue.

[0123] Exemplarily, the execution process of step S141 includes: adaptively performing convolutional operations in each modality on the impedance characteristic data synchronized in time to extract the local changes, abnormal features, and key features respectively mapping the low-impedance biological tissue in space;

[0124] Combining the obtained local changes, abnormal features, and key features to obtain the local features describing the spatio-temporal feature changes of the low-impedance biological tissue.

[0125] The execution of step S141 can adaptively perform convolutional operations in each modality on the impedance characteristic data synchronized in time, extract the local changes, abnormal features, and key features of the low-impedance biological tissue, and combine these features into local features describing the spatio-temporal feature changes of the low-impedance biological tissue.

[0126] By performing convolution operations in different modalities, features in the time domain, frequency domain, and spatial domain can be extracted respectively. This multi-level and 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.

[0127] Convolution operations help to efficiently identify local changes and abnormal features in signals. Especially when dealing with non-linear changes, mutations, or diseased signals in biological tissues, convolutional neural networks (CNNs) can effectively extract detailed features, enabling the system to detect early signs of diseases more sensitively.

[0128] Through adaptive convolution operations, the processing method can be dynamically adjusted according to the impedance characteristics of different biological tissues and different time points. This adaptive feature 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.

[0129] The changes in the electrical properties of low-impedance biological tissues are often affected by various factors (such as cell density, tissue hydration status, metabolic activity, etc.). Using convolution operations can better adapt to these complex data changes and capture the complex patterns and rules hidden behind the data.

[0130] The timely capture of abnormal features contributes to the diagnosis of early diseases. For example, through convolution operations, changes in impedance characteristics caused by pathological states such as tumors, inflammation, and hypoxia can be detected, providing early warnings in the initial stage of diseases and helping doctors take more precise intervention measures.

[0131] This process can efficiently and accurately extract the spatio-temporal features of low-impedance biological tissues by adaptively performing convolution operations, and by fusing local changes, abnormal features, and key features, it provides strong support for tissue health monitoring, disease diagnosis, treatment evaluation, and personalized medicine.

[0132] In an exemplary embodiment, the execution process of step S142 includes:

[0133] Capturing the relationships between the feature dimensions mapped by each modality through a multi-head attention mechanism to obtain relationship description features between the feature dimensions mapped by each modality in time and space;

[0134] Performing non-linear transformation and deep integration of local features through the relationship description features to obtain a high-order feature expression of the measured low-impedance biological tissue.

[0135] The multi-head attention mechanism focuses on local features from multiple perspectives, thereby obtaining a relationship description feature that corresponds to each modality and spatio-temporal feature dimension of the local features. By paying attention to the interaction between features, deeper cross-modal information, i.e., the relationship description feature, is extracted.

[0136] 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 non-linear variation and attempted fusion of local features will achieve cross-modal correlation learning, and then the existing correlations will be accurately captured and fused into a unified relationship description feature. These relationship description features will provide valuable information about the mutual relationship between the electrical characteristics of tissues in different dimensions (time, space, frequency, etc.), providing a basis for subsequent feature processing and diagnosis.

[0137] Local features may exhibit certain patterns in a single dimension, but there may be complex non-linear relationships between them. Therefore, non-linear transformation is required to extract deeper features.

[0138] Non-linear transformation is used to handle feature relationships that cannot be explained linearly. Exemplarily, by introducing activation functions (such as ReLU, sigmoid, tanh, etc.), non-linear mapping of data can be performed, enhancing the ability to capture complex patterns.

[0139] Non-linear transformation enables the expression of local features to more comprehensively reflect the actual state of low-impedance biological tissues, especially in the case where impedance characteristic data is highly complex and dynamically changing. The transformed features can better reveal the health, lesion, and other states of biological tissues.

[0140] The deep integration of local features is carried out through a deep learning network, integrating local features at multiple levels. During the integration process, multiple levels of transformation, activation, and fusion operations will be applied to each local feature to capture deeper feature expressions.

[0141] The transformation from low-order features to high-order features is achieved through feature integration at different levels. Exemplarily, some shallow features may contain local electrical characteristic changes, 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.

[0142] After deep integration, high-order feature expressions can be obtained. This is not just a simple combination of local features, but through complex non-linear processing and cross-layer feature fusion, high-dimensional features that can comprehensively reflect the state of low-impedance biological tissues are generated. These high-order features can describe the electrical characteristics of low-impedance biological tissues, including the health state, metabolic situation, cell density, etc. of the tissues.

