390 results about "Heart disease" patented technology

The dynamic monitoring system for the senile cardiac patient comprises a multi-mode sensor module, a data processing module, an analysis module and an alarm module. The multi-modal sensor module collects parameters to generate a multi-modal physiological signal; the data processing module generates a preprocessing signal; the analysis module performs real-time analysis on the pre-processed signal based on a preset elderly electrocardiogram characteristic model to generate a graded alarm instruction; the alarm module triggers corresponding local reminding, remote notification or first-aid linkage response according to the graded alarm instruction, wherein the first-aid linkage response automatically sends the patient position and key physiological data to a specified first-aid center through the first-aid communication module; wherein the self-adaptive adjustment of the multi-mode sensor module comprises the step of dynamically optimizing signal gain according to a skin contact state. According to the dynamic monitoring system for the senile cardiac patients, the problems that dynamic monitoring of the senile cardiac patients is poor in real-time performance, high in false alarm rate and delayed in first-aid response can be solved.

A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and / or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention belongs to the technical field of medical image recognition, and particularly discloses a myocardial infarction complication recognition method based on a heart multi-modal image, and the method comprises the steps: firstly obtaining multi-modal medical image data and corresponding diagnosis report text data; constructing a myocardial infarction complication identification model; dividing the whole network into a feature extraction stage and an image text feature matching stage by adopting a separation training thought, carrying out back propagation on the network in the feature extraction stage through L1 loss, and optimizing weight parameters of the feature extraction network; performing back propagation on the network in the image text matching stage by comparing loss, and optimizing parameters of an image and a text encoder; and finally, collecting a medical image of the heart of a patient, inputting the medical image into the trained myocardial infarction complication identification model, and generating a character diagnosis report of heart disease complication. Therefore, early automatic diagnosis of myocardial infarction complications is realized.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention discloses a multi-mode-based flexible wearable heart function monitoring method, system and device, and relates to the technical field of health monitoring, and the method comprises the steps: outputting a multi-mode signal through a flexible patch sensing module when the flexible patch sensing module is arranged in a detection area; wherein the flexible patch sensing module comprises an electrocardio sensor, a pulse wave sensor, a heart sound sensor, a strain sensor and a microfluidic biosensor. According to the invention, high-precision synchronous signal acquisition is realized through flexible patch integrated multi-modal sensing, heart multi-source signal features are adaptively extracted in combination with a multi-scale convolutional neural model, and timing sequence relevance is captured by using a bidirectional long-short-term memory network, so that the accuracy and reliability of heart disease diagnosis are remarkably improved, and the accuracy and reliability of heart disease diagnosis are improved. Meanwhile, non-invasive and continuous monitoring and early pathological screening are supported.

This invention is eyewear (e.g. eyeglasses) with electrodes (e.g. EEG sensors) which collect brain activity data (e.g. electroencephalographic data) which can be used to predict and / or detect health events (e.g. epileptic seizure, stroke, or heart attack) or function as a BCI (Brain-to-Computer Interface) for communication. There can be anterior and posterior electrodes on the eyewear temple and the temple can have an upward and / or inward curving wave or arm. A posterior electrode can have multiple electroconductive protrusions to help penetrate between strands of hair.

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.

This document provides devices and methods for the treatment of heart conditions, hypertension, and other medical disorders. For example, this document provides devices and methods for treating atrial fibrillation by performing thoracic vein ablation and / or electroporation procedures, including pulmonary vein myocardium ablation. In some embodiments, the ablation and / or electroporation is delivered in differing areas in and / or around the pulmonary vein in coordination with each other for enhanced efficacy.

