49 results about "Heart disorder" patented technology

The invention relates to the technical field of electrocardiosignal analysis, and discloses a long-sequence electrocardiosignal disease recognition system based on a Transform architecture. The core defects that in traditional electrocardiogram analysis, waveform integrity is damaged by fixed window segmentation, a lead space topological relation is neglected, and long sequence modeling efficiency is low are overcome, a P-QRS-T waveform structure is completely reserved through the heart beat adaptive segmentation technology, and the fixed window truncation risk is eliminated; the lead anatomical topology and the space-time coding are fused, and multi-lead electrophysiological association is modeled; long sequence efficient processing is realized based on hierarchical sparse Transform, and the recognition sensitivity of complex pathologies such as arrhythmia and myocardial ischemia is improved; in combination with gradient directional regulation and control and a streaming processing mechanism, the clinical real-time requirement is met while the diagnosis accuracy is guaranteed, and finally, reliable, efficient and universal intelligent decision support is provided for early warning of heart diseases through lightweight deployment of an adaptive mobile terminal.

The invention discloses 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.

The invention belongs to the technical field of noninvasive hemodynamic monitoring, and provides a dynamic noninvasive cardiac output monitoring system and method. High-quality dynamic pulse wave waveform signals are collected based on a sensor array, motion artifacts in the motion state are corrected, the motion artifacts meet input conditions of a cardiac output calculation model, and dynamic pulse wave feature vectors are calculated; calculating an aorta cross-sectional area estimated value as a correction variable based on the personalized characteristic parameters; and based on a cardiac output model of deep learning training, cardiac output is calculated so as to realize real-time monitoring and evaluation of the dynamic cardiac output of the testee. According to the invention, dynamic, real-time and long-term monitoring of cardiac output parameters can be realized, intermittent correction is not needed, and the system is suitable for household rehabilitation of patients with heart diseases.

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.

Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.

Electrocardiogram-based deep learning for cardiac prediction is described. An electrocardiogram analysis module may include a data preprocessor configured to normalize an electrocardiogram to generate a standardized input for an electrocardiogram-based cardiac prediction. The electrocardiogram analysis module may further include a deep learning model including a neural network and at least one dense layer, the deep learning model trained to identify features associated with a cardiac condition that reflect underlying cardiac changes that result from or predispose development of the cardiac condition, and generate a cardiac prediction based on the identified features.

The disclosure provides systems, methods, and non-transitory computer readable media for non-invasively assessing cardiac pressure and efficiency. An example computer implemented method includes: receiving patient-specific image data of a cardiac chamber of a patient, the patient-specific image data acquired from an imaging modality over a cardiac cycle; receiving patient-specific vitals data; deriving non-invasive cardiac parameters from the patient-specific image data and the patient-specific vitals data; applying the non-invasive cardiac parameters to a trained model to generate: a total energy output for blood flow out of the cardiac chamber during the cardiac cycle, and a total energy input for blood flow into the cardiac chamber during the cardiac cycle; and generating, in real time, based on a ratio of the total energy output to the total energy input, a patient-specific cardiac efficiency value for the cardiac chamber, the patient-specific cardiac efficiency value classifying a severity of a heart disease of the patient.

The present disclosure describes compositions comprising vitamin C, vitamin E, zinc, copper, lutein, zeaxanthin, and Coenzyme Q10, as well as pharmaceutically acceptable salts of any one of the foregoing. The compositions are useful for treating diseases associated with loss of visual acuity, including, for example, age-related macular degeneration, and supporting cardiac health.

To provide an ultrasonic treatment device for a cardiac disease, capable of irradiating the whole heart with an ultrasonic wave.SOLUTION: An ultrasonic treatment device includes: at least two ultrasonic transducers that transmit and receive an ultrasonic wave; and a holding part that controls irradiation of an ultrasonic beam from the at least two ultrasonic transducers and holds the ultrasonic transducers. At least two ultrasonic transducers perform irradiation of the ultrasonic beam such that the whole heart is irradiated with the ultrasonic wave of the irradiation.SELECTED DRAWING: Figure 4

Methods and apparatuses, including devices and systems, for remote and detection and / or diagnosis of acute myocardial infarction (AMI). In particular, described herein are handheld and adhesive devices having an electrode configuration capable of recording three orthogonal ECG lead signals in an orientation-specific manner, and transmitting these signals to a processor. The processor may be remote or local, and it may automatically or semi-automatically detect AMI, atrial fibrillation or other heart disorders based on the analyses of the deviation of the recorded 3 cardiac signals with respect to previously stored baseline recordings.

