9460results about "Artificial respiration" patented technology

The patent application describes an adaptable neuromodulation device for transcranial electrical brain stimulation. The system can generate complex poly-modulated waveforms by combining multiple pulse parameters in a randomized manner across dimensions like amplitude, frequency, phase, timing, and polarity. It leverages both digital and analog modulation techniques for waveform versatility. The device features pathway optimization algorithms that tune stimulation using neural feedback data to target specific brain region shapes with greater precision. It also enables closed-loop operation for responsiveness to physiological changes. The application details the mathematical and computational foundations, spanning graph theory to generate digital twins. It highlights applications from cognitive enhancement to seizure control. Overall, the innovation promises versatility and precision in non-invasive neuromodulation through adaptable, optimized waveforms grounded in sophisticated modeling.

Systems and methods herein generally relate to prolonging viability of bodily tissue, especially an organ, by adjusting pressure as needed to maintain a constant pressure within the organ even during external pressure fluctuations due, for example, to transportation of the organ in an airplane. The systems and methods herein can include an electronic pump that pumps gas into an organ and a mechanical pressure regulator to release gas based on organ pressure.

A system and method for maintaining an oxygen concentration of a biological sample. The oxygen concentration can be maintained by measuring the oxygen concentration within the biological sample and adjusting a rate of an oxygen supplier in response to this measurement. For example, when the oxygen concentration is below a threshold, oxygen can be delivered to the biological sample at a higher rate.

The invention discloses a brain-computer hybrid intelligent closed-loop swallowing rehabilitation system for treating dysphagia after stroke, and relates to the field of artificial intelligence medical instruments. The system is composed of a plurality of key modules: an intelligent swallowing brain-computer interface module for accurately identifying a swallowing intention through multi-modal physiological signal acquisition and processing; the intelligent swallowing functional electrical stimulation module is used for finely regulating and controlling stimulation parameters according to the condition of the patient; the AI auxiliary online integrated module is used for realizing non-invasive brain-controlled swallowing and high-precision AI regulation and control; the closed-loop intelligent feedback regulation and control module is used for real-time monitoring, early warning and self-adaptive optimization of electrical stimulation; the cloud platform remote monitoring and data storage module supports remote diagnosis and treatment of doctors; and the man-machine interaction interface display and printing module optimizes doctor-patient interaction. According to the system, a brain-computer interface and FES are combined, a personalized, safe and efficient rehabilitation scheme is provided for a patient based on data driving and an artificial intelligence algorithm, swallowing function recovery can be accelerated, complications are reduced, and the system has great clinical and social values.

Systems and methods herein generally relate to prolonging viability of bodily tissue, especially an organ, by adjusting pressure as needed to maintain a constant pressure within the organ even during external pressure fluctuations due, for example, to transportation of the organ in an airplane. The systems and methods herein can include an electronic pump that pumps gas into an organ and a mechanical pressure regulator to release gas based on organ pressure.

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. Personalized therapy based on algorithms and aggregated data are also provided.

The invention relates to the technical field of biomedical engineering and neuroscience, and discloses an in-ear vagus nerve stimulation control method, system and device based on scalp electroencephalography, and the method comprises the steps: collecting a scalp electroencephalogram signal, a body movement signal and a pulse signal of a to-be-detected user; performing preprocessing and frequency domain separation on the signal; calculating a sleep coupling score, and dividing a physiological state according to the sleep coupling score; dynamically adjusting the intensity and frequency of intra-ear vagus nerve stimulation of the user according to the physiological state, and generating feedback data; in-ear vagus nerve stimulation is performed and the dynamic adjustment parameters are updated based on the feedback data. According to the method, accurate sleep recognition is realized through multi-source signal parallel processing and SCS modeling, and the classification precision is remarkably improved; lSTM prediction and biphase modulation are combined to optimize stimulation parameters, sleep time is shortened, and side effects are reduced; a GPU acceleration and low-power-consumption framework is adopted, and efficient self-adaptive closed-loop regulation and control are achieved.