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Systems and methods and devices are provided for treating conditions such as heart failure and / or pulmonary hypertension by at least partially occluding flow through the superior vena cava for an interval spanning multiple cardiac cycles. A catheter with an occlusion device is provided along with a controller that actuates a drive mechanism to provide at least partial occlusion of the patient's superior vena cava, which reduces cardiac filling pressures, and induces a favorable shift in the patient's Frank-Starling curve towards healthy heart functionality and improved cardiac performance. The occlusion device may include a lumen obstructed by a relief valve that may permit fluid flow through the occlusion device to release an excessive build-up of pressure.

The invention discloses a hierarchical OvO-SVM nondestructive testing method for the attack degree of potato blackheart disease, which comprises the following steps: preparing a detection sample, and collecting a VIS / NIR original transmission spectrum; preprocessing the collected original spectrum; adopting a random forest algorithm to screen sensitive wavebands; a two-stage hierarchical classification framework is constructed, health / disease coarse classification-disease degree subdivision is carried out, cross validation type Platt calibration is carried out on decision scores of each group of SVMs, and corresponding probabilities are output; according to the method, a classification threshold value is introduced, the final grade of the morbidity degree is judged through the maximum value of a pseudo probability / threshold value, and through the technical path of spectrum preprocessing, sensitive band screening, two-stage hierarchical modeling, probability calibration and classification threshold value shaping, hierarchical modeling and OvO local boundary learning are utilized to judge the morbidity degree. According to the method, the boundary confusion problem of'health-light 'and'moderate-heavy' is remarkably relieved, the spectrum dimension is reduced by screening sensitive wavebands, and accurate grading of the attack degree of the blackheart disease is achieved.

The invention provides a training method of a CT and DSA real-time automatic fusion registration system for structural heart disease intervention and a storage medium. The training method comprises the following steps: establishing a registration system data set; establishing an initial registration system; based on the registration system data set, training an initial registration system to obtain a trained CT and DSA intraoperative registration system; and inputting the preoperative CT image data and the intraoperative real-time DSA image data of the person to be identified into the trained CT and DSA intraoperative registration system to obtain an intraoperative real-time fusion navigation image and intraoperative risk graphical prompt information. According to the method, cross-modal and cross-time-point dynamic registration can be realized, higher-precision intraoperative navigation support is provided for structural heart disease intervention, the operation safety is improved, and radiation exposure is reduced.

The invention discloses an integral forming method of a myocardial chip embedded with nanofibers and a flexible circuit board, which adopts integral forming equipment, integrates a photocuring part and an electrostatic spinning part, and realizes collaborative operation through an upper computer and a driving device; the method comprises the following steps: firstly, forming a lower runner layer by adopting a hydrogel material through a photocuring component, and embedding a flexible circuit board into the top of the lower runner layer; forming a nanofiber bracket on the flexible circuit board by adopting a spinning material through the electrostatic spinning part; a hydrogel material is printed on the nanofiber support to form an upper flow channel layer, flow channel packaging is achieved, and efficient transmission of electrical stimulation and field potential signals is achieved through a flexible circuit board. According to the method, the problems of low alignment precision, strong manual dependence and insufficient automation degree in the traditional process are solved, the chip manufacturing efficiency and consistency are improved, and a detection platform with high integration level and high biocompatibility is provided for drug screening and heart disease research.

The invention discloses a pyrimidinedione derivative as well as a preparation method and application thereof. Molecules of the pyrimidinedione derivative have better myosin inhibitory activity, also have antioxidant effects of DPPH and / or MDA and the like and anti-ferroptosis activity, and can be applied to treatment / prevention of heart myosin, anti-oxidative stress and anti-ferroptosis mechanism related diseases. The diseases or symptoms mainly comprise hypertrophic cardiomyopathy, diastolic heart failure, left ventricular hypertrophy, heart diseases with pathophysiological characteristics related to the hypertrophic cardiomyopathy, or other disease states and conditions which are obvious to those skilled in the art, or a prodrug thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt thereof. Or a hydrate or solvate thereof, and a pharmaceutical composition containing the same.

