Electrical impedance imaging data processing method, apparatus, and storage medium

Abstract

The application relates to an electrical impedance imaging data processing method, device and storage medium, wherein the electrical impedance imaging data processing method comprises the following steps: acquiring multiple channel data of multiple electrodes in a measurement area; obtaining an electrode state characteristic parameter of each electrode based on the channel data; when the electrode state characteristic parameters of the multiple electrodes are all within a characteristic threshold range, uploading the channel data to an upper computer in an N-frame interval in a channel-by-channel polling manner; otherwise, determining abnormal channels in all channels corresponding to the multiple electrodes based on the electrode state characteristic parameters of the multiple electrodes; determining to-be-uploaded data according to the channel data of the abnormal channels, and uploading the to-be-uploaded data to the upper computer in an M-frame interval which is smaller than the N-frame interval; and uploading the channel data of other channels except the abnormal channels to the upper computer in an N-frame interval in a channel-by-channel polling manner. Through the application, the problem of large data processing amount in the data transmission and processing process is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electrical impedance tomography, and in particular to an electrical impedance imaging data processing method and device and a storage medium. BACKGROUND

[0002] Electrical impedance tomography (EIT) is a non-invasive medical functional imaging technology. The principle is to arrange an electrode array on the surface of the human body, inject a safe current, measure the voltage signal, and reconstruct the internal electrical impedance distribution and changes of the human body. This technology has the advantages of no radiation, simple system structure, fast imaging speed, and wide application prospects in dynamic monitoring of physiological activities such as cardiovascular, esophagus, and stomach. The existing EIT system faces significant technical bottlenecks in high-speed imaging scenarios: when using a 16-electrode mode, one frame of image needs to collect 256-channel data, and the data volume of long-time monitoring can reach several tens of GB. If the original data is directly transmitted, it will face problems such as large data volume, large storage pressure, and insufficient transmission speed. Especially under the demand of high-speed imaging, the transmission and processing of the original data will become a bottleneck, restricting the imaging speed and real-time performance of the EIT system. In related technologies, simplified processing (such as setting abnormal signals to 0) or adjusting the excitation frequency are used, which still has the problem of large original data transmission volume.

[0003] At present, there is no effective solution to the problem of large original data transmission volume in related technologies. SUMMARY

[0004] The embodiments of the present application provide an electrical impedance imaging data processing method and a storage medium to at least solve the problem of large original data transmission volume in related technologies.

[0005] In a first aspect, the embodiments of the present application provide an electrical impedance imaging data processing method, which comprises:

[0006] Obtaining a plurality of channel data of a plurality of electrodes in a measurement region;

[0007] For each electrode, obtaining an electrode state feature parameter based on the channel data;

[0008] When the electrode state feature parameters of the plurality of electrodes are all within a feature threshold range, uploading the channel data to an upper computer in a polling manner with an interval of N frames; N is a positive integer;

[0009] Otherwise, determining an abnormal channel in all channels corresponding to the plurality of electrodes based on the electrode state feature parameters of the plurality of electrodes;

[0010] According to the channel data of the abnormal channel, determine the data to be uploaded, and upload the data to the host computer at an interval of M frames; in other channels in the all channels except the abnormal channel, poll the channel data of the other channels to the host computer at an interval of N frames; M is a positive integer, and M is less than N.

[0011] In some embodiments, the determining the data to be uploaded according to the channel data of the abnormal channel comprises:

[0012] For the channel data of the abnormal channel in the current frame and the previous K frames, calculate the abnormal waveform correlation coefficient of each abnormal channel; K is a positive integer;

[0013] Determine a first abnormal channel whose abnormal waveform correlation coefficient is lower than a correlation coefficient threshold, and identify the channel data of the first abnormal channel as the data to be uploaded;

[0014] Determine a second abnormal channel whose abnormal waveform correlation coefficient is higher than the correlation coefficient threshold, and mark the current frame data of the second abnormal channel with an abnormal identifier and notify the host computer.

[0015] In some embodiments, the obtaining the electrode state feature parameter based on the channel data for each electrode comprises:

[0016] For each electrode, perform orthogonal demodulation on multiple frames of the channel data to obtain multiple voltage module values;

[0017] Perform variance sum calculation on the multiple voltage module values to obtain the electrode state feature parameter.

[0018] In some embodiments, the obtaining the electrode state feature parameter based on the channel data for each electrode comprises:

[0019] For each electrode, perform orthogonal demodulation on multiple frames of the channel data to obtain multiple voltage module values;

[0020] Perform variance sum calculation on the multiple voltage module values to obtain the electrode state feature parameter.

[0021] In some embodiments, the obtaining the electrode state feature parameter based on the channel data for each electrode further comprises:

[0022] For each electrode, perform signal correlation analysis on multiple channel data to obtain multiple signal correlation coefficients;

[0023] In the multiple signal correlation coefficients, filter and count the number of target signals whose signal correlation coefficients are higher than a signal correlation threshold to obtain the electrode state feature parameter.

[0024] In some embodiments, after obtaining the electrode state characteristic parameter based on the channel data for each electrode, the method further comprises:

[0025] obtaining an electrode off-state according to the electrode state characteristic parameter, and uploading the electrode off-state to a host computer;

[0026] calculating an electrode contact impedance for each electrode based on the channel data and the electrode off-state, and obtaining an electrode contact state based on the electrode contact impedance, and uploading the electrode contact state to the host computer.

