259 results about "Autonomic nerve" patented technology

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

The invention discloses a rehabilitation training evaluation system for dealing with schizophrenia patients, and relates to the technical field of data processing, the evaluation system comprises a multi-modal sensing module, an edge intelligent processing module and a dynamic graph network evaluation module; according to the technical key points, neurophysiology, behavior tracks, cognitive functions and environmental parameters are fused to form a'microscopic neural activity-mesoscopic behavior performance-macroscopic environment interaction 'full-dimension evaluation network, for example, the recessive decoupling phenomenon of'reduced brain oxygen metabolism but normal autonomic nerve function' of a negative symptom patient can be synchronously captured, and the accuracy of the evaluation network is improved. The method comprises the following steps of: firstly, quantifying the coordination and causality of data of different dimensions through a dynamic graph network, disclosing a dynamic association path of insufficient activation of a forehead cortex, social attention distraction and cognitive task error rate increase, and providing a visual basis for mechanism research and intervention target selection.

Disclosed is a system for providing a sleep healthcare service using brainwave entrainment and autonomous nervous system control, and the system including: a user terminal configured to set a bedtime and a wake-up time, at the bedtime, output a sleep-inducing breathing method for enhancing the parasympathetic nervous system, while emitting light of non-melatonin-suppressing wavelengths, and at the wake-up time, insert and output an Alpha-Band or Beta-Band flicker for brainwave synchronization of alpha or beta waves, and output blue light to induce arousal; and a sleep healthcare server.

The invention provides a transcranial stimulation magnetic therapy closed-loop control method and system, and the method comprises the steps: determining a multi-modal physiological signal of a user, the multi-modal physiological signal at least comprising an electroencephalogram signal, an autonomic nerve physiological signal and a subjective feedback signal; constructing an individual dynamic baseline spectrum based on user multi-state data collected before treatment; the method comprises the following steps: preprocessing a multi-modal physiological signal collected in a treatment process, and extracting a real-time feature vector; calculating a comprehensive deviation index of the real-time feature vector and the individual dynamic baseline spectrum, and determining a deviation type based on the comprehensive deviation index; the adjustment amount of transcranial magnetic stimulation parameters is dynamically calculated according to the deviation type, the individual response characteristics and the historical treatment data, stimulation operation is conducted according to the adjustment amount of the transcranial magnetic stimulation parameters, and the transcranial magnetic stimulation parameters at least comprise the stimulation frequency, the intensity, the pulse width and the stimulation duration. Parameters of transcranial magnetic stimulation are adaptively changed according to changes of neuron oscillation of a patient in the treatment process.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention relates to the technical field of medical health monitoring, and discloses a multi-modal data fusion health monitoring analysis early warning method and system, and the method comprises the steps: synchronously collecting motion and heart rate data streams through a wearable device, generating a coupling vector of the motion intensity and the heart rate change rate in a short time window, and carrying out the long-time accumulation to form a coupling vector cluster, and judging the physiological regulation stability by evaluating the change of the morphological dispersion. Micro-motion is converted into a natural probe, through dynamic evolution of a motion-heart rate coupling relation, transition from observation of vital sign values to evaluation of autonomic nerve regulation ability is achieved, early decline of body functions can be captured before conventional indexes are abnormal, and meanwhile evaluation reliability in a complex environment is ensured through frequency domain feature filtering.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses wearable equipment with body temperature measurement and body temperature early warning functions. The wearable equipment comprises a signal sensing and collecting module, a metabolism dynamic estimation module, a thermal dynamic state deduction module, an autonomic nerve stress analysis module, a multi-mode risk assessment module, a graded early warning and intervention execution module and a closed-loop optimization and self-adaptive calibration module. Compared with the prior art, the method has the following advantages and effects that through fusion of multi-modal physiological, environmental and behavior data and based on dynamic metabolism estimation, thermal dynamics deduction and autonomic nerve stress analysis, accurate and foresight evaluation and personalized grading early warning of the heatstroke risk of the user are achieved, and the early warning accuracy and intervention effectiveness are remarkably improved.