The present invention relates to a D-galactopyranose compound of formula (1)whereinthe pyranose ring is α-D-galactopyranose, and these compounds are high affinity galectin-1 and / or galectin 3 inhibitors for use in treatment of inflammation; fibrosis; scarring; keloid formation; aberrant scar formation; surgical adhesions; septic shock; cancer; metastasising cancers; autoimmune diseases, metabolic disorders; heart disease; heart failure; pathological angiogenesis; eye diseases; atherosclerosis; metabolic diseases; diabetes type I; diabetes type II; insulin resistance; Diastolic heart failure; asthma; liver disorders.

The invention discloses a multi-feature fusion heart sound classification method based on a channel attention mechanism, and belongs to the technical field of signal processing and heart disease diagnosis. The method comprises the steps that collected original heart sound records are preprocessed, a self-adaptive two-stage filtering method based on local variance and global variance is adopted, a wavelet denoising technology is combined, background noise is effectively removed, high-quality heart sound signals are extracted, and meanwhile heart sound events are accurately detected through a double-threshold segmentation method; fusing information of different dimensions to enrich the expression of the heart sound signal; a deep learning architecture fusing a convolutional recurrent neural network and a convolutional neural network is constructed, a channel attention mechanism is introduced, the fusion weight of each sub-network is dynamically adjusted according to the importance of features, and the classification accuracy is improved. According to the method, the quality of heart sound signals can be effectively improved, multi-domain features are fully fused, meanwhile, the discrimination ability of the model for heart sound categories is enhanced, and therefore the heart sound diagnosis precision is improved.

A prosthetic heart valve for the treatment of structural heart disease wherein the prosthetic heart valve includes an expandable frame that a) has an open cell geometry in the frame of the prosthetic heart valve that can be used to reduce delivery system size, b) has high radial strength, c) has improved restoration of the physiologic EOA, d) has lower recoil, e) has little or no longitudinal foreshortening, f) allows for proper placement of the bioprosthetic valve in relation to the native commissures of the valve, h) has symmetrical and cylindrical expansion of the prosthetic valve resulting in lower rates of leaflet thrombosis and structural valve deterioration, and / or i) prevents allergic response and restenosis associated with nickel content.

The invention provides an application of human source circular RNA hsacirc0083796 in preparation of a diagnostic reagent or a medicine for diagnosing and / or treating aortic dissection. The invention also provides a kit for diagnosing the aortic dissection. The invention also provides a pharmaceutical composition for treating aortic dissection. According to the application of the human source circular RNA in preparation of heart disease related products, the related heart disease related circular RNA (circled-Frizzled class receptor 3, hsacirc0083796) is one of circular RNAs with the highest expression quantity in the blood of aortic dissection patients and has high conservative property, the function of the circular RNA is closely related to heart diseases, and the circular RNA has a good application prospect in preparation of heart disease related products. The method has great significance in diagnosis and treatment of heart diseases. Meanwhile, due to the stable and specific properties of the circular RNA, compared with other types of markers, the circular RNA has larger abundance in body fluid of a patient and more obvious change, so that the circular RNA has the potential of becoming a novel diagnostic marker.

The present invention includes a method of reducing or eliminating cardiac toxic effects or cardiac pathological effects of one or more active agents comprising administering to a subject in need thereof an effective amount of one or more lipids that reduce or eliminate cardiac toxic effects of one or more active agents wherein the lipids have the formula: # imgabs0 #.

