34 results about "Sudden cardiac death" patented technology

Computer-implemented neural network techniques for predicting patient-specific arrhythmic sudden cardiac death survival are presented. The techniques can include obtaining cardiac image data for a patient; obtaining cardiac covariate data for the patient; providing the cardiac image data to a first subnetwork; providing the cardiac covariate data to a second subnetwork; combining an output from the first subnetwork with an output from the second subnetwork to produce survival probability data; and outputting patient-specific arrhythmic sudden cardiac death survival prediction data based on the survival probability data.

The invention provides a sudden cardiac death high-risk group multi-modal data intelligent analysis and prediction method and system, and relates to the technical field of medical health monitoring, and the method comprises the steps: collecting multi-modal data, projecting an electrocardiogram to a three-dimensional coordinate system, extracting potential trajectory features, and calculating a space-time discrete index; performing dynamic response analysis on the blood pressure data, and matching with a space-time discrete index to calculate a cardiovascular coupling coefficient; and performing segmented correction according to the motion state data, monitoring a coupling coefficient deviation condition in real time, calculating a risk degree and generating an early warning. According to the method, heart abnormity can be found in advance, and sudden cardiac death can be effectively prevented.

The invention relates to a sudden cardiac death risk prediction method and device based on cardiovascular indexes and a medium. According to the method, the machine learning model can be trained and formed based on the basic feature set formed by the resting data of the heart rate of the sample human body in the resting state and the stress data of the heart rate in the stress state, and sudden cardiac death risk probability prediction is realized through feature weighting by using the machine learning model. The sudden cardiac death risk score of the individual can be obtained only by collecting the resting heart rate data of the individual in the resting state and the heart rate dynamic change data in the induced stress test process, experience deviation is avoided, and the prediction accuracy of sudden cardiac death is improved.

The invention relates to a method for the magnetocardiographic characterization of at least a portion of a heartbeat cycle of a heart of a subject. Magnetocardiographic parameter obtained by the method can, for example, be used for the prediction of a risk for sudden cardiac death (SCD) in a subject.

The invention relates to a method for the magnetocardiographic characterization of at least a portion of a heartbeat cycle of a heart of a subject. Magnetocardiographic parameter obtained by the method can, for example, be used for the prediction of a risk for sudden cardiac death, SCD, in a subject.

The invention discloses a method for constructing a cardiac resuscitation visualization process. The method comprises the following steps: collecting multi-source data; constructing an arrhythmia automatic identification model based on deep learning; judging the vital sign state of the patient; analyzing by using a machine learning algorithm to evaluate the severity of the illness state; through the Internet of Things technology, real-time positioning and state monitoring are carried out on first-aid resources in a hospital; an optimal first-aid resource allocation scheme is automatically calculated; analyzing the relationship between each factor and the cured patient to obtain key factors and potential problem points of the treatment success rate; a personalized cardiac resuscitation advanced life support process is generated based on an artificial intelligence algorithm and is visually displayed, and first-aid operation steps and time node guidance are provided for first-aid personnel. According to the method, the whole process of cardiac resuscitation is realized by fully utilizing an artificial intelligence technology, and intelligence and visualization of each link are realized, so that the treatment timeliness and success rate of sudden cardiac death patients are remarkably improved.

This invention discloses a method for predicting sudden cardiac death based on forensic autopsy data using machine learning and its forensic applications, belonging to the fields of machine learning, statistics, and forensic identification. This invention provides a method for screening independent predictors of forensic diagnosis of sudden cardiac death using LASSO regression and logistic regression. Using 10-fold cross-validation, the method selects the minimum lambda(λ) to identify 14 risk factors. Logistic regression ultimately identifies 9 independent predictors, including age, heart weight, left ventricular wall thickness, right ventricular wall thickness, interventricular septum thickness, aortic valve circumference, mitral valve circumference, liver weight, and left kidney weight. A nomogram and a web-based calculator are constructed for predicting sudden cardiac death in forensic practice, thereby determining sudden cardiac death using objective indicators. This method has not been reported worldwide.

The invention relates to the technical field of medical big data, in particular to a method for establishing an HCM patient endpoint event prediction model based on LGE radiomics features and clinical prediction features, and a method and device for predicting SCD risks based on the HCM patient endpoint event prediction model. According to the method for establishing the model, LGE radiomics and clinical features are innovatively combined, and a plurality of radiomics-clinical feature combined prediction models corresponding to each of a cardiovascular death patient group, a heart failure inpatient group and a secondary endpoint event patient group are established by adopting a plurality of machine learning methods; and then, screening out the optimal radiomics-clinical feature combined prediction model of each patient group to serve as the HCM patient endpoint event prediction model of each patient group. The present disclosure enables the prediction of various cardiovascular endpoint events for HCM patients, not limited to sudden cardiac death, and exhibiting good predictive efficacy in various predictions.