Systems, methods and associated devices for locking a tensioning element, such as a bridging element, suture, wire or string, during a medical procedure, such as placement of a medical implant in a subject are provided herein. The systems, methods and devices described herein are particularly useful for delivery and deployment of heart implants for reshaping a heart valve annulus for treatment of a heart disorder, such as mitral valve regurgitation.

A method of treating or preventing a heart condition selected from the group consisting of acute myocarditis and acute pericarditis in a human subject in need thereof, the method comprising administering an effective amount of cannabidiol (CBD) to the subject.

The present invention relates to the technical field of electrocardiogram processing, and specifically relates to an electrocardiogram classification method, classification device, equipment and storage medium. The present invention first extracts the signal feature map of the electrocardiogram, then distinguishes the timing information and channel information included in the signal feature map, and then extracts the timing features from the timing information and the channel features from the channel information by using the transformer algorithm. Finally, the classification result of the electrocardiogram is given by comprehensively considering the timing features and channel features, and the classification result is used to reflect the user's heart disease. From the above analysis, it can be seen that the present invention processes the timing information and channel information separately, which improves the classification speed on the one hand and the accuracy of the classification result on the other hand.

The invention discloses a radiofrequency ablation needle assembly and ablation system comprising a multifunctional Bu-shaped coaxial sheath, the ablation assembly comprises a multipurpose double-channel Bu-shaped insulating coaxial sheath and an ablation needle, the Bu-shaped insulating coaxial sheath is detachably connected with a handle, and a position adjusting piece on the handle realizes self-adaptive adjustment of the length of the working end of the ablation needle. The B-shaped insulating coaxial sheath synchronously realizes auxiliary surgical diagnosis and treatment such as ablation, biopsy, pericardial effusion drainage, cardiac conduction bundle position marking, myocardial drug injection, ICD implantation and cardiac ECG monitoring, and ensures that integrated myocardial interventional diagnosis and treatment are safely and effectively completed. According to the ablation needle assembly containing the multipurpose B-shaped sheath, the bottleneck that an existing radio frequency ablation technology is low in efficiency, tedious in operation and multiple in complications is broken through, and an innovative solution is provided for comprehensive interventional therapy of heart diseases and various tumor diseases.

The invention relates to the technical field of bioinformatics, and discloses a three-dimensional virtual myocardial cell reconstruction method, system, equipment and medium based on cell membrane boundary division, a multi-feature joint matching strategy is adopted, the cross-layer cell tracking accuracy is greatly improved, and the problem of trajectory breakage caused by large volume and multi-core characteristics of myocardial cells is solved; secondly, the data processing efficiency is remarkably improved through an automatic feature extraction model; moreover, three-dimensional integration of space transcriptome data and cell morphological characteristics is innovatively realized, an adult myocardial cell three-dimensional model containing gene expression information is constructed on a single cell level for the first time, and unprecedented spatial resolution is provided for researching a molecular mechanism of heart diseases. The method is suitable for myocardial cell research, the technical framework of the method can be expanded to three-dimensional reconstruction of other large-volume cells such as skeletal muscle cells, neurons and hepatocytes, and the method has wide scientific research and clinical application value.

Disclosed is a method of characterizing a cardiac lesion using an image comprising: a black blood image generated from signals acquired during black blood late gadolinium enhanced magnetic resonance acquisition (ACQ1); a bright blood image is generated from signals acquired during bright blood late gadolinium enhanced magnetic resonance acquisition, the bright blood acquisition step (ACQ2) being different from the inversion recovery sequence. The method comprises: computer segmenting the bright blood image to generate localization data defining a set of at least one wall of the cardiac muscle; the computer extracts the set of at least one wall characterizing a cardiac lesion from the set of at least one wall defining the myocardium from the positioning data of the set of at least one wall according to the black blood image and the positioning data.

The invention discloses an electrocardiosignal anomaly detection method adopting a relevance vector machine. The electrocardiosignal anomaly detection method comprises the steps that a relevance vector machine model is built; the method comprises the following steps of: acquiring individual clinical electrocardiosignal data, and obtaining an implicit function between an electrocardiosignal vector and a state target value by utilizing an association vector machine model: after determining a weight coefficient vector, acquiring individual daily electrocardiosignal data, constructing an electrocardiosignal vector initial value, substituting the electrocardiosignal vector initial value into the implicit function, and solving a corresponding state target value initial value and a probability thereof; and judging whether the initial electrocardio state of the relative individual is abnormal or not and the credibility. Therefore, according to the electrocardiosignal anomaly detection method, in the Bayesian framework, through maximum likelihood estimation, subtle anomalies of the electrocardiosignals after the individual suffers from the heart disease are identified, the generalization ability is quite high, the method is not sensitive to noise, a probabilistic evaluation result can be given, credibility information is contained, and the method is easy to accept.

