25 results about "Neurologic status" patented technology

The invention discloses a nerve regulation and control treatment method, system and device and a medium, and the method comprises the steps: designing a nerve regulation and control investigation and inquiry template, evaluating the nerve state of a patient, and generating an evaluation report; carrying out sound treatment technology applicability screening on the patient; selecting a plurality of sections of universal biological natural sound noise, providing the universal biological natural sound noise to the patient passing the applicability screening of the sound treatment technology for audition, and selecting the universal biological natural sound noise with a relatively high adaptability threshold as an initial nerve regulation treatment audio; carrying out hearing test, generating debugging parameters, debugging the initial treatment audio, and generating recommended nerve regulation treatment audio; and performing nerve regulation treatment on the patient based on the recommended nerve regulation treatment audio, and checking the treatment effect. According to the invention, a series of hearing tests are carried out on the patient, the debugged recommended nerve regulation treatment audio is generated for patients with different symptom types, and the nerve regulation treatment effect is effectively improved.

Sensing an evoked response to electrical stimulation of target tissue of a patient in conjunction with an intra-luminal electrode. The intra-luminal electrode may be implanted in a blood vessel or similar lumen proximal to the target tissue and the sensed signals and / or delivered stimulation may pass through the blood vessel. or other lumen, walls. In some examples the evoked response may be an evoked compound action potential (ECAP), which may also be evoked resonant neural activity (ERNA). The electrical stimulation may elicit a measurable response indicative of a thought pattern or neural state that would otherwise be undetectable using a non-evoked biopotential.

The invention belongs to the technical field of biomedical signal processing and nerve regulation and control, and discloses a closed-loop time interference nerve regulation and control system and method based on synchronous electroencephalogram signals. The closed-loop nerve regulation and control system integrates multi-channel electroencephalogram collection, neural state real-time recognition, brain network function connection analysis, traceability positioning calculation and individualized time interference stimulation strategies, an upper computer module can obtain electroencephalogram signals and analyze brain function states in real time, abnormal network structures are automatically recognized, and the brain function states are analyzed in real time. A double-target stimulation path is formulated by combining a nerve source positioning result, so that the defects of inaccurate positioning, incapability of responding to brain state change in real time and unpredictable stimulation effect of a traditional nerve stimulation method are overcome; the closed-loop mechanism of'perception-decision-execution-evaluation 'of the system not only enhances the effectiveness of stimulation, but also remarkably improves the safety, controllability and individualization level of the regulation and control process.

A computer implementation method for determining the neurological state of a subject by calculating a score indicating the subject's neurological state. The method includes the steps of (a) performing an encoding stage in which the subject is presented with a number of items to be recalled in a recall stage; (b) performing an intermediate task stage in which a metacognitive judgment score is obtained from the subject and / or a task to be performed is presented to the subject, resulting in an intermediate task score; (c) performing a recall stage in which the subject is asked to recall the number of items presented in the encoding stage, resulting in a recall stage score; and (d) calculating a score indicating the subject's neurological state based on the recall stage score and one or both of the metacognitive judgment score and the intermediate task score.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

The application relates to the technical field of neuromodulation devices, and provides a bone conduction neuromodulation device and a control method. The bone conduction neuromodulation device comprises an electroencephalogram acquisition device, a bone conduction vibration device and a control device. The control device is configured to extract electroencephalogram features in an electroencephalogram signal, determine vibration control parameters according to the electroencephalogram features, control the bone conduction vibration device to generate vibration according to the vibration control parameters, and adaptively adjust the vibration control parameters based on changes in the extracted electroencephalogram features during the process of generating vibration by the bone conduction vibration device, so as to form a closed-loop control. The closed-loop control mechanism based on real-time electroencephalogram feedback makes the vibration stimulation always adapt to the instant neural state of a target user, helps to reduce the influence of individual differences on the treatment effect, improves the stability and repeatability of the treatment effect, and further enhances the overall reliability of the bone conduction neuromodulation in clinical application.