This application discloses a method, device, storage medium, and equipment for predicting the risk of sudden cardiac death due to arrhythmia, relating to the field of biomedical technology. The method includes: acquiring multi-channel cardiac magnetic signals from a subject; performing fidelity enhancement processing on the multi-channel cardiac magnetic signals to extract core features characterizing the cardiac magnetic field; constructing a topological geometric model of the cardiac fiber plexus; extracting topological invariants of the cardiac electrical conduction system based on the core features and the topological geometric model; conducting non-equilibrium phase transition dynamics detection on the topological geometric model of the cardiac fiber plexus to obtain phase transition features reflecting the stability of the cardiac electrical conduction system; inputting the topological invariants and phase transition features into a preset topological neural network to complete a quantitative analysis of the degree of chaos in the cardiac electrical conduction system; and generating a risk score and early warning indicators for sudden cardiac death due to arrhythmia based on the quantitative analysis results. This application can achieve accurate early warning of the risk of sudden cardiac death due to arrhythmia.

The invention discloses a sudden cardiac death risk prediction method, device and system and a storage medium, and the method comprises the steps: obtaining resting electrocardio data outputted through a plurality of electrocardiogram leads, processing the resting electrocardio data to obtain high-frequency QRS wave group data corresponding to each electrocardiogram lead, and obtaining high-frequency QRS features corresponding to each electrocardiogram lead; determining a lead positive index corresponding to each electrocardiogram lead according to the high-frequency form index corresponding to each electrocardiogram lead; determining whether there is a sudden cardiac death risk based on at least one of a plurality of conditions; and under the condition of determining that the sudden cardiac death risk exists, predicting the sudden cardiac death risk level according to the sum value of the high-frequency form indexes corresponding to the electrocardiogram leads. According to the scheme provided by the embodiment of the invention, the sudden death risk is quantified based on a plurality of (greater than or equal to 12) lead high-frequency form indexes and values reflecting the myocardial injury volume under the qualitative condition of a plurality of risk factors, and the missed diagnosis rate and accuracy can be taken into account.

According to an aspect of the present invention, there is provided computer-implemented method for forecasting near-term sudden cardiovascular events, comprising: pretraining a large language model transformer architecture using a processor with an associated computer memory device to recognize text associated with sudden cardiovascular events cases reporting sudden cardiovascular events following a medical exam appointment; obtaining relevant data regarding an asymptomatic individual comprising one or more of a coronary artery calcium (CAC) scan, a coronary CT angiography (CCTA), chest CT, blood markers, and electrocardiogram; providing the relevant data of the asymptomatic individual to the trained computer-implemented artificial neural network; receiving a forecast of the chance of near-term sudden cardiovascular events in the asymptomatic individual; and recommending the next diagnostic or therapeutic step for the asymptomatic individual.

The application provides a method and device for generating a cardiac limit survival view, comprising: constructing a cardiac simulation model based on static clinical data; inputting basic physiological state data into a generative adversarial network to obtain a stress condition matrix; simulating based on the stress condition matrix and the cardiac simulation model to generate a simulation result; determining a limit instability evaluation result based on the simulation result, if the limit instability evaluation result indicates reaching a limit instability state, recording the current stress condition matrix as a critical condition matrix, and generating a new stress condition matrix based on the critical condition matrix; if the limit instability evaluation result indicates not reaching the limit instability state, updating the generative adversarial network based on the simulation result and the stress condition matrix; after meeting a preset iteration termination condition, generating a cardiac limit survival view of a target object based on multiple critical condition matrices. The application changes the traditional passive recording of abnormal events into active exploration, effectively reducing the misdiagnosis rate of malignant events such as sudden cardiac death.

This disclosure relates to the field of medical big data technology, specifically to a method for establishing a prediction model of endpoint events in HCM patients based on LGE radiomics features and clinical predictive features, a method for predicting SCD risk based on the HCM patient endpoint event prediction model, and an apparatus. The method for establishing the model innovatively combines LGE radiomics with clinical features and employs multiple machine learning methods to establish multiple radiomics-clinical feature joint prediction models corresponding to each patient group in the cardiovascular death patient group, heart failure hospitalized patient group, and secondary endpoint event patient group. Then, the optimal radiomics-clinical feature joint prediction model for each patient group is selected as the HCM patient endpoint event prediction model for that patient group. This disclosure achieves the prediction of multiple cardiovascular endpoint events in HCM patients, not limited to sudden cardiac death, and demonstrates good predictive efficacy in various predictions.