The application discloses a biomarker, a kit and application for diagnosing ventricular repolarization prolongation caused by cold exposure, and comprises one or more of the following applications: A1) application in preparing a product for diagnosing ventricular repolarization prolongation caused by cold exposure; A2) application in preparing a product for screening ventricular repolarization prolongation caused by cold exposure; A3) application in preparing a product for treating ventricular repolarization prolongation caused by cold exposure; A4) application in preparing a product for evaluating ventricular repolarization prolongation caused by cold exposure; A5) application in preparing a product for distinguishing ventricular repolarization prolongation caused by cold exposure from other heart diseases; and the miRNA marker is hsa-miR-125b-5p, and the nucleotide sequence is shown as SEQ ID No. 1. The expression amount of the hsa-miR-125b-5p marker provided by the application in the plasma of a ventricular repolarization prolongation caused by cold exposure group is significantly higher than that of a control group, has good diagnostic efficiency for ventricular repolarization prolongation caused by cold exposure, and indicates that the hsa-miR-125b-5p is a potential biomarker for ventricular repolarization prolongation caused by cold exposure.

The invention belongs to the field of gene drugs, and particularly relates to snoRNA Gm25215 as well as a simulant and application thereof. The invention further discloses the application of the snoRNA Gm25215 serving as the biomarker in preparation of a product for detecting myocardial cell ferroptosis or DIC. When doxorubicin cardiotoxicity occurs, the snoRNA expression activity is increased, so that the application of the snoRNA as a biomarker in preparation of detection products is provided. According to the characteristics of the snoRNA, the snoRNA is named as FERSNO, ferroptosis of myocardial cells is remarkably inhibited by knocking down the FERSNO, the FERSNO simulant and the application of the FERSNO simulant in preparation of drugs for preventing and / or treating heart diseases are provided, the FERSNO simulant is preferably an FERSNO inhibitor, and the application of the FERSNO simulant in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases in preparation of drugs for preventing and / or treating heart diseases. The reduction of FERSNO expression effectively alleviates the problems of poor prognosis of patients with doxorubicin cardiotoxicity and heart disease treatment. The nucleotide sequence of the FERSNO is as shown in SEQ ID NO. 1.

The invention relates to cellular microcompartments, each microcompartment successively comprising the following layers, which are organised around at least one lumen: —at least one inner layer of human cells undergoing cardiac differentiation, expressing at least one gene chosen from PDGFRα, MESP-1, NKX2-5, GATA4, MEF2C, TBX20, ISL1 and TBX5, —at least one intermediate layer of isotonic aqueous solution, and—at least one outer hydrogel layer. The invention also relates to the cardiac tissues obtained from said microcompartments and the use thereof, particularly in the treatment of heart disease.

To provide a method of generating a model for predicting heart disease.SOLUTION: The method includes obtaining a plurality of electrocardiogram data, and generating the model for predicting heart disease on the basis of the plurality of electrocardiogram data.SELECTED DRAWING: Figure 9

The present invention relates to the field of ECG signal denoising, and in particular to an ECG signal denoising method based on a generative adversarial network. ECG signal denoising has been a very challenging problem in the clinical diagnosis of heart diseases and telemedicine applications over the years. The present invention performs ECG signal denoising through a generator network and a discriminator network, wherein the generator network is based on the structure of a fully convolutional masked multi-layer denoising autoencoder, combining the characteristics of a fully convolutional neural network and a masked denoising autoencoder to perform ECG signal denoising. The network has a total of 11 layers, consisting of 5 convolutional layers and 6 deconvolution layers. The discriminator network uses the structure of a convolutional neural network, consisting of 5 convolutional layers and 1 fully connected layer. The idea of ​​adversarial training is used for network training, so that the generator acquires denoising capability, and more local features are retained while the denoising effect is significant.

The invention relates to the technical field of heart monitoring, in particular to wearable equipment and system for heart disease rehabilitation training monitoring, and the equipment comprises a wearable monitoring device which comprises a mounting seat, a wearable device and a wearable device, the ring type wearable device comprises a C-shaped shell, a main driving mechanism, a limiting mechanism and a baffle ring mechanism; and the ring type monitoring device comprises a display control mechanism and a sensor mechanism. Therefore, the device is reasonable in structure, easy to operate and excellent in using effect, the working efficiency is effectively improved, meanwhile, the using comfort of a patient is greatly improved, and powerful support is provided for rehabilitation training of the heart disease patient.