This invention discloses a plant essential oil spray with anti-anxiety and antidepressant effects and its preparation process, available in daytime and nighttime versions. The plant essential oil mixture is prepared by coupled flash extraction with anhydrous ethanol and 1-octyl-3-methylimidazolium bromide, followed by microfiltration-nanofiltration and vacuum distillation. The excipients and essential oils are then mixed and bottled into spray bottles equipped with an automatic spraying device to obtain the daytime and nighttime plant essential oil sprays. The daytime version stimulates the autonomic nervous system, reduces mental fatigue, improves cognitive mood, and exerts an anti-anxiety effect during the day; the nighttime version regulates neurotransmitter levels, significantly calms and soothes the mind, and adapts to the differences in human mood and nervous state between day and night. This invention features high extraction efficiency, stable essential oil quality, and the product possesses both anti-anxiety and antidepressant effects. It is convenient to use, highly safe, and suitable for daily mood regulation, psychological stress relief, and intervention for anxiety and depression tendencies.

The invention relates to the field of neural function monitoring, in particular to a neural function nursing monitoring evaluation method and system. The method comprises the following steps: acquiring historical neural function data of a patient, constructing a neural day and night time sequence through the historical neural function data, and constructing a historical neural data set in combination with the historical neural function data; constructing a state node set and a state layer through the historical neural data set, and constructing a neural state model in combination with the state node set and the state layer; acquiring real-time nerve monitoring data of the patient, and analyzing the real-time nerve monitoring data in combination with the nerve state model to obtain a real-time nerve state trajectory and a change label; and analyzing the neural state model to obtain a reference neural state trajectory, and performing nursing evaluation on the patient in combination with the real-time neural state trajectory and the change tag to obtain a nursing change strategy. According to the invention, the accuracy and response efficiency of nerve nursing can be improved.

A system for a neurological assessment is provided. The system includes a memory storing instructions for the neurological assessment. The system includes a display, an audio output, a camera, a microphone, and a processor operably coupled thereto. The processor executes the instructions to cause the system to prompt a patient to perform actions, record video and audio of the actions as non-standardized patient specific information via the camera and the microphone, and generate relevant clipped portions from the video and the audio. The relevant clipped portions convert the non-standardized patient specific information into standardized patient specific information. The processor executes the instructions to cause the system to cause a determination of a neurologic status for the patient based on the standardized patient specific information by transmitting the relevant clipped portions to a second system for analysis and receiving an analysis result including the neurologic status from the second system.

The application provides an intraoperative neuroelectrophysiological monitoring method and system combined with anesthesia depth monitoring, first, anesthesia depth data of a patient during an operation is collected in real time through an anesthesia depth monitoring device, a neuroelectrophysiological monitoring parameter dynamic adaptation process is started based on the anesthesia depth data of the patient during the operation, the signal collection configuration of a neuroelectrophysiological monitoring device is adjusted, monitoring configuration parameters adapted to the current anesthesia state are generated, the device continuously collects neuroelectrophysiological signals of the patient during the operation according to the parameters, then a synchronous correlation analysis process of the signals and the anesthesia depth data is performed, time sequence correlation processing is performed to generate a correlation analysis result, finally, an intraoperative nerve state evaluation process is started based on the correlation analysis result, the corresponding relationship between waveform signal characteristics and anesthesia depth changes is analyzed, and an intraoperative nerve state evaluation report containing nerve function state indication information is generated, thereby improving the accuracy of intraoperative nerve function monitoring.

A neurological state of a subject is accurately evaluated, and an evaluation result suitable for the subject is provided. A pupil index calculation unit generates waveform data of a pupil area based on a moving image in which a pupil of the subject is captured with a distortion-corrected lens, and then calculates a pupil index. The neurological state estimation unit estimates a sympathetic neural age and a parasympathetic neural age, which are neurological states for each of the left and right pupils of the subject, based on the pupil index. A comment generation unit evaluates the neurological state of the subject from the estimated neurological state, and the improvement measure generation unit generates advice suitable for the subject based on the evaluation result. A display unit displays the evaluation result of the neurological state and the advice in a neurological state evaluation system.

One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

Systems and methods for predicting a neurologic condition in subjects less than one year old using a movement index derived from pose estimation are provided. Video data of a body of a subject is captured over time using cameras. The video data is processed using a pose estimation model to detect and track anatomical landmarks of the subject in successive video frames. A movement index is computed for a defined time interval based on a statistical measure of variance in the position of the tracked anatomical landmarks across the interval within the video data, where the movement index is normalized to account for body size and camera distance. The movement index is inputted into a trained machine learning model to obtain, as output from the model, a prediction of the neurologic condition in the subject representing a clinically relevant neurological state.

