56 results about "Local field potential" patented technology

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention relates to the technical field of spinal cord injury repair, and discloses a multi-mode nerve regulation electric acupuncture control system for spinal cord injury repair, a patient evaluation module is used for establishing a personalized spinal cord injury model of a patient and evaluating the current nerve function state and rehabilitation potential of the patient to obtain a nerve function evaluation result; the parameter setting module is used for setting multi-modal stimulation parameters in electroacupuncture treatment; the signal processing module is used for collecting local field potential of a spinal cord injury area and electromyographic signals around the injury in real time, preprocessing the collected signals and extracting time-frequency characteristics of the signals. The evaluation module is used for inputting the time-frequency characteristics of the signals into an electro-acupuncture evaluation model, evaluating the neural pathway in real time through the electro-acupuncture evaluation model and outputting an evaluation result; the adjusting module is used for adjusting the frequency, waveform and current intensity parameters of the electroacupuncture needle based on the evaluation result and a Q-learning algorithm, searching an optimal electroacupuncture needle parameter combination and feeding back the optimal electroacupuncture needle parameter combination to the electroacupuncture needle; according to the invention, the pertinence and effectiveness of treatment are improved.

The invention discloses a local field potential signal generation method and system based on an improved diffusion model. The method comprises the following steps: acquiring and preprocessing an original noise-containing signal, timestamp information, a multi-channel behavior signal or a stimulation signal; performing multi-head self-attention calculation on the preprocessed data based on a Transform encoder to obtain a potential state of the LFP sequence in a potential space; carrying out potential variable sampling according to the potential state, carrying out continuous ODE solution and discrete GRU rule updating on the obtained initial potential variable, and generating a potential state sequence evolved along with time; performing forward noise addition and reverse noise reduction sampling on the potential state sequence based on a diffusion model to obtain a final potential sequence; and mapping a potential sequence finally obtained by the diffusion model back to an LFP signal of a preset dimension, and outputting LFP time sequence data generated by simulation. According to the invention, high-fidelity, controllable-condition and stable-structure neural signal generation is realized.

The invention relates to the technical field of nerve regulation and control, and discloses a nerve regulation and control method based on dynamic frequency band analysis, which is used for improving the accuracy of epileptic seizure judgment. The neural regulation and control method based on dynamic frequency band analysis comprises the following steps: acquiring a local field potential signal of a target brain region in a preset time period; performing dynamic frequency band analysis on the local field potential signal to obtain a pathological wave frequency band, a high-frequency oscillation band, a harmonic coupling band and a suppression wave band; calculating the energy ratio and the cross-band phase coupling strength of each feature sub-band, and generating a multi-dimensional feature vector; the epileptic seizure probability is calculated according to the multi-dimensional feature vector; and when the epileptic seizure probability is greater than a preset threshold value, selecting a corresponding stimulation parameter group according to the dominant characteristic sub-band type, and outputting an electrical stimulation pulse with a frequency band matching characteristic.

The invention discloses a multifunctional neural electrode for intracranial pressure detection and closed-loop nerve regulation, the multifunctional electrode sequentially comprises a PMUT electrode for intracranial pressure detection and nerve regulation and a macro-micro electrode for electroencephalogram signal detection from top to bottom, and the PMUT electrode is bonded on the macro-micro electrode. The PMUT electrode is bonded to the macro-micro electrode to serve as a multifunctional electrode, the PMUT electrode can serve as a pressure sensor and can also serve as an ultrasonic transducer, the dual functions of intracranial pressure detection and fixed-point ultrasonic nerve stimulation are achieved, the macro-micro electrode can achieve detection of local field potential and peak potential, and the ultrasonic nerve stimulation function is achieved. Therefore, a detection-feedback-regulation closed-loop system is realized.

The present invention discloses a closed-loop neural regulation system based on the medication and movement state of Parkinson's patients, including: a parameter setting module for determining the low beta frequency band, the high beta frequency band, the upper threshold and the lower threshold; a signal acquisition module for collecting and preprocessing the local field potential signal of the STN of Parkinson's patients; a feature calculation module for calculating the low beta frequency band energy and the high beta frequency band energy through short-time Fourier transform, and calculating the average value of the ratio of the two; a judgment output module for comparing the average value with the upper threshold and the lower threshold; if it is greater than the upper threshold, it is judged that the patient is in the drug failure-movement state and outputs high-intensity stimulation; if it is greater than the lower threshold and less than the upper threshold, it is judged that the patient is in the drug failure-resting state and outputs medium-intensity stimulation; if it is less than the lower threshold, it is judged that the patient is in the drug effective state and outputs low-intensity stimulation. The present invention can achieve precise closed-loop deep brain stimulation based on medication and movement state.