[0027] In some embodiments, the obtaining an electrode off-state according to the electrode state characteristic parameter, and uploading the electrode off-state to a host computer, comprises:

[0028] when the electrode state characteristic parameter is not within a preset characteristic parameter threshold range, setting the electrode off-state as abnormal, and uploading the electrode off-state to the host computer;

[0029] when the electrode state characteristic parameter is within a preset characteristic parameter threshold range, setting the electrode off-state as normal.

[0030] In some embodiments, the calculating an electrode contact impedance for each electrode based on the channel data and the electrode off-state, comprises:

[0031] when the electrode off-state is normal, calculating the electrode contact impedance for each electrode based on a voltage module value corresponding to the channel data.

[0032] In some embodiments, the obtaining an electrode contact state based on the electrode contact impedance, and uploading the electrode contact state to the host computer, comprises:

[0033] when the electrode contact impedance is not within a preset impedance range, setting the electrode contact state as abnormal, and uploading the electrode contact state to the host computer;

[0034] when the electrode contact impedance is within a preset impedance range, setting the electrode contact state as normal.

[0035] In a second aspect, the embodiments of the present application provide an electrical impedance imaging data processing device, the device comprising:

[0036] a channel data acquisition module, configured to acquire a plurality of channel data of a plurality of electrodes in a measurement region;

[0037] An extreme parameter module is configured to obtain, for each electrode, an electrode state feature parameter based on the channel data;

[0038] A normal state uploading module is configured to upload the channel data to an upper computer in a polling manner at intervals of N frames when the electrode state feature parameters of the plurality of electrodes are all within a feature threshold range; N is a positive integer.

[0039] An abnormal channel identification module is configured to determine abnormal channels in all channels corresponding to the plurality of electrodes based on the electrode state feature parameters of the plurality of electrodes.

[0040] An abnormal state uploading module is configured to determine to-be-uploaded data according to the channel data of the abnormal channels, and upload the to-be-uploaded data to the upper computer at intervals of M frames; the channel data of other channels except the abnormal channels in the all channels is uploaded to the upper computer in a polling manner at intervals of N frames; M is a positive integer, and M is less than N.

[0041] In a third aspect, a storage medium is provided in the embodiment, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the electrical impedance imaging data processing method in the first aspect.

[0042] Compared with the related art, the electrical impedance imaging data processing method and the storage medium provided in the embodiment can obtain a plurality of channel data of a plurality of electrodes in a measurement region, obtain, for each electrode, an electrode state feature parameter based on the channel data, upload the channel data to an upper computer in a polling manner at intervals of N frames when the electrode state feature parameters of the plurality of electrodes are all within a feature threshold range, N is an interval parameter and a positive integer, otherwise, adjust the interval parameter based on the electrode state feature parameters of the plurality of electrodes, and upload the channel data to the upper computer in a polling manner according to the adjusted interval parameter, thereby solving the problem of large amount of original data processing and slow transmission in the process of data transmission and processing of an electrical impedance imaging system in the prior art.

[0043] Details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more clear and easy to understand. BRIEF DESCRIPTION OF DRAWINGS

[0044] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 FIG. 1 is a hardware structure block diagram of a terminal according to an electrical impedance imaging data processing method in an embodiment of the present application;

[0046] Figure 2 is a flow chart of a method for processing electrical impedance imaging data according to an embodiment of the present application;

[0047] Figure 3 is a structural block diagram of a device for processing electrical impedance imaging data according to an embodiment of the present application;

[0048] Figure 4 is a system framework for electrical impedance imaging according to an embodiment of the present application;

[0049] Figure 5 is a structural block diagram of a system for electrical impedance imaging according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is described and explained below in connection with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application. In addition, it can be understood that although the efforts made in this development process can be complex and lengthy, some changes in design, manufacture or production and the like based on the technical content disclosed in the present application are only routine technical means for those of ordinary skill in the art related to the content disclosed in the present application, and should not be understood as insufficient disclosure of the content disclosed in the present application.

[0051] In the present application, the phrase "embodiments" means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0052] Unless otherwise defined, technical terms and scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. Unless otherwise defined, the terms "one" and "a" or "an" used in the present application shall not be limited to singular aspects but can include both singular and plural aspects. The terms "comprising," "including," "containing," and any variations thereof in the present application shall be taken to cover both cases where a process, method, system, product, or apparatus includes a list of steps or modules (units) and cases where the process, method, system, product, or apparatus does not include the listed steps or modules. The terms "connected," "coupled," and any variations thereof in the present application shall not be limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "plurality" in the present application means greater than or equal to two. The term "and / or" describes an associated relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. The terms "first," "second," "third," and the like in the present application are merely used to distinguish similar objects, and do not represent a specific order for the objects.

[0053] The method embodiments provided by the present embodiment can be executed in a terminal, a computer, or a similar computing device. Taking the case of running on a terminal, Figure 1 is a hardware structure block diagram of a terminal according to the electrical impedance imaging data processing method of the embodiments of the present application. As shown in Figure 1 , the terminal can include one or more (only one is shown in Figure 1 ) processor 102 (the processor 102 can include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above terminal can also include a transmission device 106 for communication function and an input and output device 108. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, and it does not limit the structure of the above terminal. For example, the terminal can include more or fewer components than those shown in Figure 1 , or have a different configuration from that shown in Figure 1 .

[0054] The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as a computer program corresponding to the electrical impedance imaging data processing method in the embodiments of the present application. The processor 102 performs various functional applications and data processing, i.e., implements the method described above, by running the computer program stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0055] The transmission device 106 is configured to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (NIC) which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module configured to communicate with the Internet in a wireless manner.

