918 results about "Bio electricity" patented technology

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.

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.

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 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 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 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.

The invention belongs to the technical field of exercise rehabilitation, and particularly discloses an exercise rehabilitation effect quantitative evaluation method which comprises the following steps: S1, a data acquisition unit acquires data of a patient in an exercise process through fusion of a multi-source sensor; s2, muscle electrical activity signals in the movement process of the patient are collected through bio-electricity signals, and the activation degree and the fatigue condition of muscles in rehabilitation movement are known; s3, acquiring muscle electrical activity signal data according to the acquired multi-source fusion data and the bio-electricity signals, and performing rehabilitation evaluation on the model; s4, according to the output of the evaluation model, a quantitative rehabilitation effect evaluation report is generated, and the report content comprises numerical values of various quantitative indexes, comparative analysis of rehabilitation targets and comprehensive scores of rehabilitation effects; an exercise rehabilitation effect quantitative evaluation system comprises a data acquisition system and a rehabilitation evaluation module. The method solves the problems that a traditional evaluation method is high in subjectivity and single in data, and has the advantages of being objective, comprehensive and accurate in quantification.

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 relates to a chin electrical stimulation combined upper airway muscle group training system and method based on bioelectric feedback, and belongs to the field of upper airway muscle group training. Comprising a pressure detection module used for detecting a pressure value in an upper respiratory tract muscle group training process; the myoelectricity acquisition module is used for detecting myoelectricity numerical values in the training process of the upper respiratory tract muscle group; the electrical stimulation output module is used for stimulating partial nerve excitation and muscle contraction of the muscle group of the upper respiratory tract of the trainee; the signal conditioning module is used for carrying out preprocessing and analog-to-digital conversion on signals transmitted among the modules; and the control module is used for controlling signal interaction in the whole training process. And the display interaction terminal is used for interacting with the trainee. Based on bioelectricity feedback, a new individualized electrical stimulation and upper airway muscle group training treatment mode is newly established, accurate electrical stimulation and training intensity are formulated, and fatigue and injury are avoided.

The invention discloses a molar signal monitoring system and method based on optimized variational mode decomposition, and relates to the technical field of bio-electricity signal detection and processing. The system comprises a signal acquisition module used for acquiring temporalis surface electromyogram signals; the signal preprocessing module is used for performing preprocessing and envelope calibration on the temporalis surface electromyogram signals; the GWO-VMD algorithm module is used for performing multi-modal characteristic decomposition on the target temporalis surface electromyogram signals by using a GWO-VMD algorithm; the feature extraction module is used for extracting time domain features and frequency domain features from the multiple modal components; the classification and recognition module is used for classifying the feature vectors; and the real-time feedback and evaluation module is used for carrying out molar state evaluation according to the state classification result, and generating and sending a molar state monitoring result. According to the invention, different actions such as tooth grinding, tooth clenching, speaking, chewing and the like can be accurately identified, so that the signals of tooth grinding and tooth clenching are extracted for quantitative calculation and evaluation, and higher monitoring precision and real-time performance are achieved.

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 present invention provides a method and system for fabricating complex human tissues by integrating bio-electrospraying, cell electrospinning merged with 3D multi material microfluidic bioprinting, and future additions of magnetic and acoustic levitation technologies. The fabricated tissues serve as an alternative to animal testing in drug discovery processes, aiding in the prediction of human physiological responses to various drug compounds.

The invention relates to the field of electrical impedance detection, in particular to a resistance impedance detection probe control method, device and system, and the method comprises the steps: detecting the liquid state characteristics of the environment where a resistance impedance detection probe is located, so as to estimate the liquid ion flowing space distribution information of the surface of the probe; estimating a bioelectricity signal drift change characteristic formed by liquid ions in a detection coverage range corresponding to the electrode, and adjusting an electric signal detection parameter of the electrode; adjusting signal processing parameters of signal processing ends corresponding to the electrodes on the basis of change characteristics of bioelectricity signal queues acquired by all the electrodes on the surface of the probe; and based on the resistance impedance information generated by the signal processing end and the position change information of all the electrodes on the surface of the probe in the environment, a resistance impedance distribution image is generated, so that a resistance impedance abnormal area in the environment is determined. The working state of the probe can be adjusted in the changing process of the liquid environment in the human tissue, and therefore the bio-electricity signal of the liquid environment is accurately detected.

The invention provides a hand rehabilitation training glove and system based on electromyographic signal guidance and pressure feedback, and the glove comprises a flexible glove main body which is made of an elastic material, is designed to fit the anatomical structure of a human hand, and is used for collecting bioelectrical signals generated by the remaining hand and forearm muscles of a patient when the patient actively intends to generate power; the embedded signal processing module is used for carrying out amplification, filtering, noise reduction and analog-to-digital conversion processing on the collected original electromyographic signals; and the main control unit is used for calculating time domain and frequency domain characteristic values of the electromyographic signals and identifying the motion intention of the patient based on a preset threshold value or an adaptive algorithm. Weak electromyographic signals are collected and processed in a high-precision mode and immediately converted into local touch pressure feedback corresponding to the functions of the weak electromyographic signals, neural pathway connection between the cerebral cortex and target muscles is enhanced, training interestingness and immersion and compliance of a patient are remarkably improved, and the training effect is improved. And key power is provided for continuous long-term rehabilitation training.

The invention discloses a micro-matrix electrode for biological tissue electric signal mapping, and belongs to the technical field of bioelectrophysiological detection.The micro-matrix electrode comprises a substrate, a matrix micro-needle, a plurality of bonding pads and a plurality of wires, the matrix micro-needle is arranged on the end face of the operation end of the substrate, and the bonding pads are symmetrically arranged on the upper surface and the lower surface of the substrate respectively; the matrix microneedle is composed of a plurality of microneedles which are arranged according to a 4 * 4 square matrix, and each microneedle is connected with the corresponding bonding pad through a wire. The embodiment of the invention also discloses a preparation method of the micro-matrix electrode. The preparation method comprises the following steps: S1, processing the conversion connection part and the operation part; s2, preparing a matrix microneedle; s3, preparing a bonding pad; s4, aerosol is sprayed and deposited to obtain a plurality of wires, one ends of every two wires are connected with the corresponding microneedles, the other ends of every two wires are connected with the corresponding bonding pads, and then the micro-matrix electrode is obtained.

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.