This document discusses a neurostimulation device to monitor electrical neural activity when connected to implantable electrodes. The neurostimulation device includes a sensing circuit configured to sense sensing a local field potential (LFP) signal of a patient when connected to implantable electrodes and signal processing circuitry operatively coupled to the sensing circuit. The signal processing circuitry is configured to compute a power spectral density (PSD) of the sensed LFP signal, compute a slope of the PSD of the sensed LFP signal, and determine a physiological state of the patient using the computed slope of the PSD of the sensed LFP signal.

The invention discloses a pathological autophagy detection method based on a myocardial cell sensing system. The pathological autophagy detection method comprises the following steps: constructing a pathological autophagy detection platform based on the myocardial cell sensing system, performing surface modification on a microelectrode array chip, culturing a cell autophagy sensitive element and performing in-vitro pathological autophagy detection. Through primary myocardial cell culture, rapamycin action and electrophysiological signal acquisition, time domain and frequency domain characteristics of myocardial cell extracellular local field potential signals are recorded and analyzed in real time, and the autophagy degree induced by rapamycin is judged. According to the method, the signal-to-noise ratio is improved through platinum black nanoparticle modification, it is ensured that the myocardial cell growth activity meets the detection requirement, the half inhibitory concentration of rapamycin is obtained through nonlinear regression analysis, and an effective means is provided for pathological autophagy research. The autophagy state can be obtained in real time without damaging the cell structure; any antigen or fluorescent molecular marker is not needed, so that cytotoxicity is avoided, and long-time monitoring is realized.

The invention relates to a three-dimensional electric signal acquisition and stimulation neural electrode device, which comprises a tissue culture component, a plane patch type electrode assembly, an invasive electrode assembly and an artificial intelligence (AI) control system, and is characterized in that the tissue culture component is used for culturing nervous tissues or organoids and supporting electrodes to measure and stimulate electric signals; the plane patch type electrode assembly carries out local field potential collection and surface electrical stimulation, and the intrusive electrode assembly is inserted into nervous tissue or an organ-like body to carry out collection and stimulation of deep three-dimensional action electric signals. And the artificial intelligence (AI) control system is used for receiving pose information of the plane patch type electrode assembly and the intrusive type electrode assembly and the collected electric signals in real time, analyzing and judging the pose information and the collected electric signals, and then feeding back and regulating the position postures of the plane patch type electrode assembly and the intrusive type electrode assembly. The device provided by the invention realizes long-term comprehensive acquisition and recording of tissue electric signals in a three-dimensional space through the composite neural electrode device and artificial intelligence feedback control of the poses of the electrodes.

The embodiment of the application provides a kind of based on difference filtering neural signal action potential detection adaptive compression method and related equipment, belong to biological signal detection technical field.The method includes obtaining high flux neural signal, wherein, high flux neural signal includes at least one potential signal, potential signal includes action potential and local field potential;Based on the difference operation detection of high flux neural signal to high frequency narrow pulse mutation characteristics, obtain the action position information of action potential in high flux neural signal;According to action position information, high flux neural signal is carried out event-driven adaptive variable rate compression, and compressed data is obtained.The embodiment of the application can realize the accurate detection of action potential, while greatly reducing resource occupation.

The invention relates to a control parameter determination method and device of deep brain stimulation equipment, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring multi-source physiological data of a target user by using an in-vitro multi-mode sensor array, and acquiring multi-channel local field potential sensing data of the target user by using deep brain stimulation equipment; determining fusion feature information of the target user based on the multi-source physiological data and the multi-channel local field potential sensing data, and determining state information of the target user based on the fusion feature information; and determining control parameters of the deep brain stimulation equipment according to the state information, wherein the control parameters are used for controlling the deep brain stimulation equipment to work. By adopting the method, the working efficiency of deep brain stimulation equipment can be improved.