The invention relates to the field of medicine, and provides a sudden cardiac death diagnosis model and a construction method thereof, and the method comprises the steps: collecting data samples, and constructing a training set based on the data samples, the training set comprises diagnosis features, and the diagnosis features represent that the data samples in the training set are sudden cardiac death; performing data interpolation and variable screening on the diagnosis features in the training set; a logistic regression model is constructed based on the training set, independent variables of the logistic regression model are diagnosis features, and dependent variables of the logistic regression model are diagnosis results of samples corresponding to the diagnosis features; and constructing a diagnosis calculator based on the logistic regression model, wherein the diagnosis calculator inputs the received characteristic values into the logistic regression model to diagnose sudden cardiac death. The sudden cardiac death diagnosis method and device are used for overcoming the defect that a diagnosis result is not accurate enough mainly depending on subjective experience of a forensic when sudden cardiac death diagnosis is carried out at present, and the sudden cardiac death diagnosis method and device can carry out accurate diagnosis on sudden cardiac death.

The present invention relates to the field of ultrasounds and imagining of the coronary blood flow of the heart. Patients with coronary microvascular dysfunction (CMD) have poor prognostic with significantly higher rates of cardiovascular events, including hospitalization for heart failure, sudden cardiac death, and myocardial infarction (MI). Despite the urgent clinical need, there are no non-ionizing and non-invasive techniques available in clinic to directly visualize the coronary microvasculature and assess the local coronary microvascular system. Flow imaging remains a difficult task to perform in the heart because of the fast movements of this organ. In order to overcome the limitations of actual imaging methods for the coronary blood flow, the inventors proposed an ultrasound ultrafast imaging method that automatically detect the time periods in which the myocardium velocity is low and estimate the coronary flow velocity and the tissue velocity from the same data acquisition.

The invention belongs to the technical field of biological medicines, and particularly discloses application of colchicine in preparation of a medicine for treating hypertrophic cardiomyopathy. Researches show that after colchicine is used for treatment, pathological changes such as cardiac fibrosis of hypertrophic cardiomyopathy are relieved. Further experimental results show that after colchicine treatment, the proportion of pro-inflammatory macrophages in the cardiac hypertrophy tissue is reduced, the IL-6 level is reduced, and it is prompted that the anti-inflammatory ability of colchicine can improve the microenvironment of the cardiac hypertrophy tissue, so that fibrosis is relieved, and the cardiac function is improved. The invention proves the clinical transformation potential of colchicine in the treatment of cardiac hypertrophy patients. If the medicine is applied clinically in the future, the risk of sudden cardiac death is expected to be reduced by relieving cardiac fibrosis, and a new treatment choice is provided for improving the prognosis of a hypertrophic cardiomyopathy patient.

The present invention relates to autoantibodies targeting the human NaV1.5 sodium channel and related isoforms as biomarkers for risk prediction of cardiac arrhythmias and sudden cardiac death in cancer patients, related methods and diagnostic kit for their detection.

The invention relates to the technical field of pharmaceutical preparations, in particular to a traditional Chinese medicine composition for invigorating qi, blood, yin and yang and a preparation method and application thereof. The traditional Chinese medicine composition comprises the following raw materials: tortoise-shell glue, deer-horn glue, donkey-hide gelatin, ginseng, astragalus membranaceus, codonopsis pilosula and fructus lycii, and is prepared by fermenting the raw materials, and the mass ratio of the tortoise-shell glue to the deer-horn glue to the donkey-hide gelatin to the ginseng to the astragalus membranaceus to the codonopsis pilosula to the fructus lycii is 1: (0.5-2): (0.5-2): (1-4): (2-5): (2-6): (2-6). The fermented product obtained by fermenting the medicine raw materials through probiotics can be used for treating qi-blood yin-yang deficiency syndromes such as fatigue, myocardial ischemia, cerebral hypoxia and kidney essence deficiency and / or treating and preventing leukopenia caused by myelosuppression and / or DNA (deoxyribonucleic acid) reduction after radiation and / or hypoimmunity and plateau hypoxia. The effects of preventing sudden cardiac death and the like are obvious.

