2267 results about "Cardiology" patented technology

An auto-occlusion safety device is provided for use in preventing rupture of infusion components during rapid infusion procedures. An occlusion valve is disclosed having a threshold pressure that is less than a rupture tolerance of the infusion system components. As the fluid pressure of the infusion system increases beyond the threshold pressure of the occlusion valve, the occlusion valve is automatically activated thereby occluding fluid flow upstream from a vulnerable infusion component. An accumulator is further disclosed for use in combination with the safety device to compensate for time delays in full activation of the occlusion valve.

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 discloses a cerebral hemorrhage postoperative gastrointestinal hemorrhage prediction method based on an LGBM model. The method comprises the steps of obtaining a multi-dimensional clinical feature sequence of a target patient, screening out a stable feature subset by adopting a Boruta feature selection algorithm, performing nonlinear relation fitting and integrated decision by utilizing a pre-trained LightGBM machine learning model, and generating an individualized ATH risk probability value; and when the risk probability value exceeds a dynamic risk threshold value, triggering a high-risk early warning signal, and based on a Kaplan-Meier survival analysis model, carrying out association mapping on a prognosis track of poor long-term neural function recovery, and finally generating a comprehensive prediction report. According to the invention, accurate quantitative evaluation of ATH risk is realized, clinical intervention timeliness is improved through a dynamic threshold early warning mechanism, short-term complication risk and long-term function prognosis are organically combined, and a comprehensive and reliable prognosis basis is provided for individualized treatment decision.

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention discloses an anesthesia depth accurate prediction system and method based on time sequence alignment. The method comprises the steps that a cross-modal joint time sequence characteristic data matrix is acquired and generated; outputting a time sequence alignment feature sample; outputting a future advanced prediction label set; obtaining a first-stage anesthesia depth prediction model; in the first anesthesia depth prediction model training stage, a logic auditing link is introduced, and a self-adaptive online updating model is formed; starting an anti-fact dose simulation module, and outputting a candidate anti-fact administration scheme set; and inputting the candidate anti-factual administration scheme set into a dynamic security constraint optimizer, rejecting non-compliant schemes by the dynamic security constraint optimizer according to single maximum dose limitation, single additional dose and administration times in a minimum administration interval, and outputting an optimal administration suggestion. According to the method, combined classification judgment is carried out on the anesthesia state grade and the change trend of each prediction step, and the sensitivity of clinical risk identification and abnormal situation early warning is effectively improved.

The invention relates to the field of bile monitoring and analysis, in particular to a dynamic bile monitoring and analysis method and device based on real-time image recognition. The method comprises the following steps: continuously collecting a bile monitoring image sequence of a drainage bag of a patient, and carrying out global image resolution optimization and multi-frame color drift analysis to obtain a multi-time-point bile color evolution sequence; performing comprehensive bile physical state vector modeling according to the bile monitoring image sequence, performing personalized bile state characteristic evolution, and constructing a bile color-property comprehensive characteristic spectrum; and performing deep color component visual detection based on the multi-time-point bile color evolution sequence, performing pathological concentration change mining, and constructing a concentration anomaly evolution model. According to the method, the real-time bile state characteristics of the patient are accurately analyzed based on precision visual identification, the risk level and deterioration prediction are combined, the dynamic adjustment of the precise early warning time point is realized, and the response speed and the early warning accuracy are improved.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

The invention provides a health state prediction method for an 8K display device, and relates to the technical field of display health prediction.The health state prediction method comprises the steps that a multi-modal feature vector is extracted from each pixel node of a multi-modal frequency spectrum image, a mutual information feature vector of the multi-modal feature vector is calculated, and subtle changes of cooperative work among different physical attributes in the nodes are captured; on the basis, each pixel node is further regarded as a node in a pixel node grid, the connection weight is quantized by calculating the norm of a mutual information feature vector of the pixel node and obtaining the similarity between adjacent nodes, then the attenuation rate of the connection weight of a continuous time window is calculated, the distribution entropy value of the attenuation rate is analyzed, and a stability degradation entropy index is obtained; trivial connection change information is condensed into a quantitative index representing the overall orderliness decline degree of the system, then time sequence prediction is carried out on an entropy index historical sequence, the expected time of the entropy index historical sequence reaching the dynamic control upper limit is calculated, and the crossing from current state monitoring to future trend prediction is achieved.

