1125 results about "Bio electricity" patented technology

Method and systems are described for identifying electrodes for stimulation of a patient using a stimulation system. The stimulation system includes at least one stimulation lead implanted in a patient and having electrodes disposed thereon. The method includes obtaining a plurality of bioelectrical signals, wherein each of the bioelectrical signals is obtained using a different one, or a different combination, of the electrodes; analyzing a dataset including the bioelectrical signals to identify at least one fundamental component of the dataset, each of the at least one fundamental component identifying a contribution of one or more of the electrodes to the fundamental component; and using at least one of the at least one fundamental component to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the at least one of the at least one fundamental component.

An electrode device for a living body includes: an electrode sheet including at least a pair of electrodes that receives a bioelectrical signal, a wiring member that transmits the bioelectrical signal received, a sensor module that outputs a signal relating to the bioelectrical signal to an exterior, a connector that connects the bioelectrical signal transmitted, to the sensor module, and a sheet member that supports the electrodes, the wiring member, the sensor module, and the connector; and an adhesive sheet that can be attached to a living body while covering the electrode sheet.

The invention belongs to the field of biomedical engineering, and particularly relates to a puncture operation tissue recognition system based on bioelectrical impedance, which comprises a dual-electrode impedance needle used for collecting electric signal data in real time; the feedback display equipment is internally integrated with a trained random forest model and is used for processing the electric signal data according to the trained random forest model to obtain a classification result; the multi-frequency bioelectrical impedance analyzer is used for displaying the real-time acquired waveform and displaying the classification result of the random forest model at the same time; the dual-electrode impedance needle comprises a needle tube structure and an electrode structure located at the head of the needle tube structure. According to the invention, electric signals of different tissues are effectively obtained, and measurement errors and signal interference are reduced.

The invention relates to an implantable biological electrode and a manufacturing method of an implantable glucose monitoring probe. The manufacturing method of the implantable biological electrode comprises the following steps: S1.1, providing a flexible substrate; s1.2, silk-screening silver ink on the first surface of the flexible substrate to form a non-implanted conductive region of the first functional electrode; s1.3, silk-screening carbon ink on the first surface of the flexible substrate to form an implanted conductive region of the first functional electrode, and forming a series structure by the non-implanted conductive region and the implanted conductive region to jointly form a conductive region of the first functional electrode; and S1.4, an insulating layer is screen-printed on the first surface of the flexible substrate, the insulating layer covers the conductive region of the first functional electrode, and the working region is exposed. According to the functional electrode, the characteristic of good biocompatibility of the carbon circuit layer and the characteristics of small resistance and high signal transmission efficiency of the silver circuit layer are fully utilized, the circuit impedance of the functional electrode is effectively reduced, the signal transmission efficiency is improved, and the biocompatibility and long-term stability of an implantation area are ensured.

The invention relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

The invention discloses percutaneous nerve field vagus nerve stimulation diagnosis and treatment equipment and a diagnosis and treatment method thereof, and particularly relates to the technical field of neuromedicine, intelligent medical equipment and multi-mode physical therapy. The equipment comprises a neural signal acquisition module, a nerve positioning module, a data processing and analysis module, a treatment execution module and a diagnosis result output module; the system is further provided with a battery power management module, a self-adaptive closed-loop cloud platform and a wearable fixing device, by collecting bioelectricity signals, light absorptivity and temperature data of an ear nerve area, ear target cranial nerve branches are positioned, a dynamic diagnosis map is constructed, and a diagnosis result and personalized treatment parameters are generated. Electric pulse, near-infrared light and local thermal therapy multi-mode collaborative stimulation is adopted for treatment, and parameters are monitored and adjusted in real time through a cloud platform. According to the invention, accurate diagnosis and personalized treatment can be realized, the medical efficiency and the treatment effect are improved, and an effective means is provided for nerve disease diagnosis and treatment and pain relief.