Disclosed are a sudden cardiac death risk prediction method, device, system and storage medium. The method comprises: acquiring resting electrocardiogram data output through a plurality of electrocardiogram leads, processing the resting electrocardiogram data to obtain high-frequency QRS complex data corresponding to each electrocardiogram lead, and obtaining high-frequency QRS features corresponding to each electrocardiogram lead according to the high-frequency QRS complex data; determining a lead positive index corresponding to each electrocardiogram lead according to a high-frequency morphology index corresponding to each electrocardiogram lead; determining whether there is a sudden cardiac death risk based on at least one of a plurality of conditions; and in the case of determining that there is a sudden cardiac death risk, predicting a sudden cardiac death risk level according to a sum of the high-frequency morphology indexes corresponding to each electrocardiogram lead. The scheme provided in the embodiments of the present application can quantitatively predict the sudden death risk based on the sum of the high-frequency morphology indexes of a plurality of (greater than or equal to 12) leads reflecting the myocardial injury volume in the case of qualitative determination based on a plurality of risk factors, and can take into account the misdiagnosis rate and accuracy.

This invention provides a safer drug and its application, and a safer drug combination and its application without sudden cardiac death toxicity. The safer drug provided by this invention is a combination preparation containing a serotonin receptor antagonist and diltiazem, which effectively avoids the possibility of ventricular depolarization and repolarization disorders caused by the serotonin receptor antagonist ondansetron, thereby preventing corresponding arrhythmias and sudden cardiac death. This application also provides the application of diltiazem in the preparation of drugs that improve cardiac electrical activity and corresponding treatment methods.

The invention belongs to the technical field of medical data analysis, and particularly relates to a new atrial fibrillation risk prediction method and system for an ICD implantation patient, and the method comprises the steps: data acquisition: collecting clinical characteristic parameters of the ICD implantation patient, including gender, left atrium inner diameter, diabetes mellitus indexes and pulmonary artery systolic pressure; data preprocessing: carrying out binarization and standardization processing and classification variable coding; model construction: based on a multi-factor statistical analysis model, establishing a mathematical association equation of the clinical characteristic parameters and the new atrial fibrillation risk; risk calculation: calculating the risk probability of new onset atrial fibrillation of the individual according to the equation, and outputting a 0-1 risk value; and outputting a result, and generating a visual risk assessment result. According to the method, a multi-factor model is constructed to predict the risk of new atrial fibrillation after ICD implantation of sudden cardiac death high-risk population, AUC = 0.793 (95% CI is 0.705-0.855), and the method is a special prediction tool for ICD implantation population.

The invention provides a probe composition for detecting hereditary sudden cardiac death and application of the probe composition. The probe composition for detecting the hereditary sudden cardiac death comprises 20388 probes which are consistent with or complementary to the sequences of corresponding sites on a chromosome of a human reference genome hg19. The probe composition for detecting the hereditary sudden cardiac death can be used for detecting the coding regions of 99 genes and the non-coding regions of 29 genes related to the hereditary sudden cardiac death at one time, so that the detection of various mutation types of the hereditary sudden cardiac death, such as cardiac ion channel disease, cardiomyopathy and hereditary aorta disease, is realized; the detection content coverage is wide, and the detection efficiency is high, so that the occurrence and genetic risk of the hereditary sudden cardiac death can be comprehensively evaluated, and the method has important significance on early prevention, early intervention and auxiliary diagnosis.

The invention provides application of circulating protein FGF23 in predicting sudden cardiac death risk of chronic kidney disease patients and model construction, and belongs to the technical field of medicines. The biomarkers comprise NTproBNP (N-terminal B-type natriuretic peptide), DTNB (heteronectin beta), ANGPT2 (angiopoietin 2), FGF23 (fibroblast growth factor 23) and SEPTIN8 (cytokinin 8), wherein the circulating protein FGF23 has the best prediction effect after the risk ratio and AUC evaluation are combined. The FGF23 marker is provided for early prediction of sudden cardiac death after chronic kidney disease, and a technical means is provided for prognosis evaluation of chronic kidney disease treatment.

The present application relates to the technical field of disease risk prediction, and particularly relates to a ten-year risk prediction method, system and device for sudden cardiac death, the method comprising: obtaining 48-hour electrocardiogram data of an individual sample and a risk classification evaluation result based on a China-PAR risk assessment model; splitting and combining the electrocardiogram data to obtain a preset multi-dimensional waveform graph; constructing a convolutional neural network model, taking the waveform graph as input and the corresponding risk classification evaluation result as output to train the model and obtain a sudden cardiac death risk prediction model; inputting the processed waveform graph of 48-hour electrocardiogram data of a subject into the prediction model, and outputting the result as a ten-year sudden cardiac death risk prediction level result. The present application can create a non-linear, high-precision, and iterative sudden cardiac death risk deep learning model for the general population and doctors, improve the detection rate of sudden cardiac death, and reduce the recurrence rate and mortality.