[0056] EIT technology, also known as bioelectrical impedance tomography technology, is a new type of medical functional imaging technology. Its working principle is to place a certain number of electrodes on the surface of the human body, inject a safe current, and measure the surface voltage of other electrodes, and reconstruct the electrical impedance value or the change value of the electrical impedance inside the human body according to the relationship between the voltage and the current. Since this method does not use a nuclide or a ray, it is harmless to the human body, so it can be measured repeatedly and used repeatedly, and has the characteristics of fast imaging speed and functional imaging. EIT technology has many advantages, such as non-invasive to the human body, no ionization and radiation danger, simple system structure, and simple measurement, etc., and can be used for fast and portable imaging, and has a wide application prospect in continuous dynamic image monitoring of physiological activities of the human body, such as the cardiovascular system, the esophagus, and the stomach.

[0057] However, the existing electrical impedance tomography device still has some problems in multi-channel signal acquisition and processing. At present, a general EIT system adopts a 16-electrode mode, and 256 channels of data are required for one frame of image. Due to the limitation of the electrode distribution mode and the control method, the actual effective channel number is 192, and the amount of original data is large, and the amount of data for long-time monitoring can reach several tens of GB.

[0058] The generation of such a large amount of data is mainly due to two factors: one is that 128-point waveform data of each channel needs to be uploaded, and the other is that a long monitoring time (usually 1 hour to several days) is needed to ensure the integrity and accuracy of the data. In this case, if the original data is directly transmitted, it will obviously face problems such as large data volume, large storage space requirement, and slow transmission speed. Especially in the case of high-speed imaging, such as 100 frames or 50 frames per second real-time monitoring, the transmission and processing of the original data will become a bottleneck, which seriously restricts the imaging speed and real-time performance of the EIT system.

[0059] In order to solve the problem of large amount of original data processing and slow transmission in the process of data transmission and processing of the electrical impedance tomography system, the embodiment provides an electrical impedance tomography data processing method, Figure 2 The flowchart of the electrical impedance tomography data processing method according to the embodiment of the application is shown in Figure 2 The flowchart of the electrical impedance tomography data processing method according to the embodiment of the application is shown in

[0060] In step S201, a plurality of channel data of a plurality of electrodes in a measurement region is obtained.

[0061] Specifically, in the electrical impedance measurement system, the channel data of the measurement region is obtained through the following process: first, a plurality of electrodes are uniformly distributed along the chest of the human body to ensure sensitive perception of the physiological activity of the human body in a close arrangement. For the sake of convenience, the following will mainly take the scheme of using 16 electrodes as an example for description, and other quantities can also be set according to actual application scenarios. When excited, the upper computer configures the excitation current of the constant current source, selects the corresponding excitation electrode pair (such as electrodes 1 and 9) through the excitation selection switch, and injects a safe current into the human body load, while each adjacent measurement electrode pair receives the voltage signal in parallel. In the signal acquisition process, the 16 electrodes are sequentially used as excitation electrode pairs in a polling mechanism, and 16 rounds of excitation can generate 16-channel measurement data. After 16 rounds of polling, one frame of 256-channel original data is accumulated. Each channel corresponds to a pair of excitation electrodes and a pair of measurement electrodes, and the channel data is the voltage time-domain waveform data of the measurement electrode collected after the excitation electrode injects the current. Due to the limitation of electrode distribution and control method, the actual effective channel number is 192. After the received analog signal is processed by the filter amplification circuit, the analog-to-digital conversion is completed by 16 analog-to-digital conversion modules (AD) synchronously, and the converted digital signal is temporarily stored in a plurality of frame (such as 20 frame) cache pool to provide original channel data for subsequent electrode state evaluation and data processing.

[0062] In step S202, for each electrode, an electrode state feature parameter is obtained based on the channel data.

[0063] Specifically, 16 electrodes (numbered from 1 to 16) generate 256 channels of data in total. Taking electrode No. 1 as an example, the electrode data related to electrode No. 1 covers 60 channels (16 channels of related data when electrode No. 1 is used as a positive excitation, 16 channels of related data when electrode No. 1 is used as a negative excitation, 14 channels of related data when electrode No. 1 is used as a positive measurement, and 14 channels of related data when electrode No. 1 is used as a negative measurement, for a total of 60 related channels). By performing signal correlation analysis and / or sum-of-squares analysis on the 60 channels of data related to electrode No. 1, an electrode state characteristic parameter is obtained. This parameter can be used to evaluate the stability of electrode No. 1.

[0064] Step S203, when the electrode state characteristic parameters of the plurality of electrodes are all within the characteristic threshold range, the channel data is uploaded to the host computer in a polling manner at an interval of N frames; N is a positive integer.

[0065] Specifically, when it is detected that the electrode state characteristic parameters of the 16 electrodes all meet the preset threshold requirement within a period of time, the system defaults to an interval of N = 100 frames (which can be configured as a positive integer according to requirements), and uploads the waveform data in a polling manner in the order of channels 1 to 256. After the 256 channels are traversed, the host computer can obtain periodic sampling information of all channels.

[0066] Step S204, otherwise, based on the electrode state characteristic parameters of the plurality of electrodes, an abnormal channel in all channels corresponding to the plurality of electrodes is determined.

[0067] Specifically, a normal threshold range of each electrode state characteristic parameter is set, and the state characteristic parameter of each electrode is compared with the corresponding threshold range. If the characteristic parameter of a certain electrode exceeds the normal threshold range, the channel corresponding to the electrode is determined to be an abnormal channel, and an abnormal channel list is updated in real time. When the electrode state characteristic parameter returns to the normal range, the corresponding channel is removed from the abnormal channel list. Conversely, if a newly added electrode characteristic parameter exceeds the threshold range, the channel corresponding to the electrode is added to the abnormal channel list.

