514 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 invention discloses an electrophysiology and hemodynamics multi-mode intelligent wearable system and a data evaluation method, and belongs to the technical field of biomedical engineering, the system comprises an optical module, a flexible stretchable electric signal module and a central module; the optical module is used for collecting hemodynamic signals including an oxyhemoglobin change signal, a deoxygenated hemoglobin change signal and a pulse wave signal by combining a near-infrared light signal with hemodynamics; the flexible stretchable electric signal module collects electric signals, including electroencephalogram signals, electromyographic signals, electrocardiosignals and electro-oculogram signals, of all parts through electrodes; the central module is used for digitalizing and preprocessing the hemodynamic signal and the electric signal of each part, fusing multi-modal data, evaluating data quality and predicting a dominant external event influencing the data quality; and meanwhile, data quality and dominant external events are output to the terminal to feed back and adjust acquisition parameters of the optical module and the flexible stretchable electric signal module, so that the effectiveness and practicability of monitoring are remarkably 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.

The present invention discloses an electrophysiological catheter having a distal microelectrode assembly, the distal microelectrode assembly having a covered ridge carrying a plurality of microelectrodes. The microelectrode assembly includes a first ridge radiating away from the longitudinal axis and a second ridge adjacent to the first ridge and radiating away from the longitudinal axis, the first ridge having a plurality of first microelectrodes disposed thereon. The second ridge has a plurality of second microelectrodes disposed thereon such that a first virtual circle intersecting one of the plurality of first microelectrodes does not intersect any of the second microelectrodes.

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.

A multi-electrode assembly of the present disclosure includes a plurality of electrodes; and an electrode support member; wherein a portion of the electrode support member is adapted to selectively ablate a tissue in contact therewith, and at least a portion of the electrodes are configured and arranged on the electrode support member to detect electrophysiological characteristics of the tissue. The electrode support member can be constructed of flexible material and shaped to facilitate contact with certain anatomical structures (e.g., linear, loop, spiral, planar array, or basket shapes). Embodiments described enable selective activation of the electrode support member for ablation and selective activation of electrodes for electrophysiological mapping and / or ablation.

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.

The invention provides a neuromuscular multi-element coupled organoid micro-fluidic chip, a device and a method. Relates to the technical field of organoid micro-fluidic chips. A first culture chamber, a second culture chamber and a third culture chamber are arranged in a culture layer of the chip; the first culture room is used for culturing nerve cells; the second culture room is used for culturing muscle cells; a microfluidic channel for communicating the first culture chamber with the second culture chamber is arranged between the first culture chamber and the second culture chamber; the third culture room is arranged on the micro-fluidic channel and is used for culturing a neuromuscular joint formed by convergence of axons of nerve cells and muscle cells; a microelectrode array plate is arranged in the circuit layer and is used for performing electrical stimulation and electrophysiological function detection on neuromuscular cells. Through the chip, co-culture of in-vitro nerves and muscle organs can be realized, a complex interaction relationship in a neuromuscular system can be accurately simulated, and the chip can also be used for constructing various neuromuscular related disease models.

The multi-electrode assembly of the present disclosure includes a plurality of electrodes and an electrode support member, a portion of which is adapted to selectively ablate tissue in contact therewith, and at least a portion of the electrodes are disposed on the electrode support member and configured to detect electrophysiological properties of the tissue. The electrode support member may be constructed of a flexible material and may be shaped to easily contact a particular anatomical structure (e.g., a straight shape, a loop shape, a spiral shape, a planar array shape, or a basket shape). The described embodiments allow for selective actuation of the electrode support member for ablation and selective actuation of the electrodes for electrophysiological mapping and / or ablation.

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 relates to the technical field of medical instruments, and discloses an implantable neural electrode for long-term electrophysiological signal monitoring and a preparation method and application thereof. The neural electrode comprises an upper hydrogel insulation packaging layer, a lower hydrogel insulation packaging layer, an electrode site, a connecting line and a bonding pad, wherein the electrode site, the connecting line and the bonding pad are arranged between the upper hydrogel insulation packaging layer and the lower hydrogel insulation packaging layer, and the electrode site and the bonding pad are connected through the connecting line. The electrode sites, the connecting lines and the bonding pads are made of conductive hydrogel; the upper hydrogel insulation packaging layer and the lower hydrogel insulation packaging layer are made of insulation polymers and hydrogel. The insulating polymer is a plasma surface-modified insulating polymer. The preparation method of the neural electrode is simple and efficient, biotoxic materials are not introduced in the preparation process, good biocompatibility is achieved, continuous monitoring in a living body can be achieved for one year, important application value is achieved for studying signal response and neural circuits of all brain regions, and a beneficial tool is provided for long-term in-vivo monitoring.

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.

Disclosed in the present invention is a cerebral function coupling analysis method based on electrophysiological and blood microcirculation signals. The method comprises: in different motion states, synchronously acquiring an electroencephalogram signal and an fNIRS signal of a subject, and preprocessing same to obtain a preprocessed electroencephalogram signal and a cerebral oxygen signal; performing complete ensemble empirical mode decomposition with adaptive noise on the electroencephalogram signal, so as to obtain a plurality of different sub-band components; symbolizing the plurality of different sub-band components of the electroencephalogram signal and a time sequence of the cerebral oxygen signal, so as to obtain symbolic sequences of the different sub-band components and a symbolic sequence of the cerebral oxygen signal; calculating the transfer entropy between every pair of the symbolic sequences of the different sub-band components and the symbolic sequence of the cerebral oxygen signal; and on the basis of the transfer entropy, analyzing the degree of coupling between the electroencephalogram signal and the cerebral oxygen signal. The present invention explores nonlinear coupling characteristics and information transfer of electrophysiological and blood microcirculation signals, thereby providing a basis for exploring the mechanism of generation of motion dysfunction and for proposing cooperative rehabilitation intervention means.

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 the technical field of medical health, in particular to a cardiac muscle tissue electrophysiology unmarked optical detection method based on deep learning. The method comprises the following steps: acquiring optical detection image data and electrophysiological signal data of myocardial tissue; the optical detection image data and the electrophysiological signal data are subjected to image preprocessing, so that optical-physiological signal mapping data are obtained, and image preprocessing comprises image registration, image cutting, periodic enhancement, time difference enhancement, spatial filtering, intensity normalization and data smoothing; and according to the optical-physiological signal mapping data, carrying out feature signal extraction based on a mechanical contraction curve and an electrophysiological curve so as to obtain mechanical contraction curve data and electrophysiological curve data. According to the method, unmarked optical detection is adopted, so that direct interference on myocardial tissues is avoided, and the physiological state of the tissues is better maintained.

Presented herein are a system and a method comprising obtaining one or more electrophysiological measures of acoustic hearing associated with a hearing device recipient, obtaining one or more hearing performance measures of the recipient, analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measure, and generating an output based on the analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures.

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.