301 results about "Electro physiology" patented technology

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 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 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 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 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 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 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 provides a pulse condition and electrophysiological signal data processing method and system for a pulse feeling instrument and the pulse feeling instrument. The method is applied to pulse condition information collection and comprises the following steps that under driving of at least a first pressure value range, at least first pulse condition information of a target object is collected; under driving of at least a second pressure value range, at least second pulse condition information of the target object is collected; and fusing the at least first pulse condition information and the at least second pulse condition information to obtain first pulse condition graph information of the target object. The pulse width and pulse length information of the pulse condition under different pressure values is obtained by changing the pressure values, and physiological and pathological changes of the blood vessel volume are reflected truthfully. Through collection of multiple parameters, the pulse diagnosis instrument can provide more comprehensive and accurate pulse condition graph information, the defects of a traditional pulse diagnosis instrument are overcome, and therefore the accuracy and reliability of pulse diagnosis are improved, doctors are helped to conduct finer pulse diagnosis analysis, and the traditional Chinese medicine pulse taking process is restored to the maximum extent.

Systems and methods for high-bandwidth, minimally invasive brain-computer interfaces (BCIs) are disclosed. The BCIs are configured for deployment and operation in conjunction with a comprehensive interventional electrophysiology procedural suite. Three primary methods of minimally invasive electrode array delivery are disclosed: (1) cortical surface delivery, (2) ventricular delivery, and (3) endovascular delivery. Additionally, systems and methods for interacting with such high-bandwidth electrode arrays are discussed, including real-time imaging, signal processing, and neural decoding. Systems and methods for architectures for accelerating the underlying computational processes (such as graphics processing units or tensor processing units) are also discussed. Multiple applications of BCIs are discussed, with emphasis on restoration, rehabilitation, and augmentation of neurologic function.

A medical device is configured to sense one or more electrophysiological signals including a distal vector signal sensed using a distal electrode of a medical electrical lead when the medical electrical lead is connected to the medical device. Based at least on the distal vector signal, the medical device may detect a migration of a medical electrical lead carrying the distal electrode and generate an output in response to detecting the migration of the medical electrical lead.

The invention provides an electrocardio physiological signal feature extraction method for heart conduction block classification, and belongs to the technical field of electrocardio physiological signals. 12-lead electrocardiosignals and auxiliary signals of a patient are collected, a wave equation is established through a water-wave-imitating interference multi-signal fusion algorithm, and a multi-lead signal interference mode is simulated; the method comprises the following steps: extracting time domain characteristic parameters including a PR interval, a QRS wave width, a QT interval and the like from a fusion signal, and calculating frequency domain characteristic parameters including power density distribution and a heart rate variability index; nonlinear dynamic characteristic parameters including a sample entropy value, a fractal dimension and the like are extracted, the conduction block type is judged according to the PR interval length and a QRS wave falling mode, and the technical problem that a weak heart conduction block characteristic signal cannot be accurately recognized through single-lead electrocardio analysis is solved.

The subject matter of the invention is a system and method for mapping electrophysiological feature points within the heart. Systems and methods for locating electrophysiological (EP) feature points in real time during electrophysiological mapping of a heart are disclosed. The method includes tracking a location of a distal end of a catheter within the heart, recording a reference location of a reference feature point in the heart, and determining a region of interest (ROI) for locating a found EP feature point relative to the reference feature point. As long as the distal end is within the ROI, an IEGM signal measured by the distal end and a concurrent ECG signal are processed to determine whether a predetermined characterization signal feature of a found EP feature point appears within a predetermined time window portion of the IEGM signal relative to a reference timing provided by the ECG. In the event that the characterization signal feature appears, the position of the IEGM signal is measured and identified as the position of the found EP feature point.

The invention discloses a three-dimensional heart chip device. The device comprises a pneumatic control layer, a flexible functional film layer and a microfluidic culture layer, wherein the flexible functional film layer is integrated with a stretchable microelectrode array with a snakelike structure, and the microfluidic culture layer is arranged on the flexible functional film layer; wherein the pneumatic control layer is used for driving the flexible functional film layer to generate periodic deformation and applying mechanical tensile strain to cardiac muscle tissues in the microfluidic culture layer, and meanwhile, the stretchable microelectrode array of the snake-shaped structure synchronously deforms along with the flexible functional film layer and keeps conformal electric contact with the cardiac muscle tissues, so that the mechanical tensile strain is applied to the cardiac muscle tissues in the microfluidic culture layer. The electrophysiological signals are monitored in the dynamic mechanical stretching process. The technical problem that in the prior art, stable electrophysiological monitoring cannot be carried out while dynamic mechanical stretching is applied is solved, physiologically-related mechanical stimulation can be applied to three-dimensional cardiac muscle tissue to promote maturity of the three-dimensional cardiac muscle tissue, and long-term and stable in-situ electrophysiological signal monitoring is achieved in the process; and a more accurate in-vitro model is provided for drug screening and disease research.