[0068] Step S205, according to the channel data of the abnormal channel, determining to-be-uploaded data, and uploading the to-be-uploaded data to the host computer at an interval of M frames; in all channels except the abnormal channel, the channel data of the other channels is uploaded to the host computer in a polling manner at an interval of N frames; M is a positive integer, and M is less than N.

[0069] Specifically, according to the channel data of the abnormal channel, the data to be uploaded is confirmed. The data to be uploaded can be all abnormal channel data or part of the abnormal channel data. Then, the data to be uploaded is uploaded to the upper computer at an M-frame interval less than N frames, and the channel data of all channels except the abnormal channel is still uploaded at an N-frame interval per channel polling, so as to realize the differentiated data uploading strategy of the abnormal channel and the normal channel. The M-frame interval can also be adjusted in real time according to the number of channels. If the number of abnormal channels is too large, the uploading frequency can be appropriately reduced to avoid too frequent uploading of the channel data of the abnormal channel and cause transmission congestion. For the abnormal channel data, the uploading frequency can also be adaptively adjusted according to the electrode state characteristic parameters of the abnormal channel data. For example, if the variance of a certain channel is low (high stability), it indicates that the signal is stable, and the uploading frequency can be adaptively reduced; if the variance of a certain channel is high (low stability), it indicates that the signal is unstable, and the uploading frequency can be adaptively increased to retain more details.

[0070] In the above steps, by acquiring multi-electrode multi-channel data and calculating electrode state characteristic parameters, real-time monitoring of the electrode state is realized. When the electrode state is normal, the data is uploaded at an N-frame interval per channel polling, and when the electrode state is abnormal, the abnormal channel is determined and the abnormal channel data is uploaded at a shorter M-frame interval, and the normal channel is still uploaded at an N-frame interval polling. This mechanism effectively reduces the data transmission amount under normal state while ensuring accurate capture and real-time uploading of abnormal state; in addition, it can also improve the data transmission efficiency and reduce the system bandwidth occupation, realizing differentiated data uploading and resource optimization configuration of electrode state monitoring.

[0071] In some embodiments, determining the data to be uploaded according to the channel data of the abnormal channel comprises:

[0072] For the channel data of the abnormal channel in the current frame and the previous K frames, the abnormal waveform correlation coefficient of each abnormal channel is calculated; K is a positive integer;

[0073] The first abnormal channel whose abnormal waveform correlation coefficient is lower than the correlation coefficient threshold is determined, and the channel data of the first abnormal channel is identified as the data to be uploaded;

[0074] The second abnormal channel whose abnormal waveform correlation coefficient is higher than the correlation coefficient threshold is determined, and the current frame data of the second abnormal channel is marked with an abnormal identifier and notified to the upper computer.

[0075] Specifically, for the channel data of the abnormal channel in the current frame and the previous K frames, the Pearson correlation coefficient algorithm can be used to calculate the similarity of the waveform of the current frame channel data and the waveform of the previous K frame channel data. When the abnormal waveform correlation coefficient of a certain channel is lower than the correlation coefficient threshold, it indicates that the waveform changes significantly and is different from the historical data waveform, and the channel is determined as a first abnormal channel. Then, the channel data of the current frame of the channel is used as the to-be-uploaded data. If the correlation coefficient of the abnormal channel is higher than the threshold, it indicates that the current waveform and the historical waveform have repeatability, and the current frame data of the second abnormal channel is marked only with an abnormal identifier and notified to the upper computer.

[0076] In the above steps, by calculating the waveform similarity of the current frame and the previous K frames of the abnormal channel, the current frame data of the first abnormal channel with an abnormal waveform correlation coefficient lower than the threshold and a significantly changed waveform is identified as to-be-uploaded data. The current frame data of the second abnormal channel with a correlation coefficient higher than the threshold and a repeatable waveform is only marked with an identifier and notified to the upper computer, so as to realize the hierarchical processing of the abnormal channel data, reduce the transmission amount of repeat abnormal data while accurately capturing the waveform mutation abnormality, improve the efficiency and pertinence of abnormal data uploading, and reduce the system bandwidth occupation and data processing pressure.

[0077] In some embodiments, the electrode state feature parameter is obtained based on the channel data for each electrode, including:

[0078] For each electrode, the multiple frames of channel data are subjected to quadrature demodulation to obtain multiple voltage module values.

[0079] The multiple voltage module values are subjected to sum of squares calculation to obtain the electrode state feature parameter.

[0080] Specifically, one frame of image needs 256 channels of data of 16 electrodes. The 60 channel data associated with each electrode (such as electrode No. 1) are subjected to quadrature demodulation processing. Two quadrature local oscillator signals are multiplied with the input alternating voltage signal, and a direct current component is obtained after low-pass filtering. Then, the voltage module value is synthesized through square root operation. One module value is generated for each channel per frame, and 60 voltage module values are generated for 60 channels of a single electrode. For 20 frames of voltage module values of each channel, the single-channel variance is calculated first, and the formula is as follows:

[0081]

[0082] wherein, is the variance of single-channel data, is the i-th voltage module value, is the average value of the voltage module value, and N is the frame number (herein, N = 20).

[0083] ​Then, the variance values of the 60 channels associated with the single electrode are accumulated to obtain a variance sum, and the larger the value is, the worse the electrode contact stability is.