The invention relates to an intelligent mind and pupil monitoring method and device, and the method comprises the steps: collecting a brain heat radiation signal, a micro-motion feature and a pupil video sequence of a patient, and obtaining the cerebral cortex neural activity data of the patient; inputting the pupil video sequence into a deformed pupil positioning model, and extracting a pupil contour, a pupil diameter, a light reflex incubation period and a contraction speed curve; inputting the brain heat radiation signal, the micro-motion characteristic and the cerebral cortex neural activity data into a time-frequency fusion neural network model, and identifying the consciousness state of the patient; according to the pupil contour, the pupil diameter, the light reflex incubation period, the contraction speed curve and the consciousness state of the patient, a comprehensive neural state index report of the patient is generated. According to the method, sub-pixel-level pupil contour segmentation is achieved, the pupil diameter is accurately extracted, the method has higher adaptability to light reflection latency and contraction speed curves, pupil deformation, shielding and illumination changes, and finer changes of the consciousness state can be captured.

The invention discloses a self-adaptive closed-loop sleep nerve regulation and control system based on implicit preference learning. The self-adaptive closed-loop sleep nerve regulation and control system comprises a multi-modal signal acquisition module, a nerve state real-time monitoring module, an implicit preference driving decision module, a multi-modal stimulation execution module and a self-evolution feedback learning engine, and the multi-modal signal acquisition module is used for noninvasively acquiring physiological and environmental data of a user and sending the acquired data stream to the neural state real-time monitoring module and the self-evolution feedback learning engine. According to the invention, by constructing a full-closed-loop neural feedback architecture of perception-decision-regulation-learning, the crossing from passive monitoring to active optimization is realized, and the purpose is to solve the core problems in the prior art that the sleep intervention means are lack of individuation, the feedback mechanism is rigid, and the user experience is disjointed from the physiological benefit.

A system comprises a neuromonitoring system configured to generate nerve data regarding a state of a nerve of a patient during a surgical procedure on the patient. The system includes a robotic system configured to receive or generate, for the surgical procedure, location data that identifies a location of the nerve of the patient. The robotic system may cause the neuromonitoring system to be in either an active state or an inactive state based on the location data, where the active state is a state in which the neuromonitoring system provides the nerve data to the robotic system, while the inactive state is a state in which the neuromonitoring system does not provide the nerve data to the robotic system. The robotic system may further generate at least one control signal that implements one or more safeguards for the surgical procedure.

The invention discloses a transaural vagus nerve stimulation method based on electroencephalogram feedback, and relates to the technical field of nerve regulation and control. According to the method, electroencephalogram signals of a specific brain region of a user are collected in real time through a flexible integrated electrode, time-frequency characteristics (such as delta, theta and alpha wave power) and nonlinear characteristics (such as entropy) are extracted after preprocessing, the current sleep stage or fatigue degree of the user is identified by using a CNN + LSTM model, taVNS stimulation parameters (frequency and intensity) are dynamically generated in combination with individual baseline electroencephalogram characteristics, and the user can be subjected to sleep state and fatigue state simulation. Stimulation is applied through an auricle stimulation electrode, and meanwhile, neural state changes are monitored in real time to iteratively optimize parameters. The problems that an existing taVNS target spot is not accurate, the stimulation mode is unknown, and the individual difference is large are solved through electroencephalogram feedback, safe, portable and personalized sleep and fatigue intervention is achieved, and remarkable clinical and application values are achieved.

The invention relates to the crossing field of a nerve regulation and control technology, a brain-computer interface technology and a sonodynamic therapy technology, in particular to a closed-loop sonodynamic therapy system and method based on electroencephalogram feedback, which are used for carrying out sonodynamic therapy by activating a sound sensitive agent through transcranial ultrasound. The system comprises a unified time sequence module, a synchronous time sequence controller, an electroencephalogram acquisition module, a signal quality evaluation module, a neural state recognition module, a risk interlocking module, a dose control module, an ultrasonic driving module, a closed-loop verification module, a self-adaptive updating module and a log auditing module. According to the method, a single stimulation event is used as a minimum causal unit, hardware timestamp alignment closed loop of collection, recognition, decision making, emission, verification and updating is achieved, treatment parameters are dynamically adjusted according to electroencephalogram feedback, and the accuracy, safety and individualization level of sonodynamic treatment are remarkably improved.

