119 results about "Bioelectric Impedance" patented technology

The invention provides a bioelectrical impedance lung cancer detection method based on artificial intelligence, which comprises the following steps: acquiring multi-dimensional electric signal data of a tissue sample, preprocessing the data, and acquiring preliminary feature representation of the sample through a static spatial feature extraction method; based on the time sequence characteristics of the signals, constructing a dynamic feature extraction model, extracting dynamic features from time sequence association, and mining the difference and regularity between samples; static space features and dynamic time sequence features are fused, multi-dimensional attributes of signals are comprehensively analyzed, high-dimensional key feature representation is generated, the distinguishing capacity and reliability of the features are improved, and attention to the key features is enhanced; according to the method, the characteristics of a sample are fused, the fused characteristics are analyzed and classified, the normal state and the abnormal state of the sample are judged in combination with model output, finally, accurate detection and diagnosis results are achieved, the performance of the method on various bioelectrical impedance data sets is superior to that of a traditional method, and the sensitivity and specificity of lung cancer detection are effectively improved.

The invention discloses a bioelectrical impedance tumor detection method based on pattern recognition, and the method comprises the following steps: S1, carrying out the electrical impedance measurement of a to-be-detected tumor region through a bioelectrical impedance analysis instrument, and obtaining the current, voltage and impedance values at different depths and frequencies; s2, designing a feature extraction module, and extracting key features in the electrical impedance data of each channel under different frequencies; s3, inputting the extracted key features into a multi-level feature fusion module, and fusing the electrical impedance features under different channels and frequencies to form new fused features; and S4, inputting the fusion features into a self-adaptive classification module, and adjusting model parameters in combination with a self-adaptive optimization method. According to the invention, automatic processing and classification are carried out on data through a pattern recognition algorithm, and the accuracy, sensitivity and reliability of tumor detection can be effectively improved in combination with the feature extraction module, the multi-level feature fusion module and the adaptive classification module.

The invention discloses a pain management method based on bioelectrical impedance data and a machine learning algorithm, and relates to the technical field of medical health, and the method comprises the steps: carrying out the phase delay analysis of an impedance phase spectrum matrix, calculating the change rate of a phase angle of each frequency point along with time, and obtaining the maximum phase lag time; according to the temperature field sequence and the maximum phase lag time, time-space registration is carried out through a multi-modal data fusion method and impedance modulus changes of the corresponding areas, and a pain biomarker feature library is generated; through a bidirectional LSTM and a cross attention fusion method, time sequence modeling and cross-modal correlation analysis are carried out on bioelectricity signal characteristics extracted by a sliding window and historical pain data, a dynamic evolution rule of pain development is captured, a pain management triple containing pain intensity, intervention response and prognosis evaluation is generated, and a pain management result is obtained. And a quantitative basis is provided for personalized treatment.

The invention relates to the technical field of AI intelligent management, and discloses an AI-based body weight intelligent management method and system, and the method comprises the steps: carrying out the bioelectrical impedance signal collection of M measurement nodes of a target user through an Internet of Things front-end device, and obtaining a multi-node impedance signal set; biological impedance space-time atlas analysis is executed through the cloud processing platform, and human body composition feature data are obtained; aI analysis is carried out through a Deepseek multi-expert model of the cloud processing platform to obtain a weight management analysis result of each professional field; weight fusion and causal inference are carried out through the cloud processing platform in combination with weight management analysis results of all professional fields and doctor review results, and a personalized weight reduction management strategy is generated; the personalized weight reduction management strategy is received through the mobile terminal, dynamic adjustment is carried out in combination with daily execution feedback data of the target user, an intelligent weight management guidance scheme is generated, and the method improves the management effect of AI weight intelligent management and user experience.

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention provides an acupoint positioning and electric pulse regulation and control device and method based on electrical impedance feedback, and the device comprises a bioelectrical impedance detection module which is provided with a flexible wearing hardware part, and the flexible wearing hardware part is provided with an electrode for collecting a skin electrical impedance signal, the bioelectrical impedance detection module determines a target acupuncture point area for placing an electrode according to the electrical impedance signal; the initial pulse output module is used for outputting a preset initial electric pulse signal to an electrode of the flexible wearing hardware part; the dynamic cooperative control module is used for adjusting an electric pulse parameter according to a real-time skin electrical impedance signal acquired by the electrode after the initial electric pulse signal is applied to the target acupuncture point area through the electrode; the electric pulse output module outputs electric pulse signals to an electrode according to the adjusted electric pulse parameters, acupuncture points can be accurately positioned, the electric pulse output parameters can be accurately adjusted, stimulation output is dynamically optimized through a feedback mechanism under the condition that the skin state of a patient changes or the characteristics of the acupuncture points are different, and the treatment effect and the comfort of the patient are improved.