The invention relates to the technical field of neuromuscular electrical stimulation, and provides a multi-modal feedback self-adaptive adjustment neuromuscular regulation and control treatment system, which comprises wearable working equipment for outputting pulse electric signals to adjust nerves and muscles to form movement or adjustment feeling, electromyographic signals, motion data and user feeling input are collected to conduct feedback adjustment on output parameters of the output pulse electric signals; the handheld control equipment is used for being connected with the wearable working equipment and controlling the output working mode and the working state of the wearable working equipment; and the server-side equipment is used for managing user information and preference of the handheld control equipment, setting function authority of the handheld control equipment, acquiring data acquired by the wearable working equipment, and realizing self-adaptive output of output parameters of the pulse electric signals of the wearable working equipment based on a self-adaptive reinforcement learning algorithm. Adaptive optimization of millisecond-level dynamic electrical stimulation parameters is realized through a multi-modal feedback data fusion technology in combination with a layered reinforcement learning architecture.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

The present invention provides systems and methods for treatment planning and / or administering electrical pulses that elicit desired cell death mechanisms. Embodiments of the invention include selection of pulse parameters to tailor cell death type.

The invention discloses a sleep monitoring and intervention method and system based on electroencephalogram signals, and belongs to the technical field of sleep monitoring and intervention. The method comprises the steps that electroencephalogram signals are collected through a dry electrode worn on the head of a user; the signals are preprocessed; performing automatic sleep staging on the pre-processed signal based on a layered space-time joint modeling network, and outputting a waking period label, an N1 period label, an N2 period label, an N3 period label and an REM period label; whether intervention conditions are met or not is judged based on the staging labels and the real-time electroencephalogram spectrum features; and if yes, generating and applying an alternating current stimulation signal for intervention. The system comprises a wearable electroencephalogram acquisition device, a signal analysis module and an intervention module. According to the method, the deep learning model integrating front-end self-adaptive processing, the graph neural network and Transform is adopted for sleep staging, closed-loop phase synchronous stimulation driven by the digital phase-locked loop is combined on the basis, connection from sleep monitoring to closed-loop intervention is achieved, and the sleep monitoring accuracy and the intervention effect are effectively improved.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

The present invention relates to a skin care device using RF needles, and more specifically to a skin care device using RF needles, which applies RF energy to needles driven forward and backward, and inserts the needles into skin, thereby enabling a thermal effect to be applied to skin tissue. The skin care device, according to one embodiment of the present invention, comprises: a handpiece; a needle assembly attachable to / detachable from the front end of the handpiece, and comprising needles which may be driven forward and backward by means of a motor and are inserted into skin; an RF generation part for applying RF energy to the needles inserted into skin at the needle assembly; a skin contact part formed on the front end of the needle assembly; and a vacuum pressure formation part comprising a suction tube connected from one side of the needle assembly to one side of the hand piece, and forming vacuum pressure so as to pull the skin contacting the skin contact part.

The invention relates to the technical field of pelvic floor muscle repair, in particular to a pelvic floor muscle repair method and related equipment, and the method comprises the following steps: acquiring a surface electromyogram signal collected by a surface electrode; based on bispectrum analysis, effective feature extraction is carried out on the abdominal electromyographic signals and the pelvic floor electromyographic signals, the extracted features are input into a deep learning model, and the muscle activity state is analyzed; the muscle activity state is fed back to the patient, and the patient is prompted to adjust contraction and relaxation of pelvic floor muscles through voice; through contraction and relaxation of pelvic floor muscles, the pelvic floor function condition is evaluated based on a Glazer evaluation method; and according to the pelvic floor function condition, selecting a corresponding treatment mode for pelvic floor muscle repair. The pelvic floor muscle repairing effect can be improved.

A skin contact-sensitive high-frequency treatment apparatus of the present invention comprises: a cartridge including a main body that generates an output signal for high-frequency output, an electrode unit that is in contact with the skin and outputs high-frequency waves to the contacted skin through the output signal, and a detection unit that detects the occurrence of displacement in an area of the electrode unit according to pressure applied to the electrode unit; and a handpiece to which the cartridge is coupled and which electrically connects the electrode unit and the detection unit to the main body, wherein, only when a detection result of the detection unit confirms that the electrode unit is in close contact with the skin, the main body provides the output signal for high-frequency output to the electrode unit through the handpiece.