The invention relates to the technical field of health monitoring, in particular to an electrocardiograph monitoring intelligent diagnosis system based on deep learning and multi-modal fusion technology, which is characterized in that a multi-modal synchronous sensing unit synchronously acquires ECG, PCG and PPG data streams with aligned timestamps, and an edge intelligent diagnosis unit operates a lightweight model with the parameter smaller than 1MB, so that the ECG monitoring intelligent diagnosis system is obtained. The cloud fusion analysis unit is used for dynamically fusing multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF, and the dynamic collaborative decision-making unit is used when R is equal to III or Cilt, and the cloud fusion analysis unit is used for dynamically fusing the multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF; deep diagnosis, PVFgt, is triggered within 5 seconds at 0.9; and when 0.95, a third-level audible and visual alarm is triggered, and the closed-loop optimization unit continuously optimizes the model by distilling and compressing the misdiagnosis sample. According to the intelligent diagnosis system for electrocardiograph monitoring, rapid edge diagnosis is achieved through a lightweight model, critical cases can respond rapidly through cloud cooperation, and the accuracy and timeliness of heart disease diagnosis are improved.

A method of treating heart disease in a subject includes percutaneously introducing a catheter into vasculature of the subject. The catheter may include a neuromodulation element. The neuromodulation element may be positioned on a flexion element. The flexion element may deflect away from or toward a central axis defined by the catheter in response to an operator pulling or releasing a pull-wire. The neuromodulation element of the catheter can be positioned in a subclavian artery of the subject. The catheter can be used to selectively target and electrically stimulate at least one of a dorsal subclavian ansae or a ventral subclavian ansae. The method can include confirming stimulation of the dorsal subclavian ansae and / or the ventral subclavian ansae by monitoring a cardiac parameter. After confirming stimulation of the dorsal subclavian ansae and / or the ventral subclavian ansae, ablation energy can be provided to the dorsal subclavian ansae and / or the ventral subclavian ansae.

This invention discloses a cardioprotective compound composition and its preparation based on improving the absorption rate of coenzyme Q10, relating to the fields of pharmaceuticals and functional foods. The core active ingredients of this composition consist of coenzyme Q10, vitamin E, and black pepper extract, with a specific mass ratio range for each. It can also be combined with pharmaceutically acceptable carriers and antioxidant synergists to prepare various oral formulations. This invention, through the construction of a ternary active component with a specific ratio, achieves in vivo synergistic effects on absorption promotion and antioxidant regeneration, solving the technical problems of low oral bioavailability and insufficient accumulation of myocardial-targeted activity in existing coenzyme Q10 preparations. It significantly improves the conversion efficiency of coenzyme Q10 into a usable active form for cardiomyocytes and can be widely applied in the preparation of drugs and functional foods for the prevention or treatment of heart diseases.

The present application relates to the field of microbiological technology, and particularly relates to a pathogenic bacterium causing root rot symptoms of red heart disease of Astragalus membranaceus and application thereof, the pathogenic bacterium is Fusarium sp. Fusarium sp. FUS-001-2025, preserved in China General Microbiological Culture Collection Center on March 16, 2026, with the preservation number of CGMCC No. 42529; it is determined to be a new recorded strain of Fusarium sp. through morphological and molecular identification, and the bactericide sensitivity determination shows that 25% silthiofam·prochloraz has the strongest inhibition effect, 25% bromothalonil, 50% azoxystrobin and 70% methylthiophanate have significant inhibition at high concentrations. The present application provides an important pathogenic bacterium resource for the pathogenic mechanism, disease resistance evaluation and development of control agents of red heart disease of Astragalus membranaceus.

The utility model relates to the technical field of interventional medical instrument in-vitro simulation demonstration, in particular to a structural heart disease interventional medical instrument in-vitro simulation demonstration device. The device comprises a model fixing device (3), a heart model (31) and a detachable valve leaflet model (32) are fixed on the model fixing device (3), a camera (5) is further arranged on the device, and the device can be used for simulation device demonstration of heart valve intervention medical instrument in-vitro performance research, instrument operation training of an operator in vitro and operation process practice.