The present invention belongs to the technical field of non-invasive hemodynamic monitoring, and provides a dynamic non-invasive cardiac output monitoring system and method. Based on a sensor array, high-quality dynamic pulse wave waveform signals are collected and motion artifacts in the motion state are corrected to meet the input conditions of the cardiac output calculation model, and dynamic pulse wave feature vectors are calculated; an estimated value of the aortic cross-sectional area is calculated as a correction variable based on personalized characteristic parameters; based on a cardiac output model trained by deep learning, the cardiac output is calculated to realize real-time monitoring and evaluation of the dynamic cardiac output of the subject. The present invention can realize dynamic, real-time, and long-term monitoring of cardiac output parameters, and does not require intermittent correction, and is suitable for home rehabilitation use by patients with heart diseases.

The garment suitable for hosting a management and functioning logic and sensors / electrodes positioned in predetermined areas of the human body suitably is stabilized by the innovative characteristics of the garment itself and aimed at visualizing the state of health of the heart muscle. The sensors feature a new type of layout in order to minimize artifacts during vectorcardiogram generation. The entire apparatus communicates in real time with the Internet and exploits the architectures of Artificial Intelligence in order to improve the predictive performance of any heart disease.

A bed has a mattress. Force sensors transmit, to a computing system, a plurality of force streams, and wherein each of the force sensors in the plurality of force sensors is configured to: sense force applied to the bed by a user; and transmit, to a computing system, a stream of the plurality of force streams based on the sensing of the force applied to the bed. The computing system including at least one processor and memory, the computing system configured to: receive the plurality of force streams from the plurality of force sensors; and determine an atrial fibrillation (AF) risk-metric for the user using at least one of the force streams.

A system for processing electrocardiogram (ECG) signals is disclosed. A receiving module receives ECG signals corresponding to at least one channel. The ECG signal for each channel includes a plurality of beats. A beat processing module, for each channel: calculates an instantaneous heart rate (IHR) for each beat; forms a plurality of beat groups from the plurality of beats, each beat group including a set of beats of the plurality of beats; calculates an average heart rate (AHR) for each beat group based upon the IHR of the set of beats; and generates a representative beat for each beat group based upon the set of beats. A feature extraction module extracts, for each channel, a set of features based upon the plurality of representative beats. A classifier generates an indicator indicating a risk associated with a cardiac disease based upon the set of features for the at least one channel.

The present disclosure provides a modified-release pharmaceutical composition comprising 4-[(7-methoxy-2,3-dihydro-1,4-benzothiazepin-4(5H)yl)methyl]benzoic acid hemifumarate, and a pharmaceutically-acceptable excipient. The present disclosure methods of treating conditions associated with RyRs, including, for example, cardiac disorder or disease, a musculoskeletal disorder or disease, cancer associated muscle weakness, malignant hyperthermia, and diabetes.

The invention relates to a hierarchical scheduling diagnosis and treatment method based on heart disease complexity. The method comprises the following steps: performing preliminary reasoning on heart fusion features of a case to be diagnosed and treated by using an edge end small model to obtain a heart disease type distribution entropy value; based on a preset dynamic threshold value, obtaining a scheduling candidate queue by using the heart disease type distribution entropy value; obtaining a resource index and a case complexity index corresponding to the scheduling candidate queue on the basis of the diagnosis resource in combination with the heart fusion feature; based on the joint scheduling rule table, obtaining a scheduling result according to the resource index and the case complexity index; the scheduling result comprises edge end small model diagnosis and cloud end large model diagnosis. By adopting the method, an efficient hierarchical diagnosis and treatment mode with local processing as a main part and remote calling as an auxiliary part can be realized, the problems of excessive resource consumption and invalid uploading are avoided, and the diagnosis and treatment efficiency and accuracy are improved.

An information processing system (1) according to an embodiment comprises a determination unit (102) that inputs pulse information relating to the heartbeat of a subject and attribute information pertaining to the subject to a trained model and thereby generates a determination result indicating the presence / absence and the degree of heart disease in the subject. The trained model is trained using, for a plurality of providers who provide training data, pulse information relating to the heartbeat of the provider, attribute information pertaining to the provider, and diagnosis information indicating the state of cardiac function of the provider.