An electrical non-destructive real-time cell monitoring device according to an exemplary embodiment of the present invention comprises: a first electrode in contact with a cell; a second electrode provided at a position spaced apart from the first electrode; an impedance measurement unit connected to the first electrode and the second electrode; an LFP measurement unit connected to the first electrode and the second electrode; and a collected signal processing unit connected to the impedance measurement unit and the LFP measurement unit so as to receive and process measurement results, wherein the impedance measurement unit applies an alternating current to the second electrode so as to measure the impedance between the first electrode and the second electrode, the LFP measurement unit measures a local field potential between the first electrode and the second electrode and outputs a first digital signal, the impedance measurement unit and the LFP measurement unit operate simultaneously, and the collected signal processing unit processes a first digital signal so as to obtain a second digital signal.

The invention discloses a differential low-noise integrated biosensor array for cell culture detection and a preparation method thereof, and the differential low-noise integrated biosensor array integrates an ISHFET device, an REHFET device and an EXHFET device, so that extracellular action potential signals and local field potential signals can be detected during in-vitro cell culture; therefore, interference of factors such as tissue trauma and immune response on cell activity signal detection is avoided, and good reliability is realized; the ISHFET device and the REHFET device can form a differential sensing unit, so that the effect of reducing the detection error is realized; the EXHFET device not only can detect extracellular action potential signals, but also can independently detect local field potential signals, so that more diversified cell activity signals can be detected, and more data support is provided for in-vitro cell culture research. The method is widely applied to the technical field of semiconductors.

The embodiment of the invention discloses an electroencephalogram signal display method, device and equipment and a medical system.The method comprises the steps that in response to a first triggering operation, local field potential power spectrum data corresponding to at least one stimulation electrode channel implanted into the brain of a target object is obtained; in response to a second trigger operation for the target interface, performing peak detection on the local field potential power spectrum data corresponding to the at least one stimulation electrode channel, and determining peak detection results corresponding to the local field potential power spectrum data; the peak detection results are displayed in the target interface, automatic peak detection can be performed on the multi-channel local field potential signals acquired by the implantable stimulating electrode through simple interactive operation, and the judgment precision, efficiency, objectivity and real-time performance of the local peak results in the local field potential signals are improved. And a visual display picture is provided for assisting the user to select the stimulation target and adjust the stimulation parameters.

A method and apparatus are provided for determining stimulation parameters for DBS. The method includes receiving first data indicating a value of a local field potential (LFP) over a first time period from first contacts positioned adjacent a brain region. The method further includes receiving third data indicating a value of a LFP over second time periods from the first contacts, where the brain region is stimulated by second contacts over second time periods based on stimulation parameter values. The method further includes determining a frequency band encompassing a difference between a first frequency spectrum of the first data and a second frequency spectrum of the third data. The method further includes determining a value of the second frequency spectrum over the frequency band for each stimulation parameter value. The method further includes determining a value of an optimal stimulation parameter based on the value of the second frequency spectrum and each stimulation parameter value.

The invention discloses a Parkinson's disease dyskinesia recognition system based on cross-frequency coupling tensor decomposition, and the system comprises a signal collection and processing module which is used for synchronously collecting and preprocessing local field potential signals of multiple sites of a brain; the cross-frequency coupling calculation module is used for calculating a cross-frequency coupling map of each data slot; the signal-to-noise ratio balance processing module is used for generating an average cross-frequency coupling map of each tested individual; the tensor decomposition feature extraction module is used for aggregating the average cross-frequency coupling maps of all the tested individuals, constructing a third-order tensor and decomposing the third-order tensor to obtain three tensor components; moreover, when the average cross-frequency coupling atlas of the tested individual to be tested is input, weight vectors corresponding to the three tensor components are output; and the classification and identification module is used for inputting the weight vector of the tested individual to be tested into a pre-trained machine learning classification model and outputting an identification result of the dyskinesia of the Parkinson's disease. According to the method, the problem of frequency drift existing among different individuals can be solved, and the recognition accuracy is improved.

A system for automatic calibration of local field potential (LFP) sensing for deep brain stimulation may include one or more electrodes, and a control circuit. The control circuit is configured to sense an LFP of the brain using the one or more electrodes prior to delivery of electrical stimulation via the one or more electrodes, record a snapshot of LFP activity within a broad band range, determine a peak frequency of LFP activity from the snapshot, and update a frequency of interest (FOI) based on the peak frequency.