The invention relates to an electrical impedance detection method and device, an electronic device and a storage medium, which are applied to the field of electrical impedance imaging, and the electrical impedance detection method comprises the following steps: obtaining an electrode signal of a target electrode in a measurement area; a plurality of preset electrodes are attached to the measurement area; the target electrode comprises any one of a plurality of preset electrodes; the electrode signal carries excitation electrode data and measurement electrode data generated by the target electrode; determining a fluctuation value of the target electrode according to the excitation electrode data and the measurement electrode data; when the fluctuation value is within a preset fluctuation threshold range, based on a preset electrode measurement method, electrode contact impedance of the measurement area is obtained; and generating an electrode adjustment result based on the electrode contact impedance and sending the electrode adjustment result to the client. According to the method and the device, the problem that the efficiency of electrical impedance measurement is relatively low due to the fact that whether the electrode falls off or is in poor contact cannot be quickly judged during bioelectrical impedance imaging is solved.

The invention relates to the technical field of vital sign measurement, in particular to a wearable underwater vital sign and environment monitoring system, which comprises a physiological signal acquisition module, a photoelectric volume pulse wave sensor, a bioelectrical impedance electrode, a piezoelectric sensor, a signal processing module, a signal processing module and a signal processing module, a wrist photoelectric volume pulse wave signal, a chest bioelectrical impedance signal and a neck piezoelectric signal are collected. According to the method, signal reconstruction is carried out based on energy proportion calculation, an electrocardiogram R wave peak value point position and an R-R interval time sequence are extracted, variability indexes are generated through frequency domain decomposition and frequency band energy statistics, time domain and frequency domain index values are calculated, weight distribution is carried out, and therefore comprehensive monitoring on the underwater physical sign state is achieved. The signal acquisition precision is improved by means of a multi-source signal acquisition mode, noise interference is eliminated through signal track matrix decomposition operation, and the signal reconstruction quality is ensured through feature vector energy proportion calculation.

The invention discloses an Internet of Things equipment binding method and system based on AI behavior data, and relates to the technical field of Internet of Things, and the method comprises the steps: defining Internet of Things equipment as nodes, defining a space-time interaction feature set as node attributes, obtaining the weights of edges between the nodes through calculating the behavior relevance, completing the construction of a behavior relevance topological graph, and obtaining a behavior relevance topological graph; and verifying the behavior consistency of the new equipment by using the trained GNN through the behavior association topological graph, obtaining the legality probability of the new equipment, setting a trigger threshold value, collecting implicit bioelectrical impedance data of the new equipment when the legality probability of the new equipment is smaller than the trigger threshold value, performing cross-modal feature fusion, generating a user behavior identification vector, and performing user behavior identification on the user behavior identification vector. Performing final verification to obtain a final verification result of the new equipment; according to the method, the mining of the deep behavior relationship is realized, and the limitation of a static topological structure on the mining of a dynamic interaction mode is solved.

The invention relates to the technical field of intelligent security and protection, in particular to an intelligent security and protection method and system based on the Internet of Things, and the method comprises the following steps: obtaining a bioelectrical impedance signal generated when a user touches an access control sensing area, identifying a current response condition of a differentiated frequency band, matching stored user data, analyzing the continuity of an electrical impedance parameter, and screening abnormal fluctuation data. And the fluctuation frequency and the variation amplitude in a short time are identified, and a bioelectrical impedance matching value is obtained. According to the invention, through comprehensive analysis of the electrical impedance parameters and the gait data, the individual identity can be identified and verified more accurately, the protection capability for unauthorized access is improved, through careful monitoring of physiological and behavioral characteristics, the security is significantly improved, and the stability and reliability in various environments can be adjusted and improved according to environmental changes. The capability of preventing potential security threats is enhanced for risk assessment of abnormal behaviors, and generation of security vulnerabilities is effectively prevented by monitoring behavior deviation in the identity verification process in real time.