The present application relates to the technical field of detection of fruit core rot, and discloses a detection method capable of improving the detection accuracy of fruit core rot, comprising the following steps: forming aggregated light by two parallel weak light sources, combining the two aggregated lights, and forming a parallel light with uniform intensity, large amplitude and wide range by another concave lens after the combined light source, the parallel light is received by an optical fiber after passing through the fruit, and the fruit core rot is detected by analyzing the spectral information. The two weak parallel light sources are formed into aggregated light after passing through the lens, the use efficiency of the single weak light source is improved, each aggregated light is combined into a strong sector light source, and a uniform parallel light is formed after setting a concave lens in front of the sector light source convergence point. The parallel light has uniform intensity and wide range, and the spectral information received by the receiving end optical fiber at different positions has small difference. The overall light path has small construction range, is different from the traditional method, and the consistency and effectiveness of the light source passing through the fruit are obviously improved, and the fruit core rot detection accuracy is improved.

The invention relates to a heart disease patient health intelligent monitoring system. The system comprises a medical data acquisition module, a data preprocessing module, a feature extraction model construction module, a monitoring model construction module and a health monitoring module. According to the invention, original data is obtained through data acquisition; a data preprocessing method including space-time alignment, key event extraction, knowledge graph construction, irregular sampling processing and data set segmentation is adopted; an integrated learning model is adopted as a feature extraction model, and through multi-modal feature extraction and a dynamic weight distribution mechanism, the discrimination ability of feature expression is remarkably improved, and the limitation of processing a complex medical scene by a single model is overcome; an improved graph convolution model is adopted as a monitoring model, through dynamic construction of relation topology and injection of domain knowledge, in combination with a time decay mechanism and nonlinear monitoring activation design, time sequence dependence of health risk evolution and asymmetric cost characteristics of medical decision are captured, and the reliability of health monitoring is improved.

The invention relates to the technical field of medical instruments, and particularly discloses a heart disease patient rescue device which comprises two magnets I. The surfaces of the two magnets I are fixedly connected with supporting rods. A connecting rod is slidably mounted in the two supporting rods, a moving mechanism is arranged on the surfaces of the supporting rods, and the moving mechanism comprises that a first air cylinder is mounted in the supporting rods in an embedded mode, and the tail end of the first air cylinder is connected with the bottom of the connecting rod. According to the heart disease patient rescue device, the moving mechanism is arranged, the whole position of the device is conveniently fixed, a first air cylinder pushes a connecting rod to move upwards, the position of a pressing block is moved, the device is more suitable for being used by a patient, meanwhile, thrust of a sliding rod to a second magnet disappears, and at the moment, attraction force of the first magnet and the second magnet drives the second magnet to move upwards; the magnet II is in contact with the bed bottom surface to fix the position of the magnet I, so that the stability of the device is improved.

AI Based Medical Equipment for Heart Attack Alerting

The invention belongs to the technical field of medical information processing, and particularly discloses a regression model establishment method and system for detecting related indexes of myocardial cells of Wuzhishan pigs by using blood biochemical indexes, and the method comprises the following steps: collecting blood and heart tissue samples of the Wuzhishan pigs at different month ages, detecting the blood biochemical indexes including liver function, blood fat and blood sugar indexes, and establishing a regression model for detecting the related indexes of myocardial cells of the Wuzhishan pigs. Related indexes of myocardial cells; analyzing the index expression quantity difference, and screening significant difference indexes; then calculating the correlation between the remarkably different blood biochemical indexes and myocardial cell related indexes, and determining high-correlation blood biochemical indexes; and finally, constructing a first version of multiple regression model by respectively taking the myocardial enzyme index and the myocardial cell related index as dependent variables and the high-correlation blood biochemical index as an independent variable, removing outliers and updating the sample set on the basis of the first version of multiple regression model, and constructing a second version of multiple regression model by utilizing the new sample set, and on the basis of the second version of multiple regression model, constructing a third version of multiple regression model by adopting a stepwise regression method. According to the method, related indexes of myocardial cells can be accurately deduced through blood biochemical indexes, and support is provided for Wuzhishan pig heart disease research and model animal research and development.