429 results about "Electro physiology" patented technology

The invention provides an intelligent rehabilitation training method and system based on artificial intelligence and virtual reality, a user wears an intelligent wearable device to collect multi-modal data such as electroencephalogram, myoelectricity, physiological features and motion signals, the multi-modal data is preprocessed and then input into an artificial intelligence training model, and key features are extracted and fused by using a graph convolutional network and an attention mechanism algorithm. And constructing a digital twinborn model by using the fusion features, performing real-time dynamic mapping and predictive simulation, and generating a customized training scheme by means of a reinforcement learning algorithm in combination with a rehabilitation target and a physical state of the user. A user is trained in the virtual reality interaction model, the system monitors actions and physiological states in real time, the digital twin model synchronously acts, and the scene is dynamically adjusted. After training, the rehabilitation effect is evaluated according to the physiological indexes, the motion data and the twinning optimization analysis result, and an optimization training scheme and a digital twinning model are fed back. Precision, individuation and intelligentization of rehabilitation training are achieved, and the training effect and quality are improved.

The present disclosure provides a method and system for analyzing unipolar electrophysiological (EP) signals acquired by a multi-electrode catheter placed in a ventricle of a patient's heart. The method includes receiving unipolar EP signals, extracting unipolar far-field signals from the unipolar EP signals, and analyzing the extracted unipolar far-field signals to estimate the distribution of scar regions across a thickness of wall tissue of the ventricle. Using the estimated distribution, a unipolar far-field EP map showing the scar regions is generated. The method further includes displaying the unipolar far-field EP map including the scar regions to a user. The system comprises a display device and a processor configured to perform the steps of the method.

Aspects of the present disclosure relate to flexible catheters for electrophysiological mapping and ablation using high-density electrode arrays. These catheters can be used to detect the electrophysiological properties of tissue in contact with the electrodes and perform monopolar and bipolar ablation of the tissue.

A catheter for electrophysiology applications is disclosed herein that includes a tubular member and an end effector. The end effector is coupled to a distal portion of the tubular member which as a cross-section intersecting first and second orthogonal planes extending along a longitudinal axis. The end effector includes first, second, and third loop members and has an unconstrained configuration in which the first and second loop members respectively define planar surfaces which each intersect the first and second orthogonal planes and the third loop member defines a planar surface that intersects only one of the first and second orthogonal planes.

Aspects of the present disclosure are directed to intravascular electrophysiology catheters which utilize electrodes on flexible electronic circuit(s) to facilitate reduced assembly complexity and cost. In one example embodiment, a distal tip assembly of an electrophysiology catheter is disclosed. The distal tip assembly including a catheter shaft, flexible circuitry, and a distal tip coupled to a distal end of the catheter shaft. The catheter shaft includes an outer surface with a trench extending into the outer surface, and the flexible circuitry is inserted into the trench and coupled to the catheter shaft. The flexible circuitry includes one or more electrodes configured and arranged to sense electrophysiological characteristics of tissue.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

Systems and methods for providing recommended doses and instructions for pharmaceutical treatments, and more particularly systems and methods for measuring physiological and / or electrophysiological signals from a subject and providing recommended doses and instructions of pharmaceuticals based at least in part on the measured signals are described. Further, the recommended doses and instructions are based at least in part on a subject's disease(s), disorder(s), or injuries and / or symptom(s) of such, and / or side effects of medications or treatments the subject is receiving. The recommended doses and instructions are personally tailored to the subject to precisely and personally address the subject's needs, and may be based in part on correlation with database(s) comprising historical data of the subject and / or other subjects to treat the subject's movement disorders, injuries to the body or brain, cognitive diseases and disorders, sleep diseases and disorders, chronic pain, or other conditions.