A neuromodulation system including at least one input module for inputting a planned neuromodulation event or a series of neuromodulation events and at least one analyzing module for analyzing a neuromodulation event or a series of neuromodulation events.The analyzing module and the input module may be connected such that the input module is configured to forward the planned neuromodulation event or a series of neuromodulation events to the analyzing module and the analyzing module is configured to analyze the planned neuromodulation event or a series of neuromodulation events regarding one or more possible neuromodulation conflict(s).

The invention belongs to the technical field of nerve regulation and control, and discloses a portable noninvasive brain deep electrical stimulation system based on time interference, and the system comprises an electroencephalogram signal collection unit which is used for obtaining an electroencephalogram signal in a high-quality resting state, and carrying out the signal preprocessing and feature extraction, thereby obtaining an optimal stimulation envelope frequency; the personalized finite element modeling unit is used for constructing a head personalized model and outputting optimal electrode configuration and stimulation parameters for a target brain region target spot; the electrode positioning and stimulation execution unit is used for realizing accurate positioning of an electrode position and implementing time interference electrical stimulation; the MCU unit is used for realizing data management, task scheduling and remote communication; the human-computer interaction unit is used for realizing visual treatment flow and parameter setting; and the power supply management unit provides stable working voltage for the system. According to the invention, the portable, intelligent and personalized modeling of the electrical stimulation treatment equipment is realized, and the precision and efficiency of nerve regulation and control are remarkably improved.

The invention belongs to the technical field of medical electronic equipment, and discloses a control system for alternative electrical stimulation of idiopathic tremor, and the system comprises a signal collection module which is used for obtaining tremor motion data of a patient in real time; the signal processing module is used for analyzing tremor frequency, amplitude and phase characteristics; the dynamic parameter generation module is used for generating an alternate stimulation mode according to the tremor characteristics; and the electrode array module comprises at least two stimulating electrodes and a reference electrode. The invention provides a non-invasive and dynamically optimized alternate electrical stimulation control system which is used for inhibiting hand tremor of an idiopathic tremor patient, regulation and control of a tremor network can be enhanced by alternately stimulating different peripheral nerves, the curative effect can be improved through closed-loop feedback optimization parameters, long-acting and safe tremor inhibition is achieved, and the treatment effect is improved. The problems of insufficient curative effect, nerve adaptability, surgical risk and the like in the prior art are solved.

The invention relates to the technical field of medical rehabilitation training, in particular to an intelligent hand training method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a training mode instruction selected by a user; real-time biomechanical data of the affected side hand are collected through a multi-source sensor array, the real-time biomechanical data comprise finger joint angles, electromyographic signal intensity and a touch pressure distribution matrix, and when the affected side hand is in a mirror image training mode, the six-degree-of-freedom motion trail of the uninjured side hand is synchronously collected; calling a corresponding algorithm model for processing based on the training mode, and generating a control instruction set containing pneumatic pressure parameters, electrical stimulation waveforms and virtual interaction instructions; the control instruction set is sent to a server side, a mechanical glove arranged on the hand of the affected side in a sleeving mode executes corresponding pneumatic-electric combined driving operation, and multi-sense feedback is synchronously presented. According to the application, the composite requirements of patients in different rehabilitation stages on training mode diversity, control accuracy and interactive immersion can be met.

The invention discloses an intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus, and relates to the technical field of electrical stimulation rehabilitation therapeutic apparatuses, the intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus comprises an electroencephalogram signal acquisition module, an electromyographic signal acquisition module and a mixed signal processing module, the mixed signal processing module is connected with the electroencephalogram signal acquisition module and the electromyographic signal acquisition module; the method is used for preprocessing electroencephalogram and electromyogram signals, fusing multi-modal features and classifying motion intentions. By installing the mixed signal processing module, preprocessing, multi-modal feature fusion and motion intention classification of electroencephalogram and electromyogram signals are achieved, a preprocessing unit can effectively remove ocular artifacts and power frequency noise, meanwhile, independent component analysis and a dynamic baseline calibration strategy are adopted, the signal quality is further improved, and the accuracy of motion intention classification is improved. According to the multi-mode signal processing mode, the limitation of single signal processing in the prior art is overcome, and the accuracy and effectiveness of rehabilitation treatment are improved.