[0084] In the above embodiment, by performing orthogonal demodulation on the 60 channel data associated with each electrode to obtain the voltage module value, and performing variance sum calculation on 20 frames of data, quantitative analysis of the electrode contact state is realized: when the electrode falls off, the voltage module value of the associated channel fluctuates intensively, and the variance sum increases significantly. Compared with the traditional single-channel threshold detection method, the recognition accuracy of electrode falling off and other abnormal states is improved. At the same time, reliable feature parameter support is provided for subsequent dynamic adjustment of data transmission strategy.

[0085] In some embodiments, the electrode state feature parameter is obtained based on the channel data for each electrode, and further includes:

[0086] For each electrode, signal correlation analysis is performed on the multiple channel data to obtain multiple signal correlation coefficients;

[0087] Among the multiple signal correlation coefficients, the number of target signals with signal correlation coefficients higher than a signal correlation threshold is screened and counted to obtain the electrode state feature parameter.

[0088] Specifically, the waveform data of the 60 channels associated with each electrode are respectively subjected to Pearson correlation coefficient calculation with a standard sinusoidal reference signal. The formula is as follows:

[0089] ;

[0090] Wherein, Xi is the channel waveform sampling value; Yi is the standard waveform sampling value; n is the sample number, i.e. the number of X, Y data pairs; r is the Pearson correlation coefficient, the value range is [-1, 1], r>0 is positive correlation, r<0 is negative correlation, The closer to 1, the stronger the linear correlation.

[0091] After calculation, the channels with correlation coefficients higher than the threshold value are selected as target signals, and the number thereof is counted as the electrode state feature parameter. This parameter directly reflects the electrode contact stability. When the electrode falls off, the waveform distortion of the abnormal channel leads to a decrease in the correlation coefficient and a decrease in the number of target signals. If the number is lower than a preset value (such as 50), it is determined that the electrode state is abnormal. This process provides accurate features for subsequent dynamic adjustment of data transmission strategy.

[0092] In the above steps, the electrode contact stability quantitative monitoring is realized by performing Pearson correlation coefficient calculation on the 60 channel waveform data associated with each electrode and the standard sine wave, screening and counting the number of target signals higher than the threshold value. Compared with the traditional single-channel threshold detection method, the recognition accuracy of abnormal states such as electrode falling off is improved. At the same time, reliable feature parameter support is provided for subsequent dynamic adjustment of data transmission strategy.

[0093] In some embodiments, after obtaining the electrode state feature parameters based on the channel data for each electrode, the above electrical impedance imaging data processing method can further include the following steps:

[0094] Obtaining the electrode falling off state according to the electrode state feature parameters, and uploading the electrode falling off state to the upper computer;

[0095] Based on the channel data and the electrode falling off state, calculating the electrode contact impedance for each electrode; based on the electrode contact impedance, obtaining the electrode contact state, and uploading the electrode contact state to the upper computer.

[0096] Specifically, after calculating the electrode state feature parameters, it can be judged whether the electrode is falling off. If the electrode state feature parameters are not within the preset range, it is determined that the electrode is in a falling off state, and the electrode number and falling off state identifier are sent to the upper computer. Then, for the electrodes not determined to be falling off, the electrode contact impedance is calculated based on the voltage module value corresponding to the channel data and the constant excitation current, and then the contact state of the electrode is judged according to the value of the electrode contact impedance, and the electrode contact state is uploaded to the upper computer.

[0097] In the above steps, the electrode falling off state is determined by calculating the electrode state feature parameters based on the channel data, and the contact state of the electrodes not falling off is calculated to determine the contact state, which realizes multi-dimensional accurate monitoring of the working state of the electrodes. At the same time, the electrode state data is uploaded to the upper computer in real time, so that the upper computer can perform corresponding electrode adjustment operation according to the reported results.

[0098] In some embodiments, obtaining the electrode falling off state according to the electrode state feature parameters, and uploading the electrode falling off state to the upper computer, includes:

[0099] When the electrode state feature parameters are not within the preset feature parameter threshold range, the electrode falling off state is set to abnormal, and the electrode falling off state is uploaded to the upper computer;

[0100] When the electrode state feature parameters are within the preset feature parameter threshold range, the electrode falling off state is set to normal.

[0101] Specifically, the variance sum or Pearson correlation coefficient feature parameters of 60 channel data of each electrode are calculated and compared with a preset threshold range. For example, if the variance sum exceeds the threshold or the number of channels with a correlation coefficient less than 0.6 exceeds 50, it is determined that the feature parameter is out of the threshold range, at which time the system automatically identifies the electrode off state of the electrode as abnormal, and encapsulates a data packet containing the electrode number (1-16), state identification and time stamp to upload to the upper computer in real time. If the feature parameters are all within the threshold range (such as the variance sum ≤200 and the number of channels with a correlation coefficient <0.6 ≤5), the off state is set to normal.

[0102] In the above steps, through the judgment of whether the electrode state feature parameters are within the threshold range, the accurate identification and real-time reporting of the electrode off state are realized, so that the upper computer can perform corresponding electrode adjustment operation according to the reported results.

[0103] In some embodiments, based on the channel data and the electrode off state, for each electrode, an electrode contact impedance is calculated, including:

[0104] When the electrode off state is normal, for each electrode, the electrode contact impedance is calculated based on the voltage module value corresponding to the channel data.