The invention discloses a quantitative detection method for sweat gland cholinergic nerves of a neuron intracellular inclusion body disease. Belongs to the technical field of biological science. Performing immunohistochemical staining; scanning a slide; and calculating the density of the cholinergic nerve fiber. The invention relates to a simple and feasible method for quantitatively evaluating cholinergic nerve fibers of a neuron intracellular inclusion body disease. The method comprises the following steps: carrying out skin biopsy on control receptors of a neuron intranuclear inclusion body disease and a nervous system-free disease, slicing, carrying out immunofluorescent staining by using a VIP antibody, quantitatively calculating the density of cholinergic nerve fibers labeled by the sweat gland VIP antibody in dermis by using Image J software, analyzing whether the cholinergic nerve fibers have differences in the two groups of receptors, and if so, determining whether the cholinergic nerve fibers have differences in the two groups of receptors. Evaluating the nervous injury and severity of the cholinergic nerve; the invention provides a new way for accurately evaluating the autonomic nerve injury and severity of the neuronal intracellular inclusion body disease.

The invention provides a cardiac autonomic neurodynamics quantitative evaluation method based on a phase ordering signal averaging technology. The cardiac autonomic neurodynamics quantitative evaluation method comprises an ECG signal preprocessing step, a QRS wave detection and processing step, a phase ordering signal averaging step and an autonomic neurodynamics quantitative analysis step. According to the cardiac autonomic neurodynamics quantitative evaluation method based on the phase ordering signal averaging technology, on the basis of inhibiting aperiodic interference and improving index stability in a complex physiological environment, continuous analysis of cardiac sympathetic nerve activity and cardiac parasympathetic nerve activity is carried out; time-varying CSI and CPI indexes are extracted to realize quantification of cardiac autonomic neurodynamics, and a lightweight algorithm framework is designed to adapt to real-time monitoring requirements of portable equipment.

The invention discloses a psychological health screening method and system based on a large model, and relates to the technical field of psychological health, and the method comprises the following steps: scale data, micro expression video data and physiological signal data of a user are acquired, a multi-modal data acquisition module is constructed, and the physiological signal data at least comprises EEG (electroencephalogram) and PPG (pulse photoplethysmography); performing preprocessing and feature extraction on the scale data, the micro-expression video data and the physiological signal data; according to the psychological health screening method and system based on the large model, cross validation is carried out from three dimensions of subjective reports, unconscious behaviors and autonomic nerve responses by fusing multi-modal data such as scales, micro expressions, EEG and PPG; the problem that a traditional single scale method is easily influenced by subjective deliberate concealment, memory deviation and social sponsoring effect is fundamentally solved.

The invention discloses an acupuncture intervention method after a pancreatic cancer operation, and aims to solve the problem that autonomic nerve regulation based on heart rate variability in an acupuncture stimulation closed loop is difficult to objectize and safely optimize. Heart rate variability time-frequency and time-domain features and respiratory coherence are extracted based on synchronous compression wavelets, a reliability score is generated, short-time heart rate variability and uncertainty are predicted through a neural state space model, a hierarchical control barrier function is generated through a context gating network, a safe MPC is constructed, and real-time quadratic programming solution is carried out. The heart rate variability enters the target domain and the stimulation parameter change rate and the energy consumption are reduced under the condition that safety constraints such as current, pulse width, charge quantity and impedance are met by combining safety verification and self-adaptive updating, and the reliability and the robustness of closed-loop control are improved.

The invention provides a brain-computer interface-based TMS and VR collaborative rehabilitation training system, which is characterized in that a multi-mode collaborative positioning module is used for collecting VR interaction data and electroencephalogram data and determining the optimal stimulation state of a subject; the motion intention analysis module is used for matching a rehabilitation mode according to the collected data and in combination with the type of the subject; and the closed-loop rehabilitation interaction module is used for adjusting parameters of the TMS equipment according to the rehabilitation mode and stimulating the final stimulation area under the optimal stimulation state of the subject. For a mild stroke patient, the system dynamically monitors the plastic change of the autonomic nerve and adaptively adjusts the treatment parameters according to the plastic change of the autonomic nerve; for a severe stroke patient, the virtual reality situation is combined to effectively induce motor imagery and synchronously collect and analyze electroencephalogram signals, closed-loop nerve regulation is achieved, synchronous accurate intervention and curative effect optimization of the severe stroke patient are achieved, and an intelligent solution is provided for rehabilitation treatment of the nerve function of the severe stroke patient.

The invention discloses a rectal cancer postoperative anus function training rehabilitation device, and relates to the technical field of medical treatment, and the rectal cancer postoperative anus function training rehabilitation device is characterized in that an anorectum manometer detects the anorectum internal pressure of a rectal cancer patient to obtain a first detection result; the rectal ultrasonic probe monitors the surface myoelectricity or systolic pressure change of the sphincter ani to obtain a second detection result; the medical care terminal comprehensively evaluates the anus function based on the first detection result, the second detection result and a subjective scale, formulates a pelvic floor biofeedback combined acupuncture treatment scheme, and generates a corresponding acupuncture instrument control instruction and a biofeedback instrument control instruction; the acupuncture instrument stimulates a physiological regulation mechanism in a body by stimulating acupuncture points, and benign regulation is carried out on recovery of pelvic autonomic nerve functions; the biofeedback instrument feeds back the pelvic floor muscle signal to the rectal cancer patient so as to guide the rectal cancer patient to consciously control pelvic floor muscle movement. The anus function of the patient can be evaluated, protected and assisted in rehabilitation, and the postoperative life quality of the patient is improved.