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 relates to a method for automatically identifying and removing ocular artifacts of multi-lead electroencephalogram signals based on a CycleGAN model, and belongs to the technical field of bio-electricity signal processing. The method comprises the steps that electroencephalogram signal data of a tested person is collected and preprocessed; extracting ocular artifacts, adding the ocular artifacts into an ocular artifact signal library, and adding the electroencephalogram signals without the ocular artifacts into a clean electroencephalogram signal library; a clean electroencephalogram signal and an ocular artifact are randomly selected, the ocular artifact is added into the clean electroencephalogram signal to obtain a noisy electroencephalogram signal, the clean electroencephalogram signal and the noisy electroencephalogram signal are input into a CycleGAN model, and the model is trained; the collected multi-lead electroencephalogram signal data are preprocessed and then input into the trained model, and eye artifacts of the multi-lead electroencephalogram signals are removed. Effective information in the electroencephalogram signals can be reserved for other electroencephalogram signal analysis or application, and eye artifacts in the multi-lead electroencephalogram signals can be recognized and removed.

The invention relates to the technical field of medical monitoring and early warning, in particular to an acute kidney injury risk assessment and early warning system which comprises a urine bioelectrical impedance analysis module, a data acquisition module, a data preprocessing module, a model construction module, a risk assessment module and an early warning module. Wherein the urine bioelectrical impedance analysis module is used for acquiring urine electrical impedance spectroscopy data; the data acquisition module is used for acquiring physiological parameters of a patient and laboratory inspection indexes; the data preprocessing module is used for extracting urine electrical impedance characteristic parameters, physiological characteristic parameters and laboratory characteristic parameters; the model construction module is used for constructing an acute kidney injury risk assessment model; the risk assessment module is used for calculating the risk score and the risk grade of the acute kidney injury of the patient. According to the system and the method, early-stage accurate evaluation and graded early warning of the acute kidney injury are realized through multi-source data fusion and intelligent analysis technologies, timely intervention of medical staff is facilitated, and the accuracy and response efficiency of risk detection are remarkably improved.

A system for detecting an anomalous biologic event in a person includes a wearable device for monitoring skin surface sites of a person. The wearable device includes an electrode for contacting skin sites, an electronic stimulus source with a surface area to provide stimulus, and a sensor placed adjacent the skin sites to sense physiological data. A processor is coupled to the wearable device and is configured to: cause the stimulus source to generate a stimulus; excite the electrode to trigger monitoring the skin sites; cause operation of the sensor; receive bioelectrical data from each skin site; receive physiological data from the sensor; continuously compute a difference in the received bioelectrical data for a duration; compute a difference in the received physiological data at time intervals; and generate, based on the computation, an assessment including a likelihood of occurrence of the anomalous biologic event.

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 provides a sleep-aiding medicine pillow dynamic regulation and control method and system based on physiological parameter analysis. According to the method, pressure data and sleep bio-electricity signals of a contact area of a user are collected in real time, a characteristic frequency band proportion and a heart rate fluctuation parameter are analyzed, and a correlation model is established; calculating a bioelectricity signal energy integral value of the characteristic frequency band based on the model, and quantizing to generate a bioelectricity intensity index; the heat conduction efficiency is changed by adjusting the molecular density of the temperature-sensitive material, and a contact layer heat transfer dynamic instruction is generated; pressure feedback and instruction waveform characteristics are combined, the micropore on-off duty cycle is adjusted, and the opening duration of unit time is controlled; the drug release rate is synchronously adjusted, so that the response waveform and the heat conduction instruction are dynamically balanced, and closed-loop regulation is formed. According to the application, through multi-parameter dynamic analysis and feedback adjustment, accurate cooperative control of heat conduction and medicine release of the medicine pillow is realized, and the matching degree of sleep-aiding substance release and sleep rhythm is optimized.

The invention discloses a bio-electricity signal acquisition electrode and an efficient electroencephalogram signal acquisition method, and relates to the technical field of signal processing. The method comprises the following steps: step 1, carrying out electrode-tissue interface dynamic impedance modeling based on ion diffusion and a capacitance effect; calculating density distribution of the conductive coupling agent based on the impedance function; 2, calculating a signal quality index; performing signal enhancement according to the signal quality index to obtain an enhanced signal; 3, performing feature extraction on the enhanced signal to obtain a feature signal; performing dynamic noise suppression according to the characteristic signal to obtain a noise suppression result; and 4, performing signal reconstruction according to a noise suppression result to obtain a reconstructed signal, and completing electroencephalogram signal acquisition. According to the electrode design, the contact quality is improved, the distribution of the conductive coupling agent is more uniform, and long-time stable monitoring is ensured. Meanwhile, the definition and fidelity of the signal are effectively improved through adaptive noise suppression and a signal reconstruction mechanism.

