31 results about "Heart arrhythmia" patented technology

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses an electrocardiogram arrhythmia classification method and system based on a residual shrinkage network, and relates to the technical field of arrhythmia classification. According to the method, efficient electrocardiogram arrhythmia classification is achieved through multi-link cooperation, and a fine preprocessing, data balance strategy and multi-attention mechanism fusion model is designed; preprocessing provides high-quality input through wavelet denoising, precise R-wave detection and the like; the under-sampling-over-sampling mixed strategy is used for solving class imbalance and improving minority class recognition; the ResTCL-Net is fused with CNN, GRU, RCA, TSA and CLA modules, and signal features are mined in multiple dimensions; the optimization training strategy gives consideration to efficiency and stability, and is matched with comprehensive evaluation to guarantee performance. According to the scheme, the classification accuracy and generalization ability are remarkably improved, and abnormal heart beat recognition is enhanced.

The invention discloses an arrhythmia recognition method and system based on electrocardio waveform dynamic feature reconstruction, and the method comprises the steps: obtaining an electrocardiogram of a user, extracting the electrocardio potential of each cardiac blog in the electrocardiogram, and calculating the deformation rate vector of each cardiac blog according to the electrocardio potential; according to the deformation rate vector, calculating a heart rhythm transition value between the heart blogs as a heart rhythm transition fingerprint, calculating heart beat form difference energy between the heart blogs, and generating a form fluctuation index of each heart blog according to the heart beat form difference energy; and acquiring a change acceleration of the form fluctuation index between heart blogs, mapping the change acceleration, the form fluctuation index and the cardiac rhythm transition value into a discrete symbol, and inputting the discrete symbol into a diagnosis synthesizer to output an arrhythmia identification result.

The invention discloses a multi-modal electrocardiosignal and arrhythmia recognition system and method. The system comprises a signal acquisition module, a preprocessing module, a space-time alignment module, a feature extraction module, a self-adaptive fusion module, a multi-modal fusion module, a classification risk assessment module and a visual report module. The recognition method comprises the following steps: S1, synchronously acquiring an electrocardiosignal and a heart sound signal, and preprocessing the electrocardiosignal and the heart sound signal; s2, establishing a time corresponding relation between the electrical activity and the mechanical activity; s3, calculating a mechanical shrinkage efficiency index; s4, evaluating the quality of the electrocardiosignal and the heart sound signal in real time, and dynamically adjusting contribution weights of the electrocardiosignal and the heart sound signal in a fusion process according to the quality; s5, adopting a multi-head attention mechanism to generate a joint feature vector for classification; s6, inputting the joint feature vector into a classification network model, and outputting an arrhythmia type and a quantitative risk level at the same time by the classification network model; and S7, generating a visual atlas and a structured diagnosis report.

The invention relates to the technical field of traditional Chinese medicines, in particular to a traditional Chinese medicine composition for treating arrhythmia, which comprises raw rehmannia root, dwarf lilyturf tuber, donkey-hide gelatin, ginseng, raw licorice root, Chinese date, Chinese angelica, cassia twig, ginger, polygala root and tuckahoe. According to the Qianjin pulse restoring granules prepared from the traditional Chinese medicine composition, the three-in-one treatment rule of nourishing yin and blood, warming yang and promoting coronary circulation and tonifying qi and soothing the nerves is put forward, and the core pathogenesis of arrhythmia of yin-yang deficiency and heart-vessel malnutrition is focused on that yin-blood deficiency causes heart-vessel malfunction, yang-qi deficiency causes agitation and weakness, the yin-blood deficiency and the yang-qi deficiency cause causes and effects of each other, and palpitation, pulse stagnation and the like are caused. The traditional Chinese medicine composition breaks through the limitation of'partial yin tonifying 'or'partial yang tonifying' of the traditional prescription, and by regulating yin and yang and tonifying both qi and blood, 'sufficient yin blood and filled pulse, and recuperating yang qi and dredging pulse ', not only is the root cause of deficiency of yin and blood solved, but also the symptom of deficiency of yang qi is improved, and the technical effect of'treating both symptoms and root causes' is achieved.