Disclosed are valve replacement devices, systems, and methods. Valve replacement devices may comprise one- or two-piece systems comprising a receiver body (also called an adapter) and a valve assembly with replacement leaflets attached to and located within the receiver body. In two-piece systems, the valve assembly may be removable from the receiver body such that both can be delivered together or separately, and the receiver body may remain implanted while the valve assembly may be removed and replaced. Also described are devices, systems, and methods related to delivering, removing, and replacing a valve replacement. Such delivery methods may include transseptal insertion of a new minimum leaflet structure, and securement of the valve replacement using several securement type (e.g., supra-annular, sub-annular, radial, leaflet securement, etc.). Also described is a braided helical design that mimics the heart's natural movement, and a flange structure for assisting the functioning of the valve replacement.

A method for predicting a cardiac index (CI) for a patient includes receiving a set of a measurements from the patient that characterizes an anatomical condition, a physical condition, a physiological condition, or a combination thereof. The method also includes receiving a non-invasive physiological measurement from the patient. The method also includes performing feature extraction on the non-invasive physiological measurement to produce one or more extracted features. The method also includes combining the set of measurements and the extracted features to produce a dataframe. The method also includes predicting the CI for the patient. The CI is predicted using a prediction model based upon the dataframe.

Apparatus and methods are described including an impeller (50) that includes a proximal bushing (64) and a distal bushing (58). Two or more helical elongate elements (52) extend from the proximal bushing (64) to the distal bushing (58), and an axial structure (54) is disposed inside of the two or more helical elongate elements (52), and along an axis around which the helical elongate elements (52) wind. The impeller (50) includes an impeller-overexpansion-prevention element (72). The impeller-overexpansion-prevention element is a single integrated structure that includes a ring (73) disposed around the axial structure (54), and a plurality of elongate elements (67) each of the elongate elements (67) extending from the ring to a respective helical elongate element (52) and being coupled to the respective helical elongate element (52) so as to prevent radial expansion of the impeller (50). Other applications are also described.

The invention relates to a simulation-based target removal area determination method and device. The method comprises the following steps: acquiring a heart simulation model and corresponding blood flow field information; according to the left heart lumen model and the blood flow field information, determining a first target area in the ventricular septum model; stress calculation is carried out according to the ventricular septum model, and a second target area in the ventricular septum model is determined; determining an initial target area according to the first target area and the second target area; according to the ventricular septum model, determining the thickness range of the initial target area at the end of contraction; determining a target cutting thickness according to a preset basic thickness and a thickness range; according to the heart simulation model, the initial target area, the target resection thickness and the blood flow field information, a first target resection area is determined. By adopting the method, the cardiac muscle tissue area needing to be cut off can be accurately determined.

The invention discloses an abnormal heart beat detection method and system based on a magnetic cardiogram, and relates to the technical field of medical data processing, and the method comprises the steps: carrying out the preprocessing of original magnetic cardiogram data; magnetic cardiogram features are extracted based on discrete wavelet transform, model parameters are adjusted, and an abnormal heart beat detection model is obtained; and judging whether abnormal heart beat exists or not and the position. According to the method, the problem that the magnetocardiogram data lacks a large number of heart beat level labeling samples is effectively solved, dependence on labeling data is reduced, automatic, efficient and accurate detection of abnormal heart beats is achieved, the good clinical application prospect is achieved, and powerful support can be provided for early screening, auxiliary diagnosis and risk assessment of arrhythmia.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