[0143] In an exemplary embodiment, after step S140, the method provided by the embodiments of the present invention further includes:

[0144] Adapting the description information to update the acquisition frequency of the biological probe, and the acquisition frequency is used to control the acquisition of multimodal data.

[0145] As described above, the biological probe collects the electrical characteristics of low-impedance biological tissues in real time to reflect information such as the health status, metabolic state, and pathological changes of low-impedance biological tissues. However, different biological states, different tissue sites, and different pathological processes have different requirements for the data acquisition frequency. Too high or too low an acquisition frequency will affect the effectiveness 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 to make the acquisition frequency match the actual requirements is the key to improving data quality and diagnostic efficiency.

[0146] The description information is used to describe the composition and / or dynamic changes of the measured low-impedance biological tissue. The dynamic changes include but are not limited to the functional state or health state of the low-impedance biological tissue. The composition of the described low-impedance biological tissue is used for the hierarchical structure of the biological probe in the low-impedance biological tissue to realize the positioning of the biological probe in the low-impedance characteristic tissue.

[0147] As the biological probe moves in the low-impedance biological tissue and the state changes of the low-impedance biological tissue occur at different time periods, for example, in the early stage of the disease, the change of the electrical characteristics of the tissue is relatively slow, while in the acute stage of the disease, the change may increase significantly. Therefore, the acquisition frequency needs to be flexibly adjusted to avoid waste of resources and ensure the timeliness and accuracy of the data.

[0148] By adapting the description information, the acquisition frequency of data collection can be dynamically adjusted to ensure that data collection not only meets the real-time requirements but also avoids collecting too much useless data.

[0149] As described above, through the biological probe, data such as impedance, temperature, and pressure impedance characteristics of low-impedance biological tissues are collected in real time. After these data are preprocessed (such as filtering, denoising, etc.), the current state of the low-impedance tissue is analyzed through algorithms (such as state analysis methods based on machine learning or rules).

[0150] From this, it is identified whether there is an acute lesion or abnormal state in the low-impedance biological tissue. For example, in the acute stage of certain diseases, the change of the impedance characteristic signal of the tissue is relatively large, while in the recovery stage, the change is relatively small. Generally speaking, the requirement for the acquisition frequency is judged according to these analysis results.

[0151] When it is determined according to the description information that the low-impedance biological tissue is in the acute lesion, rapid change or dynamic response stage (such as the acute stage of tumors, inflammation), the acquisition frequency needs to be increased. This is because in this case, the changes in the impedance characteristic signals are relatively fast, and higher time resolution is required to capture the subtle changes.

[0152] For example, the acquisition frequency of the biological probe may be increased from once per second to five times per second, or in some acute pathological states, the frequency can even be increased to ten times per second or higher.

[0153] If it is determined according to the description information that the electrical property changes of the low-impedance biological tissue are relatively stable, or in the recovery period, healthy state (such as stable metabolic activities or no obvious abnormal state), the acquisition frequency can be reduced. This can save computing resources and improve efficiency.

[0154] For example, in the healthy state, the acquisition frequency can be reduced to once per minute, or according to actual needs, the sampling frequency can be reduced to twice per second.

[0155] This not only improves the resource utilization rate, but also ensures that enough key information can be captured, enhancing the effectiveness in the monitoring, diagnosis and treatment processes. This dynamic and intelligent acquisition frequency adjustment method is of great significance to the real-time performance, sensitivity and efficiency of biomedical data acquisition.

[0156] It should also be further explained that the impedance characteristic signals are the measurement outputs of the structures at different levels within the measured low-impedance biological tissue.

[0157] Exemplarily, in low-impedance biological tissues such as brain tissues, the current hierarchical results of the biological probe can be located through the output impedance characteristic signals. For example, whether it is the bleeding area of the brain tissue, or gray matter, white matter, cerebrospinal fluid, etc., so as to accurately locate for the diagnosis and treatment without the assistance of other devices.

[0158] And it is further clarified that corresponding to the impedance characteristic data obtained from the impedance characteristic signals, it can be the data representing the change rate obtained in the acquisition and processing process, such as the change rate corresponding to the previous time and the current time, so as to further apply to the low-impedance biological tissue and enhance the accuracy of the measurement.

[0159] In an exemplary embodiment, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method as described above.

[0160] In an exemplary embodiment, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method as described above.

[0161] From the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0162] In an exemplary embodiment of the present invention, there is also provided a computer program medium, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor of a computer, the computer is enabled to execute the method described in the method embodiment part above.