The invention relates to the technical field of medical instruments, in particular to a high-precision time interference non-invasive brain deep electrical stimulation method, equipment and system, and solves the problems that two stimulation electrodes are used for each pair of current in traditional time interference-based non-invasive brain deep electrical stimulation, so that the current distribution is relatively dispersed, and the stimulation focusing performance is relatively poor. According to the method, high-precision 4 * 1 electrode configuration is used, two electrodes for applying a stimulation current are changed into a 4 * 1 electrode form, the flowing path of the current is effectively limited, the current is more focused, and the focusing performance of the interference current is further improved. According to the device, high-precision current control is realized through the anti-phase output circuit, the 4 * 1 voltage division circuit, the constant current source module and the impedance detection module; the system supports forward and reverse electrode position optimization, a personalized real finite element head model simulation system based on MRI data is used for guiding practical application, the focusing performance of each pair of current in a target brain region is improved, and therefore the overall stimulation precision in the target brain region is improved.

The invention discloses a closed-loop noninvasive spinal cord electrical stimulation regulation and control method and system based on motion data, and the system is provided with a flexible electrode array patch which is attached to the skin surface of a to-be-tested human body; the sensing module is used for collecting electromyographic signals or acceleration signals of a user; the signals are processed, and characteristic values related to actions and motions are extracted; the closed-loop feedback module compares the characteristic value with a model set characteristic / threshold value, and judges whether to start stimulation site and stimulation parameter adjustment or not; automatically selecting an optimal electrode unit and a corresponding stimulation parameter based on a judgment result; the dynamic regulation and control module controls the optimal electrode unit to output stimulation current, and regulates the activity of corresponding spinal cord neurons; and continuously performing closed-loop adjustment on the electrode unit and the stimulation parameters according to the motion feedback data. According to the method and system, precise stimulation and closed-loop regulation and control based on the motion response of the patient are achieved, and the problems that stimulation target positioning depends on artificial experience, parameter adjustment is not timely, and individualized and closed-loop feedback capacity is lacked are solved.

A medical apparatus for a patient comprises an external system configured to transmit one or more transmission signals, each transmission signal comprising at least power or data; and an implantable system configured to receive the one or more transmission signals from the external system. The apparatus can be configured to treat a patient and / or record patient data. Methods of treating a patient and recording patient data are also provided.

The invention discloses a medical integrated module type electroacupuncture anesthesia therapeutic apparatus, and belongs to the technical field of medical apparatuses and instruments. A medical integrated module type electroacupuncture anesthesia therapeutic apparatus comprises a signal collecting and monitoring module, a main control module, a parameter self-adaptive adjusting module, a stimulation output module and a safety monitoring unit. The problems that existing current output parameters depend on manual adjustment and doctor experience, an objective quantitative basis is lacked, a real-time physiological monitoring and feedback mechanism is lacked in the treatment process, and current adjustment lags behind the state change of a patient are solved. Through a multi-parameter fusion decision algorithm and a closed-loop real-time feedback mechanism, 'experience-driven open-loop control 'of traditional electro-acupuncture equipment is upgraded to'data-driven closed-loop control', the subjectivity and hysteresis problems of manual adjustment are solved, and treatment safety and individual adaptability are greatly improved through high-precision monitoring and quick response.

The invention provides a transcranial stimulation control method, a transcranial stimulation control system and a computer readable storage medium, and the method comprises the steps: collecting an electroencephalogram parameter signal, and converting the electroencephalogram parameter signal into an electroencephalogram feature vector; inputting the electroencephalogram feature vector into a large language model, obtaining an initial electrical stimulation signal corresponding to the electroencephalogram feature vector from a stimulation electroencephalogram knowledge model according to the electroencephalogram feature vector, and generating an initial electrical stimulation signal scheme; the initial electrical stimulation signal is optimized through an attention mechanism according to historical response data of a user, a final electrical stimulation signal scheme is output, and the historical response data of the user comprises a historical electrical stimulation signal scheme and user feedback information; and outputting the final electrical stimulation signal scheme to a transcranial stimulation module. The method can improve the insomnia treatment efficiency of the user.