A system for detecting an anomalous biologic event in a person includes a wearable device for monitoring skin surface sites of a person. The wearable device includes an electrode for contacting skin sites, an electronic stimulus source with a surface area to provide stimulus, and a sensor placed adjacent the skin sites to sense physiological data. A processor is coupled to the wearable device and is configured to: cause the stimulus source to generate a stimulus; excite the electrode to trigger monitoring the skin sites; cause operation of the sensor; receive bioelectrical data from each skin site; receive physiological data from the sensor; continuously compute a difference in the received bioelectrical data for a duration; compute a difference in the received physiological data at time intervals; and generate, based on the computation, an assessment including a likelihood of occurrence of the anomalous biologic event.

The invention discloses a self-driven bio-electro-Fenton sewage treatment method based on granular sludge electroactivity domestication. The sewage treatment method comprises the following steps: firstly, inoculating anaerobic granular sludge into an anode chamber of a microbial fuel cell, fixing a clustered carbon fiber electrode in a biological anode chamber as an electrode collector, taking a cathode as an oxygen reduction cathode, and performing electrochemical enrichment to obtain electroactive granular sludge; then, preparing an iron-cobalt diatomic catalyst from the nitrogen-doped graphene oxide, cobalt phthalocyanine and iron phthalocyanine; and finally, adding the iron-cobalt diatomic catalyst into the electroactive granular sludge to form a hybrid structure, controlling dissolved oxygen in a reaction system, and degrading organic pollutants by adopting an SBR (sequencing batch reactor). The biological electro-Fenton technology has a better effect of removing refractory organic pollutants, and compared with a traditional electro-Fenton method, the self-Fenton technology can achieve the activity of molecular oxygen without external electric energy input.

The invention discloses an LLM-based scheduling management system, which belongs to the field of industrial and commercial enterprise scheduling management systems, adopts a data acquisition technology driven by bio-electricity signals, enriches data acquisition forms, can acquire various bio-electricity signals such as electroencephalogram, electrocardio, myoelectricity and the like, and improves the scheduling efficiency. And through a multi-channel parallel data transmission architecture and an advanced signal coding technology, the accuracy and stability of data transmission are ensured, key data in a human physiological state or a special environment are reflected, and some application scenes with relatively high requirements on data comprehensiveness and accuracy are met. The LLM-based scheduling module generates an optimal scheduling scheme by means of the powerful analysis and reasoning capability of a large language model in combination with a genetic algorithm and a simulated annealing algorithm, quantitative evaluation is carried out through a multi-objective optimization algorithm, various complex constraint conditions can be fully considered, and efficient scheduling is realized.

The invention relates to the technical field of chronic kidney disease screening, and discloses an intelligent chronic kidney disease early screening instrument which comprises a physiological parameter detection module configured to synchronously collect multi-dimensional bio-electricity signals, optical characteristic parameters and body fluid biochemical parameters of a subject and output original signals; the multi-modal data processing module is in communication connection with the physiological parameter detection module and is configured to receive original signals, perform time domain and frequency domain conjoint analysis and construct a parameter incidence matrix; the wireless communication module is configured to realize encrypted interaction between the screening data and the cloud server and block chain evidence storage; and the man-machine interaction module is configured to display the risk level map in real time. According to the method, holographic dynamic correlation modeling of kidney function related micro-electrophysiological fluctuation and optical metabolism characteristics is realized, and a specific cross-modal pathological correlation mode of early renal injury can be effectively identified based on a time-frequency domain combined characteristic extraction and dynamic coupling algorithm.

The invention discloses a lower limb muscle fatigue state recognition method and system based on multi-modal feature fusion, and the method comprises the following steps: S1, arranging a multi-modal sensor on a to-be-detected object, and carrying out the signal collection through the multi-modal sensor; bioelectricity signals, muscle form information and force position information are obtained through a multi-mode sensor; and S2, obtaining a fatigue state through a fatigue state recognition model according to the bio-electricity signal, the muscle form information and the force position information. The invention discloses a lower limb muscle fatigue state recognition method and system based on multi-modal feature fusion. The method and system have the advantages of being high in detection precision, fast in dynamic response and high in anti-interference performance.