The invention relates to a non-invasive cardiac output function detection method and device, and belongs to the technical field of biological measurement. According to the method, on the basis of pulse wave related data, electrocardiowave related data and biological impedance and biological reactance related data which are obtained through detection, the bioelectrical impedance method is adopted to calculate and obtain the cardiac output of the bioelectrical impedance method, and the pulse wave method is adopted to calculate and obtain the cardiac output of the pulse wave method. And according to the impedance signal confidence coefficient and the pulse wave signal confidence coefficient, carrying out weighted average on the cardiac output of the bioelectrical impedance method and the cardiac output of the pulse wave method, and taking the weighted average as a cardiac output detection result. The detection device comprises a pulse wave detection system, an electrocardiogram detection system, a biological impedance and reactance detection system and a host used for data processing and result display. The reliability of cardiac output detection is remarkably improved while portability is guaranteed, and accurate, continuous and portable detection of non-invasive cardiac output is achieved.

The invention relates to the technical field of medical data management and analysis, in particular to a lymphedema treatment monitoring data management method and system. Comprising the following steps: synchronously acquiring bioelectrical impedance, tissue thermodynamic characteristics and subcutaneous tissue morphological data of a patient through multi-modal data acquisition equipment; transmitting the data to a cloud processing platform in a distributed manner, and generating a three-dimensional illness state assessment map containing edema quantitative indexes through a machine learning assessment model; generating a personalized treatment strategy adjustment scheme with a confidence score based on dynamic comparison between the current evaluation map and historical data; feeding back the evaluation map and the adjustment scheme to the medical staff through the interaction terminal, and receiving a clinical correction instruction; according to the method, on the premise of strictly protecting patient data privacy, cross-mechanism multi-modal knowledge sharing can be realized, and the contradiction between a data island and a model effect is solved.

The present invention discloses a method for detecting anti-tumor by bioelectrical impedance based on pattern recognition, which includes the following steps: S1: Use a bioelectrical impedance analysis instrument to measure the impedance of the tumor area to be detected, and obtain the current, voltage and impedance values at different depths and frequencies; S2: Design a feature extraction module to extract the key features in the impedance data at different frequencies of each channel; S3: Input the extracted key features into a multi-level feature fusion module to fuse the impedance features at different channels and frequencies to form new fused features; S4: Input the fused features into an adaptive classification module and adjust the model parameters by combining an adaptive optimization method. The present invention can automatically process and classify data through a pattern recognition algorithm, and at the same time, by combining a feature extraction module, a multi-level feature fusion module and an adaptive classification module, it can effectively improve the accuracy, sensitivity and reliability of tumor detection.

A pet body fat rate measuring system disclosed by the present application comprises a body fat scale, a host and a body fat rate calculation module, the body fat scale is provided with a pet three-dimensional measuring device, a bioelectrical impedance measuring device and a weight measuring device, and the pet three-dimensional measuring device comprises a body width measuring scale, a body length measuring scale and a shoulder height measuring scale which are arranged in a relatively sliding manner. A capacitive grating displacement measuring device is formed; the body fat rate calculation module is provided with a pet body fat rate calculation model, the model is constructed through multiple groups of body shape data, electrical impedance data and body fat rate data of pets of different varieties, and the body fat rate calculation module obtains feature data, body shape data and electrical impedance data of the current pet; and calculating the body fat rate of the current pet by using the pet body fat rate calculation model. According to the utility model, the body fat scale is utilized to automatically measure and obtain the body shape data and the electrical impedance data of the pet, then the feature data of the pet and the pet body fat rate calculation model are combined to calculate and obtain the current body fat rate of the pet, the use is convenient, and the accuracy is high.

The invention provides a multi-frequency biological resistance antibody component prediction method and device and a storage medium, and relates to the technical field of biomedical signal processing. According to the method, multi-frequency current testing is carried out on different physiological segments of a subject, an initial training data set is constructed in combination with basic information of the subject, the fine capturing ability of the multi-frequency bioelectrical impedance technology on human body physiological information is brought into full play, and individual features are fused to improve data dimensions and relevance; the quality of training data is guaranteed through data preprocessing, and a reliable foundation is laid for model training; and a component prediction model is constructed and optimized by a multi-task regression loss function, so that volume component prediction indexes can be efficiently output, and the prediction accuracy and stability of different indexes can be improved. According to the method, the problems of single data dimension and insufficient model precision in traditional volume component prediction are effectively solved, and a systematic and efficient scheme is provided for precise volume component prediction.