[0105] Specifically, for the electrodes with normal electrode off state, the voltage module value output by the quadrature demodulation module and the known constant excitation current are used to calculate the electrode contact impedance by four-electrode contact impedance measurement method. Assuming that the electrodes are arranged in a ring shape, there are electrodes 1, 2, 3, 4…16, the current excitation is injected through the outer electrodes, and the inner electrodes are switched to measure the voltage. The total impedance of two measurements is used to deduce the contact impedance, and the key is to eliminate the tissue impedance and simplify the calculation by using the assumption that the contact impedances of adjacent / symmetrical electrodes are approximately equal. The steps of calculating the electrode contact impedance by four-electrode contact impedance measurement method are as follows:

[0106] Step 1: Set the electrode and excitation mode.

[0107] Firstly, the functions of A, B, C and D electrodes are defined, in which A, B or A, D will be used as current injection electrodes, and B, C will be fixed as voltage measurement electrodes. At the same time, a constant current source is configured to ensure that the current I injected into the tissue remains stable in amplitude and frequency, providing a reliable excitation signal basis for subsequent measurement and calculation.

[0108] Step 2: Collect voltage data in different scenarios.

[0109] When performing the first group of measurements (V AB_BC ), A and B are used as current injection electrodes to inject current I into the tissue, and the voltage between B and C is measured at the same time, and the measurement value V AB_BCWill contain the B electrode contact impedance Z B The generated voltage, i.e. V AB_BC ≈Z B ×I+V BC , where V BC is the real voltage between the B and C electrodes.

[0110] Then a second set of measurements (V AD_BC ) is taken, switching the current injection electrode to A and D, keeping the injection current I constant, and still measuring the voltage through the B and C electrodes. Since the B electrode is not used as a current injection electrode at this time, its contact impedance has a negligible effect on the measured value V AD_BC , i.e. V AD_BC ≈V BC .

[0111] Step 3: Calculate the electrode contact impedance.

[0112] After obtaining two sets of measurement data, the contact impedance Z B of the B electrode can be calculated according to the formula Z B =(V AB_BC -V AD_BC ) / I.

[0113] In the above steps, by using the four-electrode contact impedance measurement method, the contact impedance is accurately calculated using the voltage module value of the orthogonal demodulation and the constant excitation current when the electrode shedding state is normal, which provides data support for the fine evaluation of the electrode contact state.

[0114] In some embodiments, based on the electrode contact impedance, the electrode contact state is obtained and uploaded to the host computer, including:

[0115] When the electrode contact impedance is not within the preset impedance range, the electrode contact state is set to abnormal, and the electrode contact state is uploaded to the host computer;

[0116] When the electrode contact impedance is within the preset impedance range, the electrode contact state is set to normal.

[0117] Specifically, for electrodes that have not fallen off, after calculating the contact impedance using the four-electrode method, it is compared with the preset impedance threshold range (such as a normal range of 1kΩ-50kΩ). If the impedance value exceeds 50kΩ (such as 55kΩ) or is lower than 1kΩ (such as 0.8kΩ), it is determined that the contact impedance is out of the threshold range. At this time, the contact state of the electrode is marked as abnormal, and a data packet containing the electrode number (1-16), state mark and time stamp is sent to the host computer.

[0118] In the above steps, the electrode contact state is accurately identified and reported in real time by judging whether the electrode state characteristic parameter is within the threshold range, so that the host computer can perform corresponding electrode adjustment operation according to the reported result.

[0119] Figure 3 is a structural block diagram of the electrical impedance imaging data processing device according to the embodiment of the application, as shown in Figure 3 , the device comprises:

[0120] The channel data acquisition module 31 is configured to acquire a plurality of channel data of a plurality of electrodes in a measurement region.

[0121] The characteristic parameter calculation module 32 is configured to obtain an electrode state characteristic parameter for each electrode based on the channel data.

[0122] The normal state uploading module 33 is configured to poll and upload the channel data to the host computer channel by channel at intervals of N frames when the electrode state characteristic parameters of the plurality of electrodes are all within the characteristic threshold range; N is a positive integer.

[0123] The abnormal channel identification module 34 is configured to determine an abnormal channel in all channels corresponding to the plurality of electrodes based on the electrode state characteristic parameters of the plurality of electrodes.

[0124] The abnormal state uploading module 35 is configured to determine to-be-uploaded data according to the channel data of the abnormal channel, and simultaneously upload the to-be-uploaded data to the host computer at intervals of M frames; the channel data of other channels except the abnormal channel in the all channels is uploaded to the host computer channel by channel at intervals of N frames; M is a positive integer, and M is less than N.

[0125] It should be noted that each of the above modules can be a functional module or a program module, which can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor; or each of the above modules can be located in different processors in any combination.

[0126] Figure 4 is a framework of the electrical impedance imaging system according to the embodiment of the application, comprising:

[0127] The host computer 401 is configured to serve as the control and data interaction core of the system, and sends instructions downward to configure the FPGA firmware and the working parameters of the entire measurement system, and receives the electrode contact impedance, signal characteristics and other data returned after processing by the FPGA firmware, so as to realize the monitoring, data display and analysis of the measurement process.

[0128] Configuration command 402, for receiving host computer instructions, generating accurate configuration commands, issuing signal frequency, amplitude, etc. parameter settings to direct digital frequency synthesizer (DDS), sending output current specification instructions to constant current source, transmitting electrode gating control information to excitation selection switch, providing logic control for excitation signal generation and application.

[0129] Direct digital frequency synthesizer 403, for synthesizing digital signals of specific frequency, phase and amplitude according to the configuration commands of FPGA firmware, providing accurate signal source for subsequent generation of stable excitation current, and ensuring that the frequency characteristics of the excitation signal meet the measurement requirements.