The invention discloses an electroencephalogram emotion recognition method based on a hierarchical multi-scale map neural network, and belongs to the technical field of emotion calculation and bio-electricity signal processing. According to the method, structural innovation is carried out on the basis of a traditional graph neural network, a multi-scale graph network model with layer-by-layer feature enhancement from local to global is constructed, and the core lies in optimization and improvement of graph structure modeling, a feature fusion mechanism and global dependency capture capability. According to the model, local graph convolution is combined with a random walk normalized matrix, and inter-electrode spatial dependence modeling is enhanced; a virtual brain region center and an SE channel attention mechanism are introduced into the mesoscale layer, and cross-brain region emotional features are effectively extracted; a simplified Transform structure is adopted in a global layer to capture a long-distance cross-time step feature relationship, and finally, the feature expression capability and robustness are improved in combination with a diffusion generation network. According to the method, the model weight is kept, meanwhile, high-precision modeling of the complex emotion mode of the EEG signal is achieved, the accuracy, stability and cross-subject generalization ability of emotion recognition are remarkably improved, and the novel efficient emotion recognition method suitable for the multi-source physiological signal scene is provided.

The invention relates to a liquid metal dual-network hydrogel patch with intelligent adhesion as well as a preparation method and application thereof, and belongs to the field of bioelectric sensing materials. By utilizing the characteristic that a polymer chain in the hydrogel has temperature-dependent phase change, the hydrogel is endowed with the advantages of intelligent adhesion and on-demand removal without pain and stimulation, and meanwhile, the hydrogel has good mechanical strength, is low in cost and convenient to operate, is suitable for large-scale production, popularization and application, and has wide application prospects. Wide application values are realized in the fields of wound dressings, postoperative care and the like.

The invention relates to a livestock health early warning and intelligent driving mechanical auxiliary system based on bio-electricity signal monitoring, and belongs to the technical field of animal husbandry intelligent management. Aiming at the problems of single parameter, strong driving irritability and low efficiency of a traditional monitoring method, the system is used for generating a health score in real time by adopting a CNN-LSTM hybrid model through data fusion of a bio-electricity signal acquisition module (electrocardio, myoelectricity and respiratory frequency) and an environment sensor, and driving an intelligent driving machine in combination with a deep reinforcement learning algorithm, so that the driving effect is improved. The abnormal livestock is guided to be isolated in a non-intrusive mode through ultrasonic directional wave beams and low-intensity laser. The monitoring accuracy is larger than or equal to 95%, the driving efficiency is improved by 60%, all-weather stable operation (availability is larger than or equal to 98%) is achieved, the epidemic disease risk and animal stress are effectively reduced, and an efficient intelligent solution is provided for large-scale breeding.

The invention relates to the technical field of biological consciousness state monitoring, in particular to an emergency patient consciousness state monitoring system based on a bio-electricity signal, and the system obtains a theta rhythm signal, an alpha wave signal and a gamma wave oscillation signal through an electroencephalogram signal acquisition module and a signal extraction module; obtaining an effective phase amplitude coupling event and an occurrence time position thereof through a phase amplitude coupling event time position positioning module; based on a patient consciousness state monitoring module, obtaining a patient consciousness state monitoring index, and fitting the patient consciousness state monitoring index with the time sequence to complete patient consciousness state monitoring. By means of the method, the theta-gamma cross-brain region phase amplitude coupling (PAC) event can be accurately positioned, the limitation that space-time positioning of the PAC event is fuzzy and dynamic tracking lags in the traditional technology is broken through, millisecond-level high-resolution monitoring of the consciousness state of an emergency patient is achieved, and a quantifiable neural electrophysiology basis is provided for real-time intervention of disturbance of consciousness.