This application provides a bioelectrical impedance measurement method, circuit, chip, and electronic device. The method includes: obtaining a first contact impedance value corresponding to a first excitation electrode and a second contact impedance value corresponding to a second excitation electrode through a preset method; determining the transmission direction of an excitation current between the first and second excitation electrodes based on the first and second contact impedance values; controlling the excitation current to transmit between the first and second excitation electrodes according to the transmission direction; determining a target voltage value between a first and second measuring electrode, and calculating the impedance of the measured object based on the target voltage value; wherein the first and second contact impedance values ​​are different. This application can reduce the influence of contact impedance on the bioelectrical impedance measurement results, reduce the measurement error of bioelectrical impedance, and improve the accuracy of bioelectrical impedance measurement.

The present invention discloses a method for detecting cancer by bioelectrical impedance based on deep learning, comprising the following steps: S1: obtaining the electrical signal data of benign and malignant samples of cancer tissues by using an electrical impedance imaging system and combining it with physiological data; S2: extracting global features of the electrical signals by using a matrix decomposition module; S3: taking the result of global feature extraction and the original electrical signal data together as new inputs and further extracting signal features by using data dependency; S4: integrating the extracted feature maps into a final classification result. The present invention uses an electrical impedance imaging system to obtain the electrical signal data of cancer tissues, performs global feature extraction through a matrix decomposition module, and at the same time takes the result of global feature extraction and the original electrical signal data together as new inputs, and further extracts signal features by using data dependency, which can improve the utilization rate of data and further enhance the accuracy and reliability of cancer detection.

The invention relates to the technical field of intelligent body fat measurement, and provides a body composition parameter adaptive calibration method and system based on an intelligent body fat scale. The method comprises the following steps: acquiring real-time weight data and real-time bioelectrical impedance signals of a user and real-time temperature and humidity data of a current environment; based on a preset bioelectrical impedance model, in combination with the real-time weight data, the real-time bioelectrical impedance signal and the real-time temperature and humidity data, initial body composition parameters are calculated and obtained; performing feature extraction on the calibration training data set according to a preset adaptive calibration algorithm to construct a real-time calibration model according to the adaptive calibration algorithm; and inputting the currently collected real-time weight data, the real-time bioelectrical impedance signal and the real-time environmental parameters to a real-time calibration model, outputting calibration correction parameters, and performing adaptive calibration on the initial human body composition parameters according to the calibration correction parameters to obtain target human body composition parameters. According to the invention, a systematic solution is provided for accurate measurement of the intelligent body fat scale.

The invention discloses an intelligent biological eardrum based on individualized 3D printing and a preparation method thereof, and belongs to the technical field of medical instruments, and the eardrum comprises an individualized eardrum substrate, a PEDOT: PSS flexible electrode array integrated on the inner surface of the substrate, a multi-frequency bioelectrical impedance detection module, a VCT module and a central processing unit. Through multi-frequency bioelectrical impedance detection and VCT imaging, an auricle conductivity distribution map can be reconstructed, a nerve active area can be accurately identified, and personalized transcutaneous nerve electrical stimulation parameters can be generated. The invention further provides a corresponding intelligent nerve regulation and control system, a preparation method and a parameter adjustment method based on physiological signal feedback, an integrated solution from individualized manufacturing, precise recognition to self-adaptive closed-loop regulation and control is achieved, and the intelligent nerve regulation and control system is particularly suitable for individualized nerve regulation and control treatment of irritable bowel syndrome and other diseases; the problems of low stimulation precision, lack of individuation, poor comfort, no closed-loop feedback and the like in the prior art are effectively solved.

The invention provides a multi-modal sarcopenia diagnosis system, method and device and a medium, a technical framework of the multi-modal sarcopenia diagnosis system, method and device integrates multi-modal data integration, a complex neural network technology, a personalized meta-learning algorithm and an interpretable artificial intelligence technology, and aims to create an accurate sarcopenia diagnosis tool with high adaptability. The method not only makes a breakthrough on the basis of a traditional bioelectrical impedance technology, but also shows a huge potential of realizing efficient and personalized diagnosis and monitoring in diversified medical environments through application of multi-modal data fusion and an intelligent model.