The invention discloses a personalized arrhythmia radiofrequency ablation system based on intelligent data analysis, and the system comprises a pressure stimulation module which is configured to apply periodic mechanical pressure stimulation to a specific anatomical site of a heart through an ablation catheter, the contact force range of the pressure stimulation is 5-10 g, and the frequency is 1-3 Hz; the autonomic nerve state recognition module is used for collecting heart rate change data of the patient after pressure stimulation in real time, and when the absolute value of the heart rate change exceeds 15% of a base line, it is judged that the patient enters an autonomic nerve sensitive window phase; the dynamic ablation strategy generation module is used for dynamically adjusting a radio frequency energy release mode and an ablation target priority according to the autonomic nerve sensitive window phase type, the sympathetic dominant type or the parasympathetic dominant type; according to the invention, by taking'neural interference conversion-dynamic energy adaptation-intelligent marking verification 'as a core, all-round breakthrough is realized from technical principle, clinical effect to application cost, and an accurate, safe and popular innovative scheme is provided for arrhythmia treatment.

The invention discloses a near-infrared light controllable irradiator for astroganglion stimulation, which comprises a physiological signal acquisition module used for acquiring a near-infrared light sensing signal of a carotid artery area in real time; the signal processing module is used for performing variational mode decomposition on the near-infrared sensing signal and extracting a heart rate component; the autonomic nerve analysis module is used for receiving the heart rate component and calculating the excitement ratio of sympathetic nerves to parasympathetic nerves; a deep learning model is arranged in the control signal generation module, and an optical parameter control signal is generated according to the heart rate cycle characteristics and the excitement ratio; and the light adjusting module is used for receiving the light parameter control signal so as to control the emission of near-infrared light. According to the invention, the output of the infrared light can be adjusted according to the physiological signal of the human body, intelligent adjustment is realized, the near-infrared light stimulation better fits the individual condition, and the pertinence and effectiveness of the stimulation are effectively improved.

The present invention relates to a method for providing brain and mental health information, comprising the steps of: obtaining brainwave data and heartbeat data of a user; calculating, on the basis of the brainwave data, brain health indices indicating brain activity level, brain flexibility, brain brightness level, and brain balance level, and calculating, on the basis of the heartbeat data, mental health indices indicating autonomic nerve activity level, autonomic nerve balance level, stress index, and stress resistance level; determining a brain health type and a mental health type of the user respectively on the basis of the calculated brain health indices and mental health indices; and providing, according to the determined brain health type and mental health type, a service interface screen for providing a management and treatment guide for the user.

The invention discloses an intelligent diopter training system and method based on a multi-modal physiological feedback and reinforcement learning algorithm. The intelligent diopter training system comprises a multi-modal physiological data acquisition module, a central processing PC, a user terminal and a cloud data platform. Visual behavior data, ciliary muscle state data and autonomic nerve reaction data of a user are collected in real time through a multi-modal physiological data collection module, multi-modal physiological data fusion and feature extraction are carried out, and a refraction state evaluation model is used for calculating a comprehensive score of a refraction state grade, visual fatigue and adjustment sensitivity of the current user. A personalized training parameter set of the next training period is dynamically decided and generated based on a reinforcement learning algorithm, and the user terminal presents visual training stimulation according to the current personalized training parameter set. The cloud data platform stores the data and generates a visual training effect trend chart, a diopter change prediction report and personalized training suggestions to complete training scheme personalization, training means intellectualization and feedback evaluation comprehensiveness.

The invention provides a nerve regulation and control system and method for a spinal cord ventral epidural region, and the system employs a microprocessor to generate multiple paths of pulse width modulation signals, and drives a voltage-controlled current source after amplitude adjustment. Stimulation current with adjustable frequency, pulse width and amplitude is output to act on a spinal cord ventral nerve structure. Real-time monitoring of stimulation current of each channel is realized through combination of voltage sampling and a single-pole multi-throw analog switch, feedback information of a body position, myoelectricity, pressure and other multi-mode sensors is fused, a closed-loop control mechanism is constructed, and dynamic adjustment and individualized optimization of electrical stimulation parameters are realized. The system has the advantages of being more accurate in electrical stimulation positioning, more sensitive in response, more intelligent in control and the like, and is suitable for intelligent rehabilitation intervention in scenes of spinal cord injury, cerebral palsy, neurological dysfunction and the like.