The invention discloses a wearing device for monitoring and adjusting vegetative nerves and a working principle thereof, the wearing device comprises a multi-mode monitoring module, a multi-mode adjusting module, an intelligent control module and a power supply module which are integrated in a wearing main body, and all the modules are electrically connected through an FPC flexible circuit board to form a cooperative working unit; the multi-mode monitoring module at least integrates an electrocardio sensor and a galvanic skin reaction sensor and is used for synchronously collecting two or more vegetative nerve associated physiological signals and transmitting the signals to the intelligent control module. The intelligent control module is internally provided with a vegetative nerve state evaluation model and is used for carrying out real-time analysis processing on the physiological signals to obtain a quantified vegetative nerve function state evaluation result, dynamically generating adaptive adjustment parameters based on the evaluation result and sending the adaptive adjustment parameters to the multi-mode adjustment module; and the multi-modal regulation module at least integrates a transcutaneous electrical nerve stimulation unit and a temperature sensing regulation unit, and is used for outputting two or more regulation signals with a synergistic effect based on the regulation parameters and performing targeted intervention on the vegetative nerve function.

A method for providing electrical stimulation to a user as a user performs a set of tasks during a time window, the method comprising: providing an electrical stimulation treatment, characterized by a stimulation parameter and a set of portions, to a brain region of the user in association with the time window; for each task of the set of tasks: receiving a signal stream characterizing a neurological state of the user; from the signal stream, identifying a neurological signature characterizing the neurological state associated with the task; and modulating the electrical stimulation treatment provided to the brain region of the user based upon the neurological signature, wherein modulating comprises delivering a portion of the set of portions of the electrical stimulation treatment to the brain region of the user, while maintaining an aggregate amount of the stimulation parameter of the treatment provided during the time window below a maximum limit.

The invention discloses a radio frequency ablation needle dynamic adjustment method based on neural electrophysiological feedback, which relates to the technical field of signal mode identification, and comprises the following steps: based on a neuromorphic spectrum fingerprint and a signal quality mark, combining a stimulation-measurement-energy control parameter group, temperature and tissue electrical impedance data and recording a coding parameter combination; generating an execution list and a synchronization time plan; executing radio frequency energy output in the energy channel according to the execution list and the synchronization time plan, and generating a cost control period energy execution data packet; and based on the energy execution data packet in the control period, fusing the temperature and tissue electrical impedance data into the neuromorphic spectrum fingerprint, identifying the change trend of the neuromorphic characteristics, and generating a dynamic adjustment report of the radiofrequency ablation needle. According to the method, target region neural state modeling and multi-source data synchronization are achieved, accurate reference is provided for energy adjustment, and the effects of improving operation precision and reducing nerve injury are achieved.

This invention relates to the field of sleep aid technology, specifically to a multimodal physiological rhythm traction sleep intervention method and system. It dynamically determines a target guiding heart rate based on heart rate data, generating a physiological guidance cycle that includes inhalation and exhalation phases with durations adaptively adjusted according to heart rate changes, ensuring the guiding rhythm remains synchronized with the user's actual heart rate changes. Synchronous control signals are generated, acting on the visual, auditory, and tactile channels respectively. Through forced synchronous changes of the three sensory channels under the same rhythmic clock, a cross-sensory rhythm traction field is formed, actively inducing the user's heart rate to approach the target resting level. The physiological guidance cycle is continuously adjusted based on real-time updated heart rate data, forming a closed-loop control chain of monitoring, intervention, evaluation, and re-intervention until the user's autonomic nervous system switches to a sleep preparation state, thereby achieving active physiological rhythm traction under multimodal synergy.

The invention discloses an intelligent rehabilitation constraint glove suitable for a neurological severe user. The intelligent rehabilitation constraint glove comprises a glove body, a signal acquisition module, a signal processing module, a control module, a constraint execution module and a rehabilitation training module. The signal acquisition module synchronously acquires a hand motion signal and a vital sign signal of a user; the signal processing module performs homologous coupling analysis on the synchronously acquired signals to generate a risk score representing the central nervous driving state of the user; the control module generates a control instruction based on the risk score; the constraint execution module dynamically adjusts the constraint strength according to the control instruction; the rehabilitation training module adjusts a rehabilitation training mode according to the control instruction. Through homologous coupling analysis and intelligent decision making of multi-modal signals, integrated intelligent management of constraint and rehabilitation functions is achieved, prospective early warning and personalized intervention can be carried out according to the central nervous state of a user, the safety of the user is effectively guaranteed, and functional recovery is promoted.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.