The invention belongs to the technical field of biomedical engineering, and discloses a multi-frequency intelligent bioelectric current cooperative modulation method. The method comprises the following steps: collecting human physiological state data of each sub-treatment area in real time, performing bioelectrical impedance analysis and connectivity analysis respectively, and constructing a differentiated bioelectrical impedance area; comprehensively evaluating the tissue penetration degree of each bioelectrical impedance region; collecting treatment characteristic data, combining the treatment characteristic data with the tissue penetration degree, extracting the energy conduction efficiency of each bioelectrical impedance region, and generating an energy conduction efficiency curve; performing quantitative evaluation on the tissue response factors of each bioelectrical impedance region, and generating a response type value matrix; integrating the energy conduction efficiency curve, the response type value matrix and the treatment characteristic data, and dynamically formulating a physiotherapy parameter optimal configuration scheme; the adaptive capacity and the application effect of the bioelectric current in refined regulation and personalized treatment can be improved, so that the treatment effect and the comfort are effectively improved.

The invention is suitable for the technical field of biological measurement, and particularly relates to an intelligent body fat scale and a multi-frequency bioelectrical impedance measurement data fusion method thereof, and the method comprises the steps: carrying out the multi-frequency bioelectrical impedance measurement of a target measurement object, and obtaining a plurality of groups of bioelectrical impedance data sequences under different frequency bands; performing feature analysis on each bioelectrical impedance data sequence, determining a data analysis result corresponding to each frequency band, and determining data reliability corresponding to each frequency band according to the data analysis result; the fusion weight corresponding to each frequency band is determined based on the data reliability, and the limitation of a traditional fixed weight or simple average fusion method is overcome; and according to the fusion weight corresponding to each frequency band, weighting adjustment is performed on the corresponding bioelectrical impedance data sequence to obtain the adjustment data, and fusion processing is performed based on each adjustment data to obtain the measurement data, so that the technical advantages of multi-frequency measurement are fully exerted, and the overall competitiveness of the body fat scale product is enhanced.

The invention discloses a bioelectrical impedance tumor identification method based on multi-modal fusion. The method comprises the following steps: S1, acquiring electrical impedance signal data and tissue slice image data of a tumor sample; the tissue slice image data are pathological slice images corresponding to tumor tissues; s2, inputting the electrical impedance signal data and the tissue slice image data into a multi-modal feature coding module for asymmetric feature coding; s3, performing splicing and weighted fusion on the global semantic feature vector and the image semantic feature vector to form a multi-modal feature vector; and S4, inputting the multi-modal feature vector into a decoding network for classification prediction, and outputting a tumor identification result. According to the method, an asymmetric coding structure and a weighted fusion mechanism are constructed by fusing the electric signal modality and the image modality, so that the utilization efficiency and the feature discrimination capability of multi-modal information can be effectively improved, and the accuracy and the reliability of tumor recognition are remarkably improved.

The invention provides a physical weeding method and system based on double closed-loop feedback, and belongs to the technical field of intelligent agriculture. The method comprises the steps that before electroporation operation is executed, a target effectiveness index is calculated through first-time bioelectrical impedance measurement, and accurate contact with weed meristem targets is ensured in a closed-loop feedback mode; after the contact is effective, generating optimal electric pulse parameters in a self-adaptive manner according to physiological characteristics of weeds, and executing operation; after operation, the irreversible cracking index of the cells is calculated through subsequent bioelectrical impedance measurement, the operation effect is quantitatively evaluated, and supplementary strike is triggered. The problems that existing physical weeding is low in precision, high in energy consumption and unreliable in effect are solved through double closed loops of verification before contact and confirmation after effect in combination with an adaptive energy strategy, and the precision, reliability and energy utilization efficiency of operation are remarkably improved.

The invention discloses a bioelectrical impedance cancer detection method based on deep learning, and the method comprises the following steps: S1, obtaining electrical signal data of a benign sample and a malignant sample of a cancer tissue through an electrical impedance imaging system, and combining the electrical signal data with physiological data; s2, performing global feature extraction on the electric signal by using a matrix decomposition module; s3, taking a global feature extraction result and original electric signal data as new input, and further extracting signal features by using a data dependency relationship; and S4, mapping and integrating the extracted features into a final classification result. The electrical signal data of the cancer tissue is acquired by using the electrical impedance imaging system, global feature extraction is performed through the matrix decomposition module, the result of global feature extraction and the original electrical signal data are used as new input, and the signal features are further extracted by using the data dependency relationship, so that the utilization rate of the data can be improved; and the accuracy and reliability of cancer detection are further improved.

