16 results about "Conduction delay" patented technology

The invention relates to the technical field of electromyographic signal monitoring, and discloses a guided monitoring method and system based on a wireless surface electromyographic analysis feedback instrument. The method comprises the following steps: acquiring electromyographic signal time sequence data and limb movement track space coordinate information when a target muscle group moves through a multi-channel wireless surface electromyographic sensor; muscle activation intensity and fatigue change features are extracted, a joint activity angle change sequence is calculated, and a muscle-joint collaborative activity feature matrix is generated through space-time alignment fusion; analyzing signal transduction delay and amplitude matching degree in the matrix, and evaluating the nerve driving coordination level; according to the deviation from the preset standard mode, generating a real-time biological feedback signal containing an action correction instruction and a muscle activation intensity adjustment parameter; and converting into a tactile vibration mode and a visual guidance mark to be output. According to the method, synchronous acquisition and fusion analysis of multi-dimensional motion information are realized, and visual guidance is provided through multi-modal feedback.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The invention relates to the technical field of temperature sensors, in particular to a temperature sensor dynamic response acceleration method and system for medical liver testing, and the method comprises the steps: collecting an original temperature measurement sequence outputted by a main temperature sensor in a first target region in real time, synchronously acquiring a reference temperature sequence output by a reference temperature sensor located in a second target area; and based on the physical characteristics of the main temperature sensor and the heat exchange characteristics of the liver tissue, constructing a sensor and liver tissue dynamic response model containing key dynamic parameter vectors. The dynamic response model of the sensor and the liver tissue is constructed, the dynamic characteristics of the sensor and the tissue in the current environment can be accurately quantified, thermal inertia and conduction delay are actively compensated from the algorithm level through the model inverse function, the real-time performance of temperature monitoring is remarkably improved, and the temperature monitoring accuracy is improved. And a timely and reliable feedback signal is provided for closed-loop control.

The invention relates to the technical field of intelligent medical rehabilitation and computer vision, in particular to a muscle-bone rehabilitation posture motion analysis system based on visual recognition. Comprising a visual analysis management center, a motion feature extraction unit, a dynamics inversion unit, a compensation coupling analysis unit, a time sequence conduction unit and a rehabilitation evaluation decision unit. Geometric compliance is analyzed and discriminated through dominant kinematics characteristics; if so, performing visual moment inference consistency analysis by using a dynamic inversion unit; in response to moment discretization, acquiring and analyzing a compensation coupling coefficient and kinematic chain conduction delay respectively; and finally, calculating a compensation evaluation index through multi-dimensional compensation quantitative matching analysis, and generating a recessive compensation or effective driving signal. According to the method, a layered filtering mechanism is constructed, implicit problems are dominated, and the blank of monitoring of the internal force exerting mode in non-contact rehabilitation evaluation is filled up.

The invention discloses an anesthesia state evaluation method and device suitable for patients of different ages and a medium, and belongs to the technical field of neural signal analys.The method specifically comprises the steps that electroencephalogram response signals triggered by stimulation input events corresponding to the patients of different ages in different consciousness states are collected; extracting nerve conduction delay time and frequency domain energy characteristics based on the electroencephalogram response signal, and constructing a delay characteristic vector based on the nerve conduction delay time and the electroencephalogram signal frequency domain energy characteristics; inputting the age data and the delay feature vector of each training patient into a preset training model, and generating a state recognition model; the method comprises the following steps: analyzing nerve conduction velocity migration features of patients of different ages under different consciousness states, acquiring the age and delay feature vectors of a current patient, inputting the age and delay feature vectors into a state recognition model, and judging the anesthesia state of the patient. And prediction and accurate evaluation of anesthesia states of patients of different ages are realized.

The invention discloses a control method of an artificial stomach, and belongs to the technical field of bionic medical instruments. Under the state that the starting signal is monitored, all the driving pieces are controlled to drive the diastole assembly according to a preset time sequence, so that the corresponding part of the artificial stomach is subjected to contraction or diastole movement, and the content in the artificial stomach is subjected to regular movement from the inlet direction of the artificial stomach to the outlet direction of the artificial stomach. By changing the time point and interval of successive movement of the whole stomach diastole assembly, the pathological characteristics of peristaltic wave conduction delay and starting time sequence disorder are simulated, and then by adjusting the interval between the contraction stop time point and the expansion starting time point, the expression of abnormal extension of the diastole and contraction switching interval or interruption of the peristaltic wave in the pathological state is re-engraved.