The present application relates to the technical field of data processing, and especially relates to a brain-computer interface data preprocessing method and device. The present application provides a brain-computer interface data preprocessing method, which comprises separating action potential signals from local field potential signals in original signals, performing variable rate compression on the local field potential signals in the local field potential channel, dividing the local field potential channel into a key channel and a compression channel, respectively compressing the data of the key channel and the compression channel to different degrees, and recombining data streams to reconstruct the compressed data. The present application utilizes the correlation between channels to jointly compress multi-channel data, condenses common information to more completely preserve at a higher rate, and combines the original information of each channel at a low rate and high compression ratio to maximize multi-channel data compression. Meanwhile, the Kalman method is used to combine high-frequency low-precision data and low-frequency high-precision data to realize the ability of high-frequency high-precision reconstruction.

The invention provides an implantable closed-loop nerve stimulation system and a stimulation parameter regulation and control method thereof, and the method comprises the steps: carrying out the impedance measurement through applying a dual-frequency test current to a plurality of electrodes, and obtaining a first composite impedance signal; obtaining a three-dimensional space impedance gradient based on the space coordinate of each electrode in the plurality of electrodes and the corresponding impedance value; according to the three-dimensional space impedance gradient and a first impedance change rate determined based on the first composite impedance signal, regulating and controlling a stimulation parameter of the electrical stimulation pulse; impedance measurement is carried out in a first time period after the electrical stimulation pulse is applied, a second composite impedance signal is obtained, and a first local field potential signal of an area where the multiple electrodes are located is obtained in the first time period; and when the second composite impedance signal satisfies a first condition, and / or the first local field potential signal satisfies a second condition, calibrating the regulated stimulation parameter.

Disclosed are a system for neural stimulation, a device for neural stimulation, and a computer-readable storage medium. The system includes a neural stimulator, the neural stimulator includes a stimulation module, configured to: determine, based on a received local field potential signal, a first energy value of a frequency band of interest in the local field potential signal; determine a threshold range in which the first energy value is; and output, according to a stimulation protocol corresponding to the threshold range, a corresponding stimulation signal. According to technical solution of the embodiments of this disclosure, a closed-loop neural stimulation mode can be achieved.

The embodiment of the invention provides a self-adaptive compression method based on difference filtering neural signal action potential detection and related equipment, and belongs to the technical field of biological signal detection. The method comprises the steps that high-throughput neural signals are obtained, the high-throughput neural signals comprise at least one potential signal, and the potential signals comprise action potentials and local field potentials; performing difference operation detection on the high-throughput neural signal based on high-frequency narrow pulse mutation characteristics to obtain action position information of the action potential in the high-throughput neural signal; and performing event-driven adaptive variable rate compression on the high-throughput neural signal according to the action position information to obtain compressed data. According to the embodiment of the invention, the action potential can be accurately detected, and meanwhile, resource occupation is greatly reduced.

Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinson's Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include a novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

The invention provides a real-time removal method, system and device for electrical stimulation artifacts and a storage medium, and relates to the technical field of signal processing, and the method comprises the steps: collecting a local field potential signal of a deep brain region, carrying out the baseline removal of the local field potential signal through a high-pass filter, and obtaining a preprocessed potential signal; the rising edge of the preprocessed potential signal is monitored on line, an amplitude threshold value is set, and a low-amplitude trailing artifact segment and a high-amplitude stimulation artifact segment in the preprocessed potential signal are jointly judged; and performing artifact removal on the low-amplitude trailing artifact segment by adopting a template subtraction method to obtain a first signal, and processing a high-amplitude stimulation artifact segment in the first signal by adopting an interpolation method aiming at the high-amplitude stimulation artifact segment to obtain a restored reconstruction signal. According to the method, the electrical stimulation artifacts can be removed efficiently and accurately in real time, so that the quality and the accuracy of signals are improved.