[0163] According to an embodiment of the present invention, there is also provided a program product for implementing the method in the above method embodiment, which can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0164] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0165] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, and the readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0166] The program code embodied on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any appropriate combination of the foregoing.

[0167] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user device, executed as a stand-alone 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 can 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, alternatively, can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0168] It should be noted that although several modules or units of a device for action execution are mentioned in the foregoing 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 of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by a plurality of modules or units.

[0169] In addition, although the steps of the method in the present invention are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0170] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to an embodiment 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, including 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 an embodiment of the present invention.

[0171] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the invention following the general principles of the invention and including known common general knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the appended claims.

Claims

1. An adaptive multi-resolution method for testing low-impedance tissues, characterized in that, The method includes: Performing low-impedance bio-tissue impedance measurement through a biological probe and outputting impedance characteristic signals in real time, where the impedance characteristic signals characterize the electrical characteristic changes of the measured low-impedance bio-tissue; Performing dual-channel acquisition on the impedance characteristic signals, so that the impedance characteristic signals are synchronously transmitted to two parallel data acquisition channels, and the parallel data acquisition channels include a high-resolution channel and a fast measurement channel; The FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels. The impedance characteristic data is high-precision impedance characteristic data or impedance characteristic fast measurement data; the FPGA main control chip will perform real-time comparison on the impedance characteristic data from the two data acquisition channels to calculate the absolute difference value between the two channel data, that is: Diff = |Data channel1 - Data channel2 |; Data channel1 and Data channel2 are the impedance characteristic data corresponding to two data acquisition channels respectively; When the absolute difference value is greater than the preset difference threshold, select the fast measurement channel to output the impedance characteristic fast measurement data; When the absolute difference value is not greater than the preset difference threshold, select the high-resolution channel to output the high-precision impedance characteristic data; Determine the composition and / or state of the low-impedance bio-tissue according to the selected output impedance characteristic data.

2. The method according to claim 1, characterized in that, Both of the two parallel data acquisition channels perform dual-channel acquisition on the impedance characteristic signals through a configured preprocessing module and an analog-to-digital conversion chip, so that the impedance characteristic signals are synchronously transmitted to the two parallel data acquisition channels, including: Performing a sampling operation on the impedance characteristic signals in parallel through the two configured parallel data acquisition channels, so that the impedance characteristic signals are synchronously transmitted to the two preprocessing modules constituting the data acquisition channels; Performing a preprocessing operation on the impedance characteristic signals through the preprocessing module, and then transmitting them 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 impedance characteristic data is transmitted into the FPGA main control chip, and the FPGA main control chip performs pre-output control on the impedance characteristic data of the two parallel data acquisition channels.

3. The method according to claim 1, wherein Before the FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels, the method further includes: Performing adaptive filtering on the incoming impedance characteristic data through the FPGA main control chip to obtain impedance characteristic data with the bandwidth noise within the sampling bandwidth filtered out.

4. The method according to claim 1, wherein The FPGA main control chip dynamically selects the impedance characteristic data corresponding to one channel according to the data difference between the two parallel data acquisition channels, including: Calculating the absolute difference value between the data of the two parallel data acquisition channels; 5. The method according to claim 4, characterized in that, Comparing the absolute difference value with the preset difference threshold, and selecting the impedance characteristic data corresponding to one channel according to the size relationship between the absolute difference value and the preset difference threshold. The comparing the absolute difference value with the preset difference threshold and selecting the impedance characteristic data corresponding to one channel according to the size relationship between the absolute difference value and the preset difference threshold includes: Perform a numerical comparison between the absolute difference value and a preset difference threshold to obtain the magnitude relationship between the absolute difference value and the preset difference threshold; If the magnitude relationship indicates that the absolute difference value is greater than the preset difference threshold, then select and output the rapid measurement data of the impedance characteristics corresponding to the rapid measurement channel.

6. The method according to claim 5, characterized in that, The comparison between the absolute difference value and the preset difference threshold, and the selection of the impedance characteristic data output corresponding to a channel according to the magnitude relationship between the absolute difference value and the preset difference threshold further includes: If the magnitude relationship indicates that the absolute difference value is not greater than the preset difference threshold, then select and output the high-precision impedance characteristic data corresponding to the high-resolution channel.

7. The method according to claim 1, wherein The impedance characteristic signal synchronously maps in time the multimodal changes of the electrical characteristics of the low-impedance biological tissue in space-time.

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

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

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