Various systems and methods are provided for determining information related to cardiac deformation of a region of interest of a heart of a subject using deformation imaging based on a delineation of the cardiac cycle of the heart of the subject determined from imaging data. The imaging data of the region of interest of the heart of the subject may be received. A set of mechanical events of the heart of the subject may be detected based on analyzing the imaging data. The delineation of the cardiac cycle may be determined based on the set of mechanical events of the heart of the subject. The information related to cardiac deformation of the region of interest of the heart of the subject may be determined using deformation imaging based on the delineation of the cardiac cycle. The information related to cardiac deformation of the region of interest may be displayed.

The invention discloses a binocular modulation difference-based visual stabilization method, and particularly relates to the technical field of biomedical engineering, and the method comprises the following steps: S1, binocular neural signal conduction delay data acquisition, S2, visual stability adaptive time difference threshold setting, S3, near-infrared light parameter and conduction velocity association relationship establishment, S4, binocular near-infrared light parameter combination screening, and S5, visual stability adaptive time difference threshold setting. And S5, calibrating double-path driving delay parameters. According to the method, a time difference threshold interval is set in combination with a visual phase difference physiological adaptation range, the incidence relation between the near-infrared light parameters and the nerve conduction velocity is established, parameter setting is deeply fused through binocular nerve conduction delay data and cortical neuron response time sequence data, the adaptability of the parameters and the nerve physiological state is enhanced, and the accuracy of the nerve physiological state is improved. The collaborative accuracy of near-infrared light modulation parameters of the two eyes is improved, it is ensured that the time sequence of the two-way driving signals is accurately matched with a nerve regulation target needed by visual stability, and the problem that the visual regulation effect is poor due to parameter setting deviation is solved.

The application discloses a brain-muscle function change coupling analysis method based on bidirectional delay compensation, which comprises the following steps: designing an experiment paradigm, collecting signals and pre-processing the signals; determining a bidirectional cortical muscle delay estimation algorithm based on a sliding window and transfer entropy TE, and quantifying the conduction time of the EEG->EMG outgoing path and the EMG->EEG incoming path of the cortical muscle of a test person; performing signal time alignment compensation, calculating the transfer entropy with delay compensation and the average delay compensation transfer entropy, and constructing a directional delay compensation directional cortical muscle connection CMC network based on the transfer entropy with delay compensation and the average delay compensation transfer entropy; extracting evaluation features, and performing brain-muscle function change coupling analysis based on the evaluation features; and the evaluation features are extracted based on the dimensions of the CMC coupling strength, the hemispheric lateralization index and the correlation of the delay-motor function behavior index score based on conduction delay, hemispheric division and task stage. The application also discloses a corresponding system, an electronic device and a computer readable storage medium.

The invention provides an Internet of Things data transmission link diagnosis and analysis method based on deep learning, and relates to the technical field of transmission link diagnosis, and the method specifically comprises the steps: dividing a plurality of stages of transmission segments and a plurality of continuous non-overlapping windows; constructing a feature vector and inputting the feature vector into the trained deep learning model to obtain the state of each window; positioning a first abnormal window of each transmission segment, determining an initial segment and an earliest abnormal window, obtaining a next-level abnormal association window and an abnormal overlapping window, calculating abnormal conduction delay time, and counting the total number of the abnormal windows; and in combination with the window correction coefficient and the abnormal overlapping proportion, completing correction of the total number of abnormal windows, locking an abnormal time range and realizing abnormal point location positioning. According to the method, the defect of insufficient time dimension dynamic diagnosis is made up, the problem of fuzzy abnormal time sequence boundary positioning is solved, the time precision and timeliness of link abnormality diagnosis are greatly improved, and the problem of inter-segment confusion in a multi-segment synchronization abnormality and cross-segment conduction scene is effectively solved.

The application relates to the technical field of living cell culture and electric stimulation, and discloses an electric stimulation time domain forbidden avoidance synchronization method and device for living cell culture, which comprises the following steps: acquiring the action time sequence information of the internal mechanical components of a device in real time, combining vibration conduction delay and oscillation decay time, constructing and dynamically maintaining a time domain vibration forbidden zone model; performing high-speed monitoring and recording time stamp on an external TTL trigger signal, performing geometric comparison between the TTL trigger signal and the active forbidden zone interval in the model; and finally performing safe passing or delay delivery processing according to the inclusion relationship. The application avoids the interference of electromechanical coupling peak noise at the trigger moment, and guarantees the complete recording of the electric physiological data in the first period after stimulation.