1. Name of the product in this design: Wearable Dynamic Electrocardiogram Recorder (ECG-D01H02). 2. Purpose of this design: It is a wearable dynamic electrocardiogram recorder used to monitor and record the electrocardiogram waveform of a human body. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention relates to the technical field of data processing, in particular to a multichannel medicine collaborative intelligent infusion system for the department of cardiology. The system determines the clinical similarity according to the clinical feature index of the current object and the clinical feature index of each historical object; determining the historical cases with the clinical similarity exceeding a first threshold value as a reference historical case set; aiming at each historical case in the reference historical case set; according to the real-time physiological monitoring data in the current time period, the physiological monitoring data of the historical cases in the historical similar time period, and the fluctuation difference between the physiological monitoring data of the historical cases in the historical similar time period and the physiological monitoring data of the historical cases in the next adjacent historical time period; according to the difference between the real-time infusion parameter of the current time period and the infusion parameter of the historical case in the historical similar time period, determining an adjustment weight; weighted fusion is carried out on infusion parameters in historical similar time periods by adjusting weights, target infusion parameters are determined, and therefore it is ensured that the target infusion parameters are fused with the quintessence of historical optimal cases.

The invention discloses an anesthesia awakening state prediction method and system, and the method comprises the steps: firstly constructing a standard prediction model, then obtaining an actual physiological parameter set, generating an actual recovery state parameter set according to the actual physiological parameter set, and generating an initial recovery curve in the standard prediction model; generating a minimum verification circle, and performing primary correction on the initial recovery curve through the minimum verification circle to obtain a corrected recovery curve; according to the correction recovery curve and a standard prediction model, initial prediction waking time is calculated, according to the actual recovery state parameter set and the correction recovery curve, a composite deviation rate is generated, according to the composite deviation rate, secondary correction is carried out on the initial prediction waking time, and prediction waking time is obtained. According to the method, accurate positioning of the wake-up point is realized by correcting the intersection point of the recovery curve and the standard prediction model, and early warning is performed in time, so that the work pressure of medical staff is reduced, the orderly development of medical work can be ensured, and the nursing quality and the operation efficiency of a wake-up ward can be improved.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

A method and system for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, or parameters proportional thereto, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). This is used, preferably in combination with at least one further physiological parameter, for example heart rate, to derive one or more hemodynamic parameters. A transfer function or machine learning model is used to process the inputs to obtain the estimated hemodynamic parameters.

This disclosure relates to a catheter system that for precisely positioning fluid flow restrictors within a blood vessel. In particular, this disclosure provides a catheter system that includes a sheath with a first fluid flow restrictor mounted thereto, and a catheter that is slidably disposed within the sheath and has a second fluid flow restrictor mounted thereto. Inside a blood vessel, the sheath and the catheter are separately slidable, thereby allowing the first and second fluid flow restrictors to be positioned at precise locations within the blood vessel.

This disclosure relates to electro-anatomical mappings of cardiac tissue at different depths using a multi-electrode catheter. A multi-electrode catheter within a heart of a patient has a first plurality of electrodes on a first side and a second plurality of electrodes on a second side, wherein the first side is proximate to cardiac tissue at a first location and the second side is facing away from the cardiac tissue. Electrical activity is received from at least one electrode on the first side and the at least one electrode on the second side, and a signal analysis is performed thereon based at least in part on distances between electrodes on different sides of the multi-electrode catheter. The process is repeated for further locations of cardiac tissue in the heart, and an electro-anatomical map of the heart depicting electrical activity or scar tissue is generated based on the signal analysis.