The invention relates to the technical field of neuroscience and medical treatment, in particular to an individual head model dynamic optimization method and system based on transcranial alternating current stimulation. According to the method, the three-dimensional initial individual head model is partitioned, the parameters are set, the real-time electrical stimulation parameters and the real-time electroencephalogram information of the individual head under the stimulation effect are synchronously obtained, and then the parameters of the initial head model are adjusted according to the real-time data; then, according to the obtained data, simulating and calculating electrophysiological change data of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation, and based on a focusing index value and a uniformity index value calculated through the electrophysiological change data, optimizing dynamically-adjusted head model parameters, and finally, calculating a focus index value and a uniformity index value of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation. The information entropy difference value obtained through calculation of the optimized head model parameters and the real-time electroencephalogram information is compared with a preset difference threshold value, a clear termination condition is provided for optimization of the head model parameters, and therefore dynamic optimization of the individual head model is achieved.

The invention provides a cognitive function in-vitro simulation method based on a microelectrode array (MEA) and brain organs and application of the cognitive function in-vitro simulation method, and belongs to the crossing field of brain organ in-vitro model construction, neural engineering, brain-like intelligence and the like. In an in-vitro environment, external signal (such as sound) information is converted into electrical stimulation input capable of being perceived by brain organs in an MEA electrical stimulation mode, so that the brain organs gradually learn and identify external signals in repeated electrical stimulation; after elution stimulation for a period of time, the reduction condition of recognition accuracy is observed, namely, the important memory and forgetting process in the cognitive function is simulated. According to the method, the electrophysiological response and the biological mechanism change of the brain organs under external electrical stimulation can be quantitatively evaluated, and a new possibility is provided for exploring the advanced cognitive function by utilizing the brain organs.

The invention relates to the technical field of biological consciousness state monitoring, in particular to an emergency patient consciousness state monitoring system based on a bio-electricity signal, and the system obtains a theta rhythm signal, an alpha wave signal and a gamma wave oscillation signal through an electroencephalogram signal acquisition module and a signal extraction module; obtaining an effective phase amplitude coupling event and an occurrence time position thereof through a phase amplitude coupling event time position positioning module; based on a patient consciousness state monitoring module, obtaining a patient consciousness state monitoring index, and fitting the patient consciousness state monitoring index with the time sequence to complete patient consciousness state monitoring. By means of the method, the theta-gamma cross-brain region phase amplitude coupling (PAC) event can be accurately positioned, the limitation that space-time positioning of the PAC event is fuzzy and dynamic tracking lags in the traditional technology is broken through, millisecond-level high-resolution monitoring of the consciousness state of an emergency patient is achieved, and a quantifiable neural electrophysiology basis is provided for real-time intervention of disturbance of consciousness.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

A catheter includes: (a) a shaft for insertion into a heart of a patient, (b) an expandable distal-end assembly, which is coupled to the shaft and is configured to make contact with tissue of the heart, (c) at least first and second electrocardiogram (ECG) electrodes, which are coupled to an outer surface of the expandable distal-end assembly, and when placed in contact with the tissue, are configured to sense ECG signals in the tissue, and (d) a reference electrode, which is positioned within an inner volume of the distal-end assembly, and in an expanded position of the distal-end assembly, the reference electrode: (i) has no physical contact with the tissue, and (ii) is positioned at a first distance from the first ECG electrode and at a second distance from the second ECG electrode, and the difference between the first and second distances is smaller than a predefined threshold.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention relates to the technical field of nerve regulation and control, and provides a closed-loop ultrasonic brain-computer interface regulation and control method for improving post-stroke dysfunction, which comprises the following steps: S1, constructing integrated closed-loop ultrasonic brain-computer interface equipment which comprises a behavior control unit, a signal generator unit, a power amplifier unit and an electrophysiological signal monitoring unit; s2, constructing a mouse model, and embedding an electrode provided for the electrophysiological signal monitoring unit for monitoring; s3, dividing the mouse model into a random stimulation group and a brain-computer interface treatment group, and simultaneously performing closed-loop ultrasonic stimulation for preset time; s4, after closed-loop ultrasonic stimulation for a preset time, performing various behavioral test evaluations including representation of a sensory movement function and representation of an emotional emotion function; and S5, carrying out neural circuit remodeling evaluation, and observing the projection change of the high-order thalamus PO nuclei region of the mouse to the primary cortex. In the aspects of targeting, closed-loop control and integrated design, the method is expected to become a novel and effective post-stroke dysfunction treatment method.