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 a key operation method and a related device. A pressure sensor, an optical sensor, a bioelectric sensor and a temperature sensor can be arranged in a first key of the electronic equipment. The electronic equipment can accurately identify whether the touch operation acts on the first key and the type of the touch operation according to the data detected by the sensor in the first key. Therefore, the first key can support various types of touch operations (such as short-press operation, long-press operation, light-press operation, heavy-press operation, up-slide operation and the like). According to the method, a user can conveniently carry out different types of touch operations on the first key so as to control the electronic equipment to realize multiple functions.

The invention relates to the technical field of mechanical arm control, and discloses a mechanical arm continuous control method based on bioelectricity signal recognition, and the method comprises the steps: collecting electromyographic signals of an arm muscle group, and carrying out filtering and denoising processing on the electromyographic signals; and the filtered and denoised electromyographic signals are converted into mechanical arm control parameters through the correlation mapping relation model, interpolation optimization based on control continuity is conducted on the mechanical arm control parameters, the mechanical arm control parameters are converted into joint torque parameters and tail end torque parameters, and mechanical arm control is conducted. According to the method, feature extraction is conducted on the filtered and denoised electromyographic signals through a correlation mapping relation model, time domain features and frequency domain features are extracted respectively, features representing electromyographic signal modes and features representing signal amplitudes are mapped into mechanical arm control parameters, and the mechanical arm control parameters are calculated based on an error value sequence after interpolation optimization. And nonlinear optimization is carried out on mechanical arm control parameters, a more continuous and smooth optimization result is obtained, and mechanical arm continuous control based on bioelectricity signal recognition is achieved.

The invention relates to the technical field of bio-electricity signal acquisition, and particularly discloses a multi-channel bio-electricity signal acquisition system and a multi-channel bio-electricity signal acquisition method.The multi-channel bio-electricity signal acquisition system comprises a bio-electricity signal acquisition unit and a data processing unit; the data processing unit is used for compensating the original electrophysiological signals according to the real-time impedance value transmitted by the analog-to-digital conversion unit, performing primary screening on the original bio-electricity signals through the lowest quality threshold value, and performing primary compensation on the original bio-electricity signals according to the real-time impedance value, the standard impedance value and the breathing phase; according to the method, more flexible quality requirement setting is realized, the quality threshold setting difficulty is reduced, secondary screening is performed on the corrected bio-electricity signals through the highest quality threshold, and secondary compensation is performed on the corrected bio-electricity signals by adopting the LSTM network, so that the influence of bio-impedance fluctuation on the bio-electricity signals is reduced, and the quality of the bio-electricity signals is improved. And the acquisition quality and stability of the bio-electricity signals 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 embodiment of the utility model discloses a three-dimensional array of integrated biological photoelectrodes, which comprises a plurality of electrode array layers, and the electrode array layers are laminated and connected along the longitudinal direction; and the electrode array layer is formed by arranging a plurality of integrated biological photoelectrodes in parallel. Integrated biological photoelectrodes with different lengths, LED wavelengths and other characteristics are arranged in multiple dimensions, each integrated biological photoelectrode can be provided with an independent bonding pad set, and during working, the device is inserted into biological brain tissue and immersed in a biological solution, so that the device is formed. And a photodiode is lightened, and a bioelectric signal is collected through a microelectrode array, so that the nerve cells are accurately controlled, and meanwhile, the collection range and the collection density of the nerve signals are effectively improved.

The invention relates to a sports injury real-time early warning and health optimization system based on multi-mode infrared thermal imaging and three-dimensional skeleton posture fusion, and belongs to the field of sports health management. The problems of motion analysis lagging, damage identification missing and cross-scene data splitting of a traditional monitoring means are solved. According to the technical scheme, the system comprises a multi-modal motion data sensing module which synchronously collects human thermodynamic distribution, three-dimensional skeleton postures and surface electromyogram signals through an infrared thermal imaging camera, an RGB-D camera and a bioelectric sensor; an AI driving training optimization engine is used for generating an adjustment instruction in real time based on a muscle thermal gradient and joint trajectory deviation degree dual-threshold model; and the full-scene intelligent management platform executes millisecond damage early warning, metabolic optimization and school-family health data closed-loop management. The technical effects are that the exercise safety guarantee is fundamentally improved, the training is scientifically optimized, the metabolism management accuracy is broken through, and the teaching resource allocation is intensive.

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.