The invention discloses a self-adaptive micro-current beauty method and beauty instrument based on electrophysiological index feedback, and relates to the technical field of photoelectric beauty, the method comprises the following steps: obtaining a surface electromyogram signal of a user face, extracting characteristic data of the surface electromyogram signal, and determining current intensity and current frequency of an NMES based on the characteristic data; acquiring a first feedback electric signal and a second feedback electric signal to determine the impedance value of the skin cuticle and the impedance value of the active skin layer, and determining the current intensity of the micro-current and the working time proportion of the NMES and the micro-current according to the impedance values of the skin cuticle and the active skin layer; therefore, a user face electrical stimulation signal is determined. According to the method and the device, parameters of the NMES and the micro-current can be adaptively adjusted, so that different somatosensory and beautifying effects are generated according to different skin states, and a good beautifying effect can be achieved while the somatosensory of a user is ensured.

This invention discloses a breathable, conductive hydrogel electrode, its preparation method, and its applications. The breathable, conductive hydrogel electrode comprises a porous fiber support structure and a hydrogel conductive network. The porous fiber support structure has a three-dimensional porous structure formed by interwoven porous fibers, each fiber possessing a three-dimensional interconnected channel structure formed by interconnected pores distributed on and within the porous fibers. The hydrogel conductive network is distributed on the surface and within the porous fiber support structure, forming a continuous ion conduction path within the electrode. The breathable, conductive hydrogel electrode provided by this invention has advantages such as high breathability, self-humidification, low impedance, and breathability, achieving high-fidelity, long-term acquisition of physiological signals such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It can be widely used in electrophysiological signal monitoring, smart wearables, sleep analysis, and human-computer interaction.

The invention discloses a positioning and delimiting method and system for a cortex pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signals; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments so as to obtain a network analysis core index; generating a fusion type three-dimensional brain model according to the network analysis core index and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiology functional area distribution diagram according to the fused three-dimensional brain model and the network analysis core indexes; and according to the space-time three-dimensional electroencephalogram model and the electrophysiology functional area distribution map, generating accurate electrophysiology positioning and delimiting information of the cortical pathological focus. According to the invention, accurate electrophysiological delimitation of intra-operative cortical pathological lesions (glioma, hemangioma, focal cortical dysplasia and the like) is realized.

A system includes an interface and a processor. The interface is configured to receive multiple electrophysiological (EP) signals from a tissue area along an ablation curve inside a cardiac chamber of a heart of a patient. The processor is configured to (i) generate local conduction vectors (LCVs) for the area based on the multiple EP signals, (ii) estimate a level of change between sets of LCVs along the ablation curve within the tissue area, and (iii) based on the level of change, identify a presence of a conduction gap in the ablation curve.#

The invention discloses a sensing and storage calculation integration method of an electrophysiological sensing device and an artificial synaptic storage calculation device, relates to the technical field of neuromorphic calculation, and aims to solve the problem of separation of wearable health monitoring sensing and storage calculation. The method comprises the following steps: S1, precisely preparing two-dimensional MXene by a liquid phase etching method; s2, constructing an MXene electrophysiological sensing device; s3, constructing an MXene artificial synaptic device; s4, the MXene electrophysiological sensing device in the step S2 and the MXene artificial synapse device in the step S3 are integrated; wherein the step S4 specifically comprises the following steps: S4-1, leading out a source electrode, a grid electrode and a drain electrode of the MXene artificial synapse device through a bonding machine instrument, connecting the grid electrode with the MXene electrophysiological sensor, applying a voltage of 100mV to the source electrode of the MXene artificial synapse device, testing a current change value of a channel material of the MXene artificial synapse device by the drain electrode, and presenting a curve through a mobile terminal, therefore, the human body electrophysiology compression signal can be presented.

Systems and methods for clustering wavefront signals in an electrophysiology map of cardiac tissue are provided. The systems and methods include receiving an electrophysiology map of the cardiac tissue, displaying propagation of the wavefront signals as a plurality of velocity vectors, discretizing the received electrophysiology map into a plurality of segments, clustering the velocity vectors into at least one group within each segment based on predefined criteria, and generating a trend line representing each group of clustered velocity vectors within each segment in the electrophysiology map.

The invention belongs to the technical field of vocal music training, and provides a singing expiration strength stability training system for breath flow control, comprising: a hardware sensing module for collecting airflow dynamic data, respiratory muscle group electrophysiological data and respiratory kinematics data of a user; the data processing control center is used for receiving and processing the airflow dynamic data, the respiratory muscle group electrophysiological data and the respiratory kinematics data to obtain a processing result; the multi-mode feedback module outputs real-time feedback to the user through visual, auditory and tactile multiple channels based on the processing result; according to the method, heterogeneous data of airflow dynamics, electromyography and respiratory kinematics are synchronously collected and fused, song audios are analyzed in combination with an artificial intelligence algorithm to generate a scene-based target curve, and a personalized training baseline is established based on accurate decision and multi-modal sensory collaborative feedback of a fuzzy logic algorithm; the efficient, accurate and personalized improvement of the stability of the exhaling force of singing is realized.

A neuronavigation system including a microelectrode array configured to be placed on a brain surface and record neural signals, the microelectrode array comprising a fiducial, and a computer system configured to register a position of the microelectrode array with respect to the brain surface based on the fiducial, receive neural signals from the microelectrode array, and generate a visualization overlaying the received neural signals on a brain image based on the registered position of the microelectrode array for display during a neurosurgical procedure. The neuronavigation system can be used in a variety of different neurosurgical applications, including implanting neural interfaces or locating particular cortical regions.