[0130] Constant current source 404, for receiving direct digital frequency synthesizer (DDS) output signal and converting it into constant current. Through internal feedback regulation mechanism, the stability and accuracy of the output current are maintained, and the current output by the constant current source is used for electrode excitation, and the current loop of the electrode-human body load is constructed.

[0131] Excitation selection switch 405, for switching internal paths according to the configuration commands of FPGA firmware, and selecting corresponding electrodes to accurately apply excitation current output by the constant current source to target electrodes, realizing time-sharing excitation of multiple electrodes, and adapting to the needs of the system for measuring different electrode contact impedances.

[0132] Human body load 406, as a component of the current loop, when the excitation current is injected through the electrode, the human body load generates a voltage response due to its own physiological characteristics (such as resistance, capacitance distribution) and electrode contact state, and the voltage signal contains information required for electrode contact impedance measurement.

[0133] Filtering and amplification 407, for processing the weak voltage signal collected by the electrode, filtering out power frequency, environmental electromagnetic noise and other noise interference, and amplifying the useful signal amplitude to an appropriate range to provide clear and identifiable voltage signals for analog-to-digital converter (AD) conversion.

[0134] Analog-to-digital converter 408, for converting the analog voltage signal after filtering and amplification into a digital signal. With the ability of multi-channel parallel conversion, 1-16 electrode signals are synchronously collected, and continuous analog quantities are discretized into digital quantities, providing basic data for digital signal processing of FPGA firmware.

[0135] 20-frame buffer 409, for receiving digital signals converted by the analog-to-digital converter (AD), and buffering them in units of 20 frames. The multi-channel data is temporarily stored and arranged, providing continuous and regular data input for subsequent signal variance and calculation, correlation calculation, etc. algorithm modules.

[0136] 20 frame voltage signal 410, used to convert 20 frame raw data into 20 frame voltage data as the core input of signal variance and calculation, electrode contact impedance calculation and other links.

[0137] Signal variance and calculation 411, used to calculate signal variance and based on 20 frame voltage signal data. By analyzing the variance and, the degree of voltage signal fluctuation and stability is evaluated to assist in judging signal quality, providing basis for subsequent algorithm adjustment and data reliability determination.

[0138] Electrode contact impedance calculation 412, used to combine the excitation current parameters (obtained by constant current source configuration) and the preprocessed voltage signal, and use the four electrode method algorithm to eliminate tissue impedance interference and accurately deduce the contact impedance value between the electrode and the human body load.

[0139] Raw signal correlation calculation 413, used to extract raw channel data from 20 frame cache and analyze channel data and standard sine wave correlation. The degree of voltage signal fluctuation and stability is evaluated to assist in judging signal quality, providing basis for subsequent algorithm adjustment and data reliability determination.

[0140] Waveform data of one channel on interval N frames 414, used to select single channel waveform data from cache or processed data according to preset interval strategy, and encapsulate and upload to the host computer. On the premise of ensuring data effectiveness, the amount of uploading is controlled to enable the host computer to obtain waveform information of different time periods and channels for in-depth analysis.

[0141] 1 frame data integration 415, used to integrate multi-dimensional processed data such as electrode contact impedance, signal variance and abnormality identification in 1 frame format. The scattered data is packaged into a unified data frame to facilitate efficient analysis and processing of the host computer, ensuring complete and accurate return of measurement results.

[0142] Figure 5 The structural block diagram of the electrical impedance imaging system according to the embodiments of the present application comprises a data acquisition module 51, a data processing and control module 52 and a communication module 53.

[0143] The data acquisition module 51 comprises 16 electrodes, which are uniformly distributed along the chest of the human body, the electrodes are excited in pairs and measured adjacent to each other, and are polled in a certain rule. Each electrode is made of flexible piezoresistive material, and a conductor is sewn on the piezoresistive material. The length ratio between the electrode and the elastic connecting band is 3:1. The electrodes are arranged in a close arrangement, and in a tight state, the force received by the electrodes when the position changes in two directions can be fully felt.

[0144] Data processing and control module 52 is used for data processing and control, and performs corresponding data processing on the AD analog-to-digital converted data in the FPGA, receives the AD analog-to-digital converted data, controls the programmable gain amplifier and other filter circuits, and switches the excitation and measurement of the 16 electrodes.

[0145] The communication module 53 realizes real-time data transmission between the FPGA and the host computer in a high-speed network mode, supports TCP / IP protocol stack and dual-mode communication of custom data frame structure. The module is equipped with dual-gigabit Ethernet PHY chips, realizes 4Gbps full-duplex transmission through the RGMII interface with the FPGA, and uses differential signal transmission technology to ensure the anti-electromagnetic interference ability in the medical environment. The data encapsulation adopts a dynamic framing mechanism, each frame contains 128 bytes of payload and 16-bit CRC check code, and the protocol stack offloading is realized through hardware acceleration, so that the transmission delay is controlled within 50μs. The module is configured with 8 priority queues, which can implement QoS hierarchical transmission of physiological signal data of different electrode groups, and has a link aggregation function to realize dynamic load balancing of bandwidth. The physical interface uses a shielded RJ45 connector, which meets the IEEE 802.3ab standard, supports POE power supply and data transmission characteristics, and reserves an optical fiber media conversion interface to realize the expansion capability of long-distance transmission of hundreds of meters.

[0146] In addition, in combination with the electrical impedance imaging data processing method in the above-mentioned embodiments, the embodiments of the present application can provide a storage medium for implementation. The storage medium has a computer program stored thereon; the computer program is executed by a processor to implement any one of the electrical impedance imaging data processing methods in the above-mentioned embodiments.