A method for photobiomodulation includes emitting light from a lead implanted in a patient using a light source according to a first delivery program to photobiomodulate the patient; monitoring at least one biomarker, an evoked signal, or a local field potential to monitor at least one effect of the photobiomodulation; determining, by a processor, whether the monitoring of the at least one biomarker meets a change condition; when the monitoring of the at least one biomarker meets a change condition, determining, by the processor, a change to the first delivery program to produce a second delivery program; and emitting light from the implanted lead according to the second delivery program. Other aspects include coordinated photobiomodulation and electrical stimulation.

Provided herein is a mesoscopic electrophysiological system including a structural system sized and configured to be attached to a head of a patient and to hold a plurality of intracranial electrode shafts in predetermined positions around the head of the patient, a plurality of mesoscopic intracranial electrode shafts that includes a first plurality of electrode contacts configured to measure local field potentials (LFPs) at a plurality of locations in a brain of a patient, wherein each electrode shaft of the plurality of intracranial electrode shafts includes a second plurality of electrode contacts, and a plurality of guide pins sized and configured to receive the plurality of mesoscopic intracranial electrode shafts positioned therein.

The application discloses a Parkinson's disease dyskinesia identification system based on cross-frequency coupling tensor decomposition, comprising: a signal acquisition and processing module, which is used for synchronously collecting local field potential signals of multiple sites of the brain and performing pretreatment; a cross-frequency coupling calculation module, which is used for calculating a cross-frequency coupling atlas of each data segment; a signal-to-noise ratio balance processing module, which is used for generating an average cross-frequency coupling atlas of each subject; a tensor decomposition feature extraction module, which is used for aggregating the average cross-frequency coupling atlas of all subjects, constructing a third-order tensor, and decomposing to obtain three tensor components; and when the average cross-frequency coupling atlas of a to-be-tested subject is input, a weight vector corresponding to the three tensor components is output; and a classification and identification module, which is used for inputting the weight vector of the to-be-tested subject into a pre-trained machine learning classification model, and outputting an identification result of Parkinson's disease dyskinesia. The application can overcome the frequency drift problem existing between different individuals and improve the identification accuracy.

This invention discloses an odor decoding method based on olfactory electroencephalogram (EEG) signals, comprising the following steps: inputting a training set into a random forest model for training to obtain a trained random forest model; inputting the sample to be tested into the trained random forest model and outputting the classification result; each sample is a dimension-reduced feature matrix corresponding to a target characteristic gas. The method for obtaining the dimension-reduced feature matrix includes: depriving a rat model of water for 12-15 hours; stimulating the rat model with a gas containing a target characteristic gas; and synchronously acquiring the local field potential signal of the rat model based on an array electrode within 5 seconds after stimulation; and obtaining the dimension-reduced feature matrix based on the local field potential signal. The odor decoding method based on olfactory EEG signals of this invention has the advantages of high specificity, high sensitivity, strong real-time performance, and portability, with a response time ≤1s and a decoding accuracy of up to 94%.

Physiological signals, such as local field potential signals, are sensed by an implantable medical device and displayed for a clinician to view during the provision of aspects of stimulation therapy. In one example, a local field potential signal that has been sensed during an automatic capture of a threshold to be used for stimulation therapy, such as deep brain stimulation, may be displayed, where the automatically captured threshold is displayed with the physiological signal. In another example, a local field potential signal is displayed with a stimulation signal such that the relationship between the local field potential signal and the stimulation signal is visible, and this display of the signals may be done in real time while the implantable medical device is capturing the signals and transmitting them to the external device.

A method for photobiomodulation includes emitting light from a lead implanted in a patient using a light source according to a first delivery program to photobiomodulate the patient; monitoring at least one biomarker, an evoked signal, or a local field potential to monitor at least one effect of the photobiomodulation; determining, by a processor, whether the monitoring of the at least one biomarker meets a change condition; when the monitoring of the at least one biomarker meets a change condition, determining, by the processor, a change to the first delivery program to produce a second delivery program; and emitting light from the implanted lead according to the second delivery program. Other aspects include coordinated photobiomodulation and electrical stimulation.

The present invention relates to a cell chip for measuring local field potential (LFP), the chip comprising: an electrode array in which a reference electrode and a plurality of working electrodes are spaced apart from each other; transmission lines connected to respective electrodes; and a terminal pad part, wherein the surface of each working electrode is modified with a conductive nanomaterial to reduce interfacial impedance and improve sensitivity to electrical signals.