The invention provides a pain expected deviation evaluation system and method, and relates to the technical field of pain management and electrophysiological signal processing. An experiment normal form containing three clue types of'determined high pain ', 'determined low pain' and'uncertainty 'is established. In the aspect of subjective pain expectation deviation evaluation, the AUC of a determined high pain scene and the AUC of an uncertain scene and the AUC of a determined low pain scene and the AUC of an uncertain scene are compared respectively, and the AUC difference between the two scenes is analyzed. In the electrophysiological data analysis, based on the decoding accuracy of the signal detection theory, the decoding accuracy of the situations of'uncertainty ', 'determined high pain' and'determined low pain 'is compared, the difference is quantified and calculated, and an objective pain expected deviation index is obtained. The subjective pain expected deviation is combined with the objective electrophysiological signal for the first time, the limitation that the traditional method only depends on subjective evaluation is overcome, subjective prejudice and individual difference interference are avoided, and the comprehensiveness, reliability and repeatability of pain expected deviation evaluation are improved.

The invention relates to the technical field of electrocardiosignal analysis, and discloses a long-sequence electrocardiosignal disease recognition system based on a Transform architecture. The core defects that in traditional electrocardiogram analysis, waveform integrity is damaged by fixed window segmentation, a lead space topological relation is neglected, and long sequence modeling efficiency is low are overcome, a P-QRS-T waveform structure is completely reserved through the heart beat adaptive segmentation technology, and the fixed window truncation risk is eliminated; the lead anatomical topology and the space-time coding are fused, and multi-lead electrophysiological association is modeled; long sequence efficient processing is realized based on hierarchical sparse Transform, and the recognition sensitivity of complex pathologies such as arrhythmia and myocardial ischemia is improved; in combination with gradient directional regulation and control and a streaming processing mechanism, the clinical real-time requirement is met while the diagnosis accuracy is guaranteed, and finally, reliable, efficient and universal intelligent decision support is provided for early warning of heart diseases through lightweight deployment of an adaptive mobile terminal.

The invention relates to a multi-mode human body respiration prediction method and system. The method comprises the steps that a difference value time sequence T is obtained through a visible light video and a thermal infrared video, and then the respiration frequency f1 of a target human body is obtained; collecting an electrophysiological signal containing a surface diaphragm electromyogram from the thoracic skin surface by means of an electromyographic signal collection system, and processing the electrophysiological signal to obtain a separated diaphragm electromyographic signal E; the heart rate f2 of the target human body is obtained through an electrocardiosignal collecting system; acquiring an environmental parameter Y of the target human body by using an environmental parameter acquisition system; the parameters obtained in the step S1-4 are processed, and a breathing state evaluation model is built; and S4, optimizing and constructing a BP neural network, and predicting the respiratory movement state of the human body by matching the sample data obtained in the step S5 with the BP neural network.

The invention discloses an electrophysiological signal fusion method and device, relates to the technical field of biomedicine, and mainly aims to solve the problem of low cardiac imaging evaluation accuracy caused by poor effectiveness of existing electrophysiological signal fusion. The method includes the steps that electrophysiological signals obtained after cardiac organs are collected and subjected to active magnetic field compensation and passive magnetic field compensation are obtained, and active magnetic field compensation signals of active magnetic field compensation are completed by controlling a main control magnetic field source based on a deep neural network model; a passive magnetic field compensation signal of the passive magnetic field compensation is completed based on residual magnetic field data modeling; screening effective signals from the electrophysiological signals, and performing inversion reconstruction on the effective signals to obtain current density distribution; cross-modal fusion is conducted on the electrophysiological signals, the current density distribution and the electrocardiosignals through a deep learning model after model training is completed, a signal fusion result is obtained, and the deep learning model is trained based on Maxwell constraints.