The invention relates to the technical field of medical instruments and clinical nursing, in particular to a radial artery puncture navigation system based on multi-modal sensing and digital projection, which comprises the following steps: a multi-modal blood vessel signal acquisition module, which is used for acquiring a blood vessel pulsation signal on the wrist of a patient through a photoplethysmography sensor array, the strongest pulsation line is positioned through the pressure sensor array, and the blood vessel depth and artery and vein distinguishing information are obtained through the bioelectrical impedance sensor array. According to the invention, through multi-modal blood vessel signal acquisition, blood vessel dynamic deformation modeling, personalized digital twinning construction and dynamic updating, projection path generation and control, and projection output and real-time error compensation, real-time visual projection of a blood vessel center line and local deformation is realized; therefore, the problem that puncture positioning is not accurate due to the fact that dynamic changes of blood vessels cannot be accurately reflected due to the fact that most traditional radial artery puncture adopts a hand feeling positioning method is solved.

Provided is a system that more accurately estimates the degree of urine accumulation by solving a problem in which the influence of a change in the amount of urine accumulation is obscured when the biological impedance in the vicinity of the bladder is affected as the muscles contract. This urine accumulation degree estimation system (2) comprises: a measurement means for measuring at least two types of biological impedance by energizing a bladder (60) at at least two different frequencies; and an estimation means that estimates the urine accumulation degree of the bladder (60) on the basis of the at least two types of biological impedance. The at least two frequencies include a frequency at which the bladder (60) and the muscles (66) around the bladder can be energized, and a frequency at which the muscles (66) around the bladder can be energized.

The invention provides an intelligent push-up device which comprises a base provided with a first bearing part and a second bearing part, and electrode plates are arranged on the first bearing part and the second bearing part respectively; the bioelectrical impedance acquisition module has a first acquisition frequency and a second acquisition frequency, and first dynamic electrical impedance data of a target object in a motion state and first static electrical impedance data of the target object in a static state are acquired at the first acquisition frequency; the second acquisition frequency acquires second dynamic electrical impedance data of the target object in a moving state and second static electrical impedance data of the target object in a static state; and the data processing module is connected with the bioelectrical impedance acquisition module and is used for evaluating the muscle amount of the target object according to the first static electrical impedance data, the second static electrical impedance data and the height information and the weight information of the target object and determining muscle contraction information according to the first dynamic electrical impedance data and the second dynamic electrical impedance data.

The invention discloses a biological high-frequency electrical impedance characteristic real-time acquisition method and system, and the method comprises the steps: transmitting a high-frequency excitation signal to a first electrode pair through a coaxial shielding line, and obtaining a voltage signal U0 of a sampling resistor R0 connected in series with a signal transmission loop of the first electrode pair; a signal transmission loop of the electrode pair I is connected with a contact resistor RL in series to form an RC filter circuit together with the coaxial shielding line, and passive RC compensation is arranged between the electrode pair I and the living body; the voltage UZ on the impedance of the living body is obtained by the coaxial shielding line through the electrode pair II, and passive RC compensation is arranged between the electrode pair II and the living body; according to the sampling resistance R0, the voltage U0 and the voltage UZ, the bioelectrical impedance value RZ of the to-be-measured organism is calculated.

The invention discloses a portable human heart imaging hardware system based on bioelectrical impedance. The system comprises an excitation signal processing module, a detection signal conditioning module and a digital processing module. The excitation signal processing module adopts a DAC (Digital-to-Analog Converter) to generate an excitation signal, and after voltage amplification and band-pass filtering, the excitation signal drives an excitation electrode through an analog switch; the detection signal conditioning module switches a measuring electrode through an analog switch, a programmable instrumentation amplifier is combined with an RDAC digital potentiometer to realize self-adaptive gain adjustment of a detection signal, flexible gain of the signal is realized, and the signal is sent to an ADC after being filtered; the digital processing module carries out digital demodulation on the collected signals, extracts amplitude and phase information and then transmits the amplitude and phase information to an upper computer for image reconstruction. The flexible signal gain mechanism of the system realizes a full-custom signal gain curve, breaks through the fixed magnification gain limitation of the traditional integrated PGA, adapts to the impedance change of different individuals and experimental media, and improves the signal-to-noise ratio of the system, thereby obtaining more accurate cardiac impedance acquisition data.