The invention relates to an SERS (Surface Enhanced Raman Scattering) component for capturing markers in blood fluid by utilizing antigen-antibody reaction and a preparation method of the SERS component. The preparation method comprises the following steps: preparing a PDA (Personal Digital Assistant) chip aiming at a target marker, preparing a micro-fluidic chip and assembling the SERS assembly. The obtained SERS assembly can be applied to sudden cardiac death early screening. The manufacturing cost and the process difficulty of the micro-fluidic chip can be reduced, and the micro-fluidic chip can be used in cooperation with a portable Raman spectrometer for detection and can be thrown for use.

The application relates to the medical field and provides a diagnosis model for cardiogenic sudden death and a construction method thereof, wherein the method comprises the following steps: collecting data samples, and constructing a training set based on the data samples, wherein the training set comprises diagnosis features, and the diagnosis features represent the data samples in the training set as cardiogenic sudden death; data interpolation and variable screening are performed on the diagnosis features in the training set; a logistic regression model is constructed based on the training set, wherein independent variables of the logistic regression model are the diagnosis features, and dependent variables of the logistic regression model are diagnosis results of samples corresponding to the diagnosis features; and a diagnosis calculator is constructed based on the logistic regression model, wherein the diagnosis calculator inputs received feature values into the logistic regression model to diagnose cardiogenic sudden death. The application can accurately diagnose cardiogenic sudden death, and solves the problem that the diagnosis result of cardiogenic sudden death is mainly dependent on the subjective experience of a forensic doctor, and the result is not accurate enough.

The invention provides a safer medicine and application thereof, and provides a safer medicine composition without sudden cardiac death toxicity and application thereof. The safer medicine provided by the invention is a compound preparation containing the 5-hydroxytryptamine receptor antagonist and diltiazem, and can effectively avoid the possibility of corresponding arrhythmia and sudden cardiac death caused by ventricular depolarization and repolarization disorder caused by the 5-hydroxytryptamine receptor antagonist ondansetron. The invention also provides application of diltiazem in preparation of medicines for improving cardiac electrical activity and a corresponding treatment method.

The invention relates to a jettisonable kit cooperating with a portable Raman spectrometer for detection. The jettisonable kit comprises a kit body, and the top surface of the kit body is provided with a group of SERS scanning holes, a pull rod hole and a sample adding port; a pull rod is arranged in the pull rod hole; a sample tube is arranged in the box body; a group of capturing devices are arranged in the box body, and each capturing device is in transmission connection with the pull rod through a transmission mechanism; each capturing device is provided with a vertically arranged capillary tube bundle, and the tail end of each capillary tube bundle extends into the sample tube; a group of SERS (Surface Enhanced Raman Scattering) detection substrates adopting micro-fluidic chips are arranged above the capturing device in the box body; each SERS detection substrate is positioned right below the corresponding SERS scanning hole; and a base protection device for locking the pull rod is arranged in the box body. According to the kit disclosed by the invention, the SERS technology and the microfluidic technology are utilized, and a plurality of SERS detection substrates can be used at the same time, so that a plurality of indexes of the same sample can be rapidly detected, and the kit can be applied to rapid detection of sudden cardiac death risk assessment.

The present invention relates to the field of ultrasound and imaging of coronary blood flow in the heart. Patients with coronary microvascular dysfunction (CMD) have a poor prognosis and a significantly higher incidence of cardiovascular events, including heart failure, sudden cardiac death, and myocardial infarction (MI) hospitalization. Despite the urgent clinical need, there are no non-ionizing and non-invasive techniques available clinically for directly visualizing the coronary microvasculature and assessing the local coronary microvasculature. Blood flow imaging remains a difficult task in the heart due to the rapid motion of this organ. To overcome the limitations of practical imaging methods for coronary blood flow, the inventors have proposed an ultrasound ultrafast imaging method that automatically detects time periods of low myocardial velocity and estimates coronary blood flow velocity and tissue velocity from the same data acquisition.

The invention relates to the technical field of disease risk assessment, in particular to a pet cat sudden cardiac death risk assessment method, which comprises the following steps: collecting pet cat physical examination case information; constructing a sudden cardiac death risk assessment model of the pet cat according to the collected physical examination case information of the pet cat, and screening out characteristic indexes related to sudden cardiac death of the pet cat; and obtaining detection report data of a to-be-evaluated pet cat, and inputting the detection report data into the sudden cardiac death risk evaluation model of the pet cat to obtain a sudden cardiac death risk evaluation result of the pet cat. Scientific support can be provided for early diagnosis and identification of heart diseases of pet cats, and the risk level of potential sudden cardiac death can be predicted, so that timely and effective prevention measures can be taken, the life quality of pets can be improved, and scientization and standardization of pet medical services can be promoted.