To provide a composition that has the effect of improving unidentified complaint, depression, decreased concentration, decreased motivation to socialize and engage in daily activities, autonomic nervous system disorders, pain, control, and poor quality of sleep, associated with menstruation.SOLUTION: Provided is a composition for improving premenstrual depression, containing as an active ingredient an Apocynum venetum leaf extract containing 4 to 10 wt.% in total of the flavonoid compounds hyperoside and isoquercitrin, and in which the daily intake of the Apocynum venetum leaf extract is in the range of 40 mg to 100 mg or less (limited to healthy adult women who are undergoing treatment for premenstrual syndrome and premenstrual dysphoric disorder or have no history of such syndrome).SELECTED DRAWING: None

The invention relates to the technical field of medical instruments, in particular to a multi-mode biological feedback ear vagus nerve self-adaptive stimulation device and method.The device comprises a host, a stimulation electrode, a signal collection bracelet and an electroencephalogram collection module, and the host comprises core hardware configuration and a power management system; the signal acquisition bracelet comprises an ECG module, a PPG module, a GSR module, a six-axis acceleration sensor and the like. The ear vagus nerve stimulation parameter can be dynamically adjusted through real-time analysis of multi-mode data such as electrocardio, electroencephalogram, heart rate variability, respiration-heart rate synchronism and emotional state, personalized precise treatment is achieved, meanwhile, the wearing comfort and the durability of the device are improved, and the application range is wide. The traditional Chinese medicine composition can be used for auxiliary treatment of various diseases such as inflammatory bowel disease, epilepsy, chronic insomnia, depression, chronic pain, anxiety disorder, post-traumatic stress disorder and autonomic nerve dysfunction, and the functions of a nervous system are adjusted and the symptoms of a patient are improved by accurately stimulating the vagus nerve in the auricular concha area.

The invention relates to a method and system for predicting post-induced hypotension of a patient, and belongs to the technical field of hypotension prediction.The method comprises the steps that original data reflecting the cardiovascular autonomic nerve regulation capacity of the patient is obtained; preprocessing the original data to obtain target data, and calculating a target pressure reflex sensitivity BRS and a target pressure reflex effective index BEI based on the target data and the same-direction change sequence of the continuous heartbeat; inputting the target pressure reflex sensitivity BRS, the target pressure reflex effective index BEI and the patient baseline data into a Logistic regression model to obtain a hypotension risk probability; and determining a hypotension risk level of the patient based on the hypotension risk probability and a preset threshold range. According to the method, the target pressure reflex sensitivity BRS and the target pressure reflex effective index BEI serve as predictive factors, so that the probability of predicting hypotension of a patient can be improved before the patient receives anesthesia induction.

The invention discloses a cognitive function intervention method and system based on multi-mode space-time dynamic decoding biological feedback, and belongs to the technical field of artificial intelligence algorithms and noninvasive nerve regulation. According to the method, brain nerve activity signals and autonomic nerve cooperation signals are captured in real time, the signals are subjected to standardization processing by adopting a multi-modal signal cascade preprocessing algorithm, and cognitive feature extraction of fusion signals and dynamic distribution of multi-modal signal feature weights are realized in combination with a circulating double-flow self-encoding model; and thus, the individual cognitive state is accurately evaluated. And based on the evaluated cognitive state, dynamically identifying and quantifying into self-perceived visual and auditory stimuli, and constructing an iterative closed-loop feedback cognitive nerve plasticity regulation loop to realize individualized targeted regulation of the cognitive function. The method is suitable for non-invasive individualized intervention of the cognitive function of the Parkinson's disease patient, and has remarkable clinical application potential and social value.

The disclosed system relates to the diagnosis and correction of autonomic nervous system imbalance through the recognition and improvement of parasympathetic tone of a user using paced breathing and vibratory neuromodulation. The system includes a wearable IBI detector, software for analyzing PSD data and determining recommended therapy, a vibratory stimulator, and optional smart phone guidance. The IBI detector reads the pulsatile waveform to produce IBI data that is used to compute PSD data. The PSD data is analyzed for ANS imbalance indicators and therapy is recommended based on continuously updated information stored in a database. Results from application of recommended therapy are stored and shared and can be used to build a personalized training regime to strengthen parasympathetic tone.