The invention discloses an intracranial pressure intelligent monitoring system, which relates to the technical field of intracranial pressure monitoring, and comprises a continuous pressure conduction chain construction module for establishing a continuous pressure conduction chain of an intracranial pressure signal, carrying out time sequence segmentation analysis on the intracranial pressure signal acquired in real time, and obtaining a continuous pressure conduction chain of the intracranial pressure signal; identifying a section with lagging and smooth abnormity in the pressure feedback curve, and determining the section as a nonlinear response positioning range; and the elastic pressure buffer window adjusting module introduces an elastic pressure buffer window in the nonlinear response positioning range and adaptively adjusts the width of the elastic pressure buffer window according to the pressure change rate of the adjacent time slices. By constructing a continuous pressure conduction chain and introducing an elastic buffering and layering rearrangement mechanism, it is ensured that an intracranial pressure signal accurately reflects true pressure changes in the abnormal stage. Through dynamic weight band and rhythmicity balance regulation and control, self-adaptive stability of signal energy and a time structure is achieved, and continuity and early warning accuracy of intracranial pressure monitoring are improved.

The invention relates to the technical field of medical health monitoring, in particular to a coronary heart disease monitoring method and system based on millimeter wave radar. Comprising the following steps: acquiring I / Q signals of a human chest area by using a millimeter-wave radar health monitor to obtain a clinical data set; processing all data in the clinical data set to extract a heartbeat time domain sequence; designing a RadarVG network model fusing VAE and AnGAN and combining a self-attention mechanism and a spectrum normalization technology, and training the RadarVG network model based on the preprocessed clinical data set; inputting an actual heartbeat signal into the trained RadarVG network model to obtain an LSAD score as a core anomaly index, and calculating a judgment threshold; and if the abnormal score exceeds a judgment threshold, calculating a coronary heart disease risk score, and when the risk score reaches an early warning threshold, generating early warning information. Non-contact and high-precision early coronary heart disease monitoring is achieved, meanwhile, long-term monitoring can be conducted, the false alarm rate is low, the system is suitable for family scenes, and technical support is provided for prevention of cardiovascular diseases.

A system for application of neurostimulation includes an outer sheath, an elongate inner member in the outer sheath and movable relative to the outer sheath. The inner lumen has a distal end. An expandable member is coupled to the distal end of the inner member and is in the outer sheath. The expandable member is self-expanding upon from a compressed state in the outer sheath to an expanded state out of the outer sheath. The expandable member includes a distal portion including a plurality of wires woven together and a proximal portion including the plurality of wires extending parallel to a longitudinal axis. The system includes a plurality of electrode assemblies outward of the expandable member and circumferentially spaced around the expandable member. Each electrode assembly is coupled to two of the wires extending parallel to the longitudinal axis. Each electrode assembly includes a plurality of longitudinally-spaced electrodes.

The invention discloses a multi-modal contrast learning fault diagnosis method for a small sample scene, and the method comprises the following steps: carrying out the enhancement of a one-dimensional signal and two-dimensional image fused fault data set through physical simulation for the small sample scene with scarce industrial fault data, and constructing a positive and negative sample pair through a plurality of data enhancement strategies; based on heterogeneous multi-modal fault data, designing a double-flow encoder architecture of a time sequence branch and an image branch, extracting depth features and mapping the depth features to a unified feature space through a projection head; performing supervised contrast learning pre-training based on intra-modal and inter-modal dual contrast loss; supervision fine tuning is carried out based on multiple loss functions such as physical guidance, so that accurate diagnosis of equipment faults is realized in a small sample scene. According to the fault diagnosis method under the unbalanced sample and limited labeling conditions, the problem that a traditional data driving model depends on large-scale labeling samples is effectively relieved through supervised comparative learning and cross-modal information alignment.

The invention discloses a prediction method and a prediction system for fracture risk of an osteoporosis patient. The method comprises the following steps: firstly, collecting a plurality of parameters such as age, bone mineral density, vitamin D level and the like of the patient; constructing a bone time-varying factor and dynamic prediction model, and determining the influence weight of each parameter on the bone condition at different time nodes; then, a three-dimensional mechanical model is constructed through mechanical simulation of the skeleton microstructure, stress distribution, strain states and structural stability of the skeleton under different loads are simulated, and the model is corrected by fusing parameters to influence weights; then the fracture probability in a future preset time period is calculated; and finally, a result is presented through a nomogram visualization model. The system realizes the above method, can predict the fracture risk more accurately, and provides powerful support for clinical diagnosis and treatment.