The invention provides a delay neuron circuit designed based on an FH model, and relates to the technical field of neuromorphological engineering, the delay neuron circuit comprises an initial segment myelin sheath structure and at least one middle segment myelin sheath structure; the voltage difference between the node a of the initial section myelin sheath structure and the ground forms the membrane potential of the section, and the voltage difference between the node b of the middle section myelin sheath structure and the ground forms the membrane potential of the section; the node a and the node b are connected through a resistor RT used for representing the resistance in the axon direction, and the nodes bi (i = 1, 2, 3,...) of the multiple middle segment myelin sheath structures are connected through the resistor RT. The delay neuron circuit can introduce flexible conduction delay through an adjustable myelin sheath structure while realizing basic discharge characteristics of biological neurons, so that a signal propagation process in a biological nervous system is reflected more truly.

The invention discloses a brain muscle function change coupling analysis method based on bidirectional delay compensation, and the method comprises the steps: designing an experiment normal form, and carrying out the signal collection and signal preprocessing; determining a bidirectional cortical muscle delay estimation algorithm based on a sliding window and a transfer entropy TE, and quantifying conduction time of an EEG-EMG outgoing path and an EMG-EEG incoming path of cortical muscles of a tester; signal time alignment compensation is carried out, a transfer entropy with delay compensation and an average delay compensation transfer entropy are calculated, and a directional cortical muscle connection CMC network with directional delay compensation is constructed based on the transfer entropy with delay compensation and the average delay compensation transfer entropy; extracting evaluation features, and performing brain muscle function change coupling analysis based on the evaluation features; evaluation features are extracted based on dimensions of conduction delay, CMC coupling strength of hemisphere and task stages, hemisphere bias index, and delay-motion function behavioral index score correlation. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The present application relates to the field of intelligent medical rehabilitation and computer vision technology, in particular to a musculoskeletal rehabilitation posture motion analysis system based on visual recognition; comprising: a visual analysis management center, a motion feature extraction unit, a dynamics inversion unit, a compensation coupling analysis unit, a time sequence conduction unit and a rehabilitation evaluation decision unit; the geometric compliance is distinguished through explicit kinematic feature analysis; if it is compliant, the dynamics inversion unit is used for visual torque inference consistency analysis; in response to torque dispersion, compensation coupling coefficient and motion chain conduction delay acquisition analysis are carried out respectively; finally, the compensation evaluation index is calculated through multi-dimensional compensation quantitative matching analysis, and the implicit compensation or effective driving signal is generated; the present application constructs a hierarchical filtering mechanism, which makes the implicit problem explicit, and fills the gap of internal force mode monitoring in non-contact rehabilitation evaluation.

This invention relates to the field of disease progression trend modeling technology, specifically a system for modeling the progression trend of subarachnoid hemorrhage by integrating multi-source data. This invention simultaneously collects and monitors four types of signals: intracranial pressure, blood flow velocity, cerebrospinal fluid pressure, and electroencephalogram (EEG). Instantaneous phase is extracted using Hilbert transform, and the time window is adaptively adjusted based on individual cerebrovascular conduction delay characteristics to calculate the phase-locking index between the three pairs of signals. A multivariate coupled oscillator model is established, and the topological invariant features of the phase trajectory are extracted. A comprehensive trend index is generated through tensor fusion. An individualized four-dimensional phase entropy baseline model is established, employing a two-layer early warning mechanism: a direct warning is issued when all three phase-locking indices show consecutive sign flips in their second derivatives; a comprehensive trend index confirmation is required when any two phase-locking indices flip. The system predicts the state level, trend level, and expected evolution trajectory based on a support vector regression model. This invention achieves accurate prediction and early warning of disease progression, providing a basis for clinical decision-making.

The invention relates to a system and method for correcting strain traces of a conduction delayed patient in real time. Various systems (100) and methods (500) are provided for determining information related to cardiac deformation of a region of interest of a heart of a subject using deformation imaging based on a depiction of a cardiac cycle of the heart of the subject determined from imaging data. The imaging data of the region of interest of the heart of the subject may be received (510). A set of mechanical events of the heart of the subject may be detected based on analyzing the imaging data (520). The depiction of the cardiac cycle may be determined based on the set of mechanical events of the heart of the subject (530). The information related to cardiac deformation of the region of interest of the heart of the subject may be determined using deformation imaging based on the depiction of the cardiac cycle (540). The information relating to cardiac deformation of the region of interest may be displayed (550).