The invention discloses a sympathetic cervical spondylosis autonomic nerve regulation enhancing method based on a brain-computer interface, and relates to the technical field of nerve regulation and repair. According to the method, by synchronously collecting electroencephalogram and skin conductance signals and conducting time-frequency domain conjoint analysis and Lempel-Ziv complexity evaluation, accurate quantitative evaluation on sympathetic nerve activity and nerve disorder degree is achieved, double-model collaborative decision-making of machine learning and deep reinforcement learning is utilized, personalized nerve regulation parameters are intelligently generated, and the method has the advantages of being high in accuracy and high in accuracy. High-targeting and safe intervention is implemented through double-target hierarchical regulation and control aiming at neck sympathetic nerves and vagus nerves, and stimulation parameters are dynamically optimized in a closed-loop manner by adopting a genetic algorithm on the basis of real-time feedback of heart rate variability, skin conductance and autonomic nerve balance indexes, so that the stimulation accuracy is improved. Therefore, the sympathetic cervical spondylosis autonomic nerve regulation efficiency and effect are enhanced.

Systems and methods for controlling autonomic nerve symptoms or side effects via spinal cord stimulation are discussed. An exemplary neuromodulation system includes an electrical stimulator to provide spinal cord stimulation (SCS) to a neural target, a user interface device to receive user input including autonomic nerve symptoms and affected anatomy, and a controller circuit to determine or adjust stimulation parameters based at least on the autonomic nerve symptoms and the affected anatomy. Spinal cord stimulation may be delivered according to the determined or adjusted stimulation parameters to alleviate autonomic symptoms or treat or alleviate autonomic disorders.

The invention provides a fatigue assessment method and device based on cardiac autonomic neurodynamics and brain coupling analysis, and the method comprises the steps: extracting time-varying CPI and CSI features representing cardiac autonomic neurodynamics features through a PRSA technology, carrying out the coupling analysis through different frequency bands of CPI, CSI and EEG, and carrying out the comprehensive, accurate and objective assessment of fatigue. On the basis of overcoming the defects of strong subjectivity and poor real-time performance of a subjective scale, through second-level dynamic quantization of sympathetic nerve and parasympathetic nerve activity states and brain activity coupling states, the limitation of traditional pure dependence on EEG or ECG spectral analysis and a static IPFM model can be effectively solved; the comprehensive and objective evaluation precision of the fatigue severity under the complex physiological state is improved, and the dynamic real-time monitoring requirement of the fatigue state is met.

The present invention discloses a method for reprogramming fibroblasts into autonomic ganglion organoids. The method uses a TetO-controlled lentiviral system to establish a method for reprogramming mouse somatic cells into autonomic ganglion organoids (induced autonomic ganglion, iAG) using a combination of transcription factors; the experimental results show that the combination of three transcription factors, Ascl1, Phox2a / 2b and Hand2, can induce mouse embryonic fibroblasts to generate self-organized, network-connected neural clusters. The method shows that the three factors Ascl1, Phox2a / 2b and Hand2 are effective means for reprogramming mouse fibroblasts into iAG organoids, and the induced iAG organoids may be used as a model to decipher the pathogenesis of autonomic neurological diseases and screen effective sources of drugs and cell replacement therapy.

The invention discloses a portable autonomic nerve function evaluation device, method, equipment and program product, and is specifically characterized in that the breathing of a user is individually adjusted to a specific frequency through an intelligent breathing monitoring and guiding program, and the heart rate fluctuation condition of the user in the process is recorded by a heart rate acquisition device; the heart and lung coupling strength of a user is quantitatively analyzed based on a feature extraction algorithm, and a mapping model between physiological features and clinical indexes is constructed to realize quantitative evaluation and portable monitoring of autonomic nerve functions. Based on accurate decoding of the cardiopulmonary coupling process, the portable autonomic nerve function evaluation device, method, equipment and program product which are rapid, low in manufacturing cost and easy and convenient to operate and have popularization significance are achieved, and the needed measuring device is simple and convenient and can be used for individualized home health evaluation.

The evaluation method of thermoregulatory function is characterized by comprising: a process of acquiring an autonomic nerve index of a subject; a process of acquiring a sweating index of the subject; and an evaluation process of evaluating a thermoregulatory function of the subject using the autonomic nerve index of the subject and the sweating index of the subject. Preferably, the autonomic nerve index comprises at least one of a heartbeat variation index and a pulse variation index, and the sweating index comprises at least one of a skin conductance and a sweating amount.