The invention relates to the technical field of radiotherapy dose analysis, in particular to a radioactive cardiotoxicity prediction system based on multi-modal dynamic fusion and a quantitative evaluation method. According to the system, medical images, radiotherapy plans and clinical parameters are acquired through the multi-modal data acquisition module; the image preprocessing and three-dimensional reconstruction module processes the image data and reconstructs a heart model containing a substructure; the dynamic dose module is combined with the four-dimensional respiratory gating image and the dose matrix to generate time sequence dose distribution; the feature fusion and prediction module extracts multi-modal features, performs calculation by using a graph convolutional network, a long-short term memory network and an attention mechanism, and outputs a cardiotoxicity risk quantitative index; and finally generating a heart substructure level risk thermodynamic diagram and a structured report. According to the method, dynamic dose modeling, heart substructure level refined analysis, multi-source information fusion and individualized prediction are realized, and the accuracy, interpretability and clinical auxiliary decision-making value of cardiac toxicity risk assessment are remarkably improved.

The invention relates to the technical field of breath monitoring, in particular to a big data intelligent health breath dynamic monitoring method and system and a terminal. The method comprises the following steps: acquiring a time domain interval of each respiratory cycle according to change characteristics of a respiratory pressure signal; further obtaining work doing amount corresponding to each breathing cycle, and dividing breathing modes; the newest seasonal period of the respiratory pressure signal is further extracted, and a respiratory stability coefficient is obtained according to the change characteristics of work doing amounts of respiratory periods with adjacent time sequences in the seasonal period in combination with the number distribution characteristics of the respiratory periods of the respiratory mode; furthermore, when the breathing stability coefficient is larger than or equal to the preset stability threshold value, the predicted breathing pressure signal is obtained and verified, and finally the predicted breathing mode is obtained according to the final predicted breathing pressure signal, so that the hysteresis of real-time processing is reduced, the response speed of breathing support is increased, and the comfort is improved.

An expandable framework for use in a replacement heart valve implant includes a plurality of struts defining a lattice structure with an inflow end, an outflow end, and a plurality of stabilization arches extending downstream from the outflow end. The lattice structure includes a first circumferential row of cells defining a plurality of lower crowns at the inflow end and a second circumferential row of cells defining a plurality of upper crowns at the outflow end, where the cells of the first and second circumferential rows each have a lower point and an upper point. The lower point of at least some of the cells in the second circumferential row is a free point devoid of attachment to any other strut and extends radially outward beyond a radially outer surface of any strut immediately below in an axial direction.

The invention discloses a cerebral hemorrhage risk prediction method and system based on multi-modal learning, and relates to the technical field of medical detection. The cerebral hemorrhage risk prediction method and system based on multi-modal learning comprises the following steps: S1, collecting craniocerebral multi-modal data, and preprocessing the craniocerebral multi-modal data; s2, identifying a high-gray-scale suspicious region, quantifying bleeding features of the corresponding region, and marking a suspected hematoma region; s3, the cerebral hemorrhage risk degree of the suspected hematoma area is evaluated, the risk level is judged based on the evaluation result, and a hierarchical regulation and control strategy is generated; and S4, based on the cerebral hemorrhage risk assessment results of the current assessment period and the historical assessment period, performing quantitative assessment on the cerebral hemorrhage risk level fluctuation state, and dynamically updating the risk threshold. The problems that in early cerebral hemorrhage recognition in the prior art, tiny hemorrhage lesions are difficult to find, risk dynamic evaluation cannot be achieved, and therefore clinical judgment and intervention opportunities are affected are solved.