[0147] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0148] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0149] Those skilled in the art should understand that the various technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. An electrical impedance imaging data processing method, characterized by, The method comprises: acquiring multiple channel data of multiple electrodes in a measurement area; for each electrode, obtaining an electrode state characteristic parameter based on the channel data, comprising: for each electrode, performing quadrature demodulation on multiple frames of the channel data to obtain multiple voltage module values; performing sum of squares calculation on the multiple voltage module values to obtain the electrode state characteristic parameter; when the electrode state characteristic parameters of the multiple electrodes are all within a characteristic threshold range, uploading the channel data to an upper computer in a channel-by-channel polling manner at an interval of N frames; N is a positive integer; otherwise, determining an abnormal channel in all channels corresponding to the multiple electrodes based on the electrode state characteristic parameters of the multiple electrodes; determining to-be-uploaded data according to the channel data of the abnormal channel, comprising: for the channel data of the abnormal channel in a current frame and K previous frames, calculating an abnormal waveform correlation coefficient of each abnormal channel; K is a positive integer; determining a first abnormal channel whose abnormal waveform correlation coefficient is lower than a correlation coefficient threshold, and identifying the channel data of the first abnormal channel as the to-be-uploaded data; determining a second abnormal channel whose abnormal waveform correlation coefficient is higher than the correlation coefficient threshold, and marking the current frame data of the second abnormal channel with an abnormal identifier and notifying the upper computer; uploading the to-be-uploaded data to the upper computer at an interval of M frames; uploading the channel data of other channels except the abnormal channel in the all channels to the upper computer in a channel-by-channel polling manner at an interval of N frames; M is a positive integer, and M is less than N.

2. The electrical impedance imaging data processing method of claim 1, wherein, The method further comprises: for each electrode, performing signal correlation analysis on multiple channel data to obtain multiple signal correlation coefficients; in the multiple signal correlation coefficients, filtering and counting a target signal number whose signal correlation coefficient is higher than a signal correlation threshold to obtain the electrode state characteristic parameter.

3. The electrical impedance imaging data processing method of claim 1, wherein, The method further comprises: obtaining an electrode falling-off state according to the electrode state characteristic parameter, and uploading the electrode falling-off state to an upper computer; calculating an electrode contact impedance for each electrode based on the channel data and the electrode falling-off state, obtaining an electrode contact state based on the electrode contact impedance, and uploading the electrode contact state to the upper computer.

4. The electrical impedance imaging data processing method of claim 3, wherein, The method further comprises: when the electrode state characteristic parameter is not within a preset characteristic parameter threshold range, setting the electrode falling-off state as abnormal, and uploading the electrode falling-off state to the upper computer; when the electrode state characteristic parameter is within the preset characteristic parameter threshold range, setting the electrode falling-off state as normal.

5. The electrical impedance imaging data processing method of claim 4, wherein, The method further comprises: calculating an electrode contact impedance for each electrode based on the channel data and the electrode falling-off state, obtaining an electrode contact state based on the electrode contact impedance, and uploading the electrode contact state to the upper computer. When the electrode off state is normal, for each electrode, the electrode contact impedance is calculated based on the voltage module value corresponding to the channel data.

6. The electrical impedance imaging data processing method of claim 5, wherein, The electrode contact state is obtained based on the electrode contact impedance, and the electrode contact state is uploaded to the host computer, including: When the electrode contact impedance is not within the preset impedance range, the electrode contact state is set to abnormal, and the electrode contact state is uploaded to the host computer; When the electrode contact impedance is within the preset impedance range, the electrode contact state is set to normal.

7. An electrical impedance imaging data processing apparatus, characterized in that, The device comprises: a channel data acquisition module for acquiring a plurality of channel data of a plurality of electrodes in a measurement region; a feature parameter calculation module for obtaining an electrode state feature parameter for each electrode based on the channel data; The feature parameter calculation module is also used for performing orthogonal demodulation on a plurality of channel data for each electrode to obtain a plurality of voltage module values; and performing variance sum calculation on the plurality of voltage module values to obtain the electrode state feature parameter; A normal state upload module is configured to, when the electrode state feature parameters of the plurality of electrodes are all within a feature threshold range, upload the channel data to the host computer in a channel-by-channel polling manner at intervals of N frames; N is a positive integer; An abnormal channel identification module is configured to determine abnormal channels in all channels corresponding to the plurality of electrodes based on the electrode state feature parameters of the plurality of electrodes; An abnormal state upload module is configured to determine to-be-uploaded data according to the channel data of the abnormal channels, and upload the to-be-uploaded data to the host computer at intervals of M frames; and upload the channel data of other channels except the abnormal channels in the all channels to the host computer in a channel-by-channel polling manner at intervals of N frames; M is a positive integer, and M is less than N; The abnormal state upload module is also used for calculating an abnormal waveform correlation coefficient of each abnormal channel for channel data of the abnormal channel in a current frame and previous K frames; K is a positive integer; determining a first abnormal channel with the abnormal waveform correlation coefficient lower than a correlation coefficient threshold; identifying the channel data of the first abnormal channel as the to-be-uploaded data; determining a second abnormal channel with the abnormal waveform correlation coefficient higher than the correlation coefficient threshold, and marking the current frame data of the second abnormal channel with an abnormal identifier and notifying the host computer.

8. A storage medium, characterized by The storage medium stores a computer program, wherein the computer program is configured to execute the electrical impedance imaging data processing method in any one of claims 1 to 6 when running.

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