The invention discloses a real-time multi-mode physiological signal analysis method for myocardial ischemia, and belongs to the technical field of data analys.The technical scheme includes that electrocardiogram, photoelectric volume pulse waves and thoracic impedance signals are synchronously acquired, and phase difference caused by motion displacement is compensated in real time by the aid of a triaxial accelerometer; constructing a multi-dimensional quality evaluation matrix containing an electrocardiogram signal-to-noise ratio, a photoelectric volume pulse wave perfusion index and a chest impedance variance, and jointly judging whether signal reconstruction is triggered or not based on a dynamic threshold value and accelerometer data; a multi-channel signal block which is judged to be low in quality through the quality evaluation matrix and comprises the electrocardiogram and photoelectric volume pulse waves and chest impedance synchronized with the electrocardiogram is input into a generative adversarial network with electrophysiological constraints to be reconstructed, and a reconstructed high-quality electrocardiogram signal is output; and myocardial ischemia key indexes are extracted from the reconstructed signals, and multi-level risk early warning is carried out. The real-time multi-modal physiological signal analysis method for myocardial ischemia has the beneficial effect that the real-time multi-modal physiological signal analysis method for myocardial ischemia is provided.

The present disclosure relates to a muscle or nerve function rehabilitation assistance device using electrically conductive hydrogel. The electrically conductive hydrogel (IT-IC) provides an electrophysiological interface function between electrically active tissues, enabling the filling of lost muscle or nerve tissue for immediate rehabilitation in the early stages. It facilitates electrophysiological signal transmission between tissues and accelerates myofiber regeneration during the recovery phase. Particularly, when the electrically conductive hydrogel (IT-IC) is applied, the transmission of electrical signals generated in the muscle can be precisely controlled by a closed-loop device capable of being feedback in conjunction with peripheral nerve stimulation and electromyogram (EMG) monitoring.

The invention belongs to the technical field of heart organ chips, and provides a heart organ chip optimization method based on mechanical matching, which is used for optimizing a double-layer substrate in a heart organ chip, a strain sensor and an electrophysiological electrode. A double-layer substrate in a heart organ chip, a strain sensor and an electrophysiological electrode are optimized, and the method comprises the steps that for the double-layer substrate simulating an extracellular matrix and an epicardium, the Young modulus is reduced by controlling phase change of tetradecane, introducing pores into polydimethylsiloxane and improving the cross-linking ratio of the polydimethylsiloxane; a strain sensor and an electrophysiological electrode based on carbon nanotube-polydimethylsiloxane are integrated into a double-layer substrate, and the Young modulus of the strain sensor and the Young modulus of the electrophysiological electrode are matched with the Young modulus of the double-layer substrate. The problem that the Young modulus of the substrate and the sensor material is too high is avoided, and mechanical matching among the soft myocardial tissue, the sensor and the substrate is achieved.

A device and system for real-time visualization of the electrophysiologic activity of a brain, particularly at the cortical surface. The neural device can acquire, process, and display high-spatiotemporal-resolution electrophysiologic data in real-time across entire electrode arrays spanning many thousands of electrodes over identified anatomic regions. The system is compatible with thin-film cortical surface electrodes that record from neural tissues without damaging those tissues. The system can be used to guide diagnostic and therapeutic actions with high precision, and also provides the basis for a brain-computer interface.

The invention relates to deep brain stimulation equipment capable of realizing an explosive stimulation mode, which comprises a deep brain stimulation unit and a deep brain stimulation electrode which are connected through an in-vivo lead. Wherein the deep brain stimulation unit is configured to enable a deep brain stimulation electrode to perform deep brain stimulation nerve regulation and control on a target spot in an explosive stimulation mode with adjustable stimulation parameters. Wherein one or more of group frequency, intra-group frequency and sine wave quantity in a single stimulation period can be adaptively adjusted by the deep brain stimulation unit according to different electrophysiological characteristics of a target point.

The invention relates to the technical field of physiotherapy, in particular to a multi-mode physiotherapy system integrating ion release and electromagnetic stimulation. The system comprises a biological feedback sensing module, an intelligent control module, a space-time cooperative control module, a multi-channel ion release module, a multi-band electromagnetic generation module and a self-adaptive optimization module. Firstly, skin impedance, blood flow and temperature parameters are collected by the system, and a collaborative strategy is generated through multi-parameter analysis of an intelligent control module; the time-space cooperative control module synchronously adjusts the ion release and electromagnetic field excitation time sequence; the multi-channel ion release module realizes quantitative release of multiple types of ions by utilizing microfluidics; the multi-band electromagnetic generation module outputs low-frequency, medium-frequency and high-frequency programmable electromagnetic fields; the adaptive optimization module dynamically corrects treatment parameters based on the electrophysiological feedback. According to the system, multi-modal fusion of iontophoresis and electromagnetic stimulation is realized, and the accuracy and individualization level of treatment are improved.

Neurological interfacing with a user is performed using by one or more hardware data processors including processing circuitry and memory that receives stimulation data from at least one data input source for stimulating two or three different axes of motion and processes the stimulation data to determine at least two of pitch, yaw, and roll parameters. Based on the at least two of the pitch, yaw, and roll parameters, at least two of a yaw current signal, a pitch current signal, and a roll current signal are determined. The at least two of the yaw current signal, the pitch current signal, and the roll current signal are time- multiplexed modulated signals.

The present invention is directed to a system for monitoring and evaluating electrogram signals representing electric activities of a heart chamber by computing the entropy of the electrogram signals, said system comprising a signal input that is connected to a mapping catheter comprising at least one electrode pole for picking up electric potentials and generating electrogram signals from the picked up electric potentials, along with the 3D location of the catheter at the time of recording and a signal processing and evaluation unit for processing and evaluating electrogram signals received at the signal input. The invention is further to a non-transitory computer readable medium for use in a system for monitoring and evaluating electrogram signals representing electric activities of a heart.

The invention discloses a three-dimensional electric field vector detection and microdialysis synchronous sampling implanted probe system, and belongs to the technical field of neural electrophysiology detection. The system comprises a vector electric field detection module (containing multiple layers of platinum-iridium alloy contacts) and a microdialysis module (provided with a semi-permeable membrane window with high selectivity and biological compatibility) which are coaxially integrated, and spatial isolation and synchronous acquisition of electro-chemical signals are realized through an insulating support structure. The problems of separation and spatial dislocation of an electric field detection and biochemical sampling system in the prior art are solved, real-time association of three-dimensional electric field vector reconstruction and neurotransmitter analysis is supported, and accurate electro-chemical coupling data is provided for nerve regulation and control research.

The invention belongs to the technical field of cognitive power, and relates to a cognitive competence assessment method, training method, system, equipment and medium, the system comprises a data acquisition unit used for acquiring electrophysiological signal data and behavior data of a user; the evaluation unit is connected with the data acquisition unit and is used for executing a cognitive evaluation scale task and synchronously acquiring the electrophysiological signal data, the behavior data and the scale score of the user; the fusion analysis unit is connected with the data acquisition unit and the evaluation unit and is used for integrating the cognitive evaluation scale data, the electrophysiological signals and the behavior data and inputting the data into a multi-modal fusion evaluation model so as to output a cognitive load index and an emotional stability score; and the training unit is connected with the fusion analysis unit and is used for adjusting the VR training difficulty according to the cognitive load index and the emotion stability score. According to the scheme of the invention, the accuracy of cognitive ability evaluation and the applicability of cognitive training are effectively improved.

The invention discloses a spatial registration and interpolation calculation method and system for cardiac electrophysiological parameters. The method comprises the following steps: segmenting heart magnetic resonance image data by using a three-dimensional DRLSE algorithm, and establishing a heart three-dimensional anatomical model; aligning the electrophysiological parameter data to a grid structure of the heart three-dimensional anatomical model, and performing anatomical model registration to obtain a registered electrophysiological parameter point set; on the registered electrophysiological parameter point set, anisotropic kernel function interpolation is designed, and multi-temporal electrophysiological data is formed; and introducing space-time coupled dynamic interpolation to the multi-temporal electrophysiological data to obtain a continuous three-dimensional space-time electrophysiological parameter field which is reconstructed in real time. The system comprises a three-dimensional anatomical model establishment module, a registration module, a high-precision static electrophysiological field generation module and a space-time coupling module. According to the method, high-precision non-rigid registration of the electrophysiological data and the grid model is realized through anatomical mark points.