The invention relates to an intelligent grading early warning method, device and equipment for an electrocardio critical value and a storage medium, and the method comprises the steps that an obtained electrocardio signal of a user is analyzed and processed, and arrhythmia features are determined; based on a preset multi-level grading standard, carrying out danger level judgment on the arrhythmia features, and determining a target danger level; according to the target danger level, a visual report is generated, and the visual report comprises a heart rate time sequence diagram and target guidance information associated with the target danger level; compared with the prior art, according to the technical scheme, the arrhythmia features can be determined by analyzing the electrocardiosignals, the danger level is judged according to the multi-level standard, and finally the visual report containing the target guidance information is generated, so that the problem of confusion after detection of a user can be effectively solved, a health management closed loop from detection to intervention is realized, and the user experience is improved. And meanwhile, the understanding and coping efficiency of the user on the electrocardio result is improved.

This disclosure presents systems and methods for baroreflex activation therapy (BAT) to regulate inflammation by modulating autonomic nervous system activity. Electrical stimulation is delivered to one or more baroreceptors, and real-time physiological signals are monitored, including inflammatory biomarkers (e.g., TNF-α, IFN-γ, IL-1, IL-6, IL-12, CRP) and autonomic parameters such as heart rate variability (HRV). A closed-loop feedback module analyzes biomarker fluctuations and autonomic balance changes and dynamically modifies stimulation parameters, including amplitude, pulse width, frequency, and duty cycle, to suppress systemic inflammation and restore autonomic balance. In certain embodiments, multi-channel stimulation architectures and AI-driven algorithms or machine learning models analyze biomarker trends and historical patient response patterns to optimize therapy. The disclosed BAT systems and methods may be used to treat inflammatory diseases, autoimmune disorders, cancer progression, chronic inflammatory pain conditions, arrhythmias associated with inflammation, and metabolic dysfunction.

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 relates to an ointment formula based on qi and blood tonifying and heart and kidney dredging and regulating and a preparation method, and belongs to the technical field of traditional Chinese medicine, the ointment is prepared from ginseng, astragalus membranaceus, five-prepared rehmannia glutinosa, Chinese yam, poria cocos, deer-horn glue, tortoise plastron, donkey-hide gelatin, dogwood, fructus lycii, semen cuscutae and other medicinal materials according to a specific ratio. The preparation method comprises the following steps: grinding part of the medicinal materials into powder, decocting the prepared rhizome of rehmannia firstly, decocting a plurality of sub-medicinal materials together, melting the glue medicinal materials by heat, mixing with other medicinal material decoctions, and concentrating to obtain the ointment. Through the synergistic effect of the medicinal materials, the comprehensive effects of reinforcing qi and nourishing yin, warming yang and promoting diuresis, tonifying kidney and replenishing essence and promoting circulation of heart and kidney are achieved, and the traditional Chinese medicine composition has the advantages of being comprehensive in curative effect, small in side effect and sufficient in component retention for consumptive disease, premature senility, chronic heart failure and arrhythmia caused by deficiency of qi and blood, yang deficiency and water flooding and heart and kidney disharmony.

The application discloses a method and device for arrhythmia analysis based on heart beat classification, equipment and medium, wherein the method comprises: generating a morphology amplitude coding vector of a target heart beat segment as a target morphology amplitude coding vector, the target heart beat segment being any one of the heart beat segment time series set; performing inner product calculation on the target morphology amplitude coding vector and each heart beat template vector in a preset heart beat template vector set to obtain an inner product value set; determining a target heart beat type corresponding to the target heart beat segment according to the inner product value set; and performing arrhythmia analysis on each target heart beat type corresponding to the heart beat segment time series set to obtain an arrhythmia analysis result. The application avoids the technical problems that the traditional method may have a large amount of calculation, ignores the amplitude difference of the waveform and easily ignores the morphology difference of the waveform when the amplitude is low, has simple steps and small calculation amount, and is thus suitable for real-time monitoring scenes.

Embodiments of the invention are broadly drawn to methods for determining an optimum dose of an antiarrhythmic drug, for example sotalol. In particular, the method involves titrating the dose of the drug gradually to determine the optimum plasma concentration for a patient, whether the patient has normal or abnormal renal function.

A power generator is disclosed for generating ablating energy deliverable to a catheter for treatment of heart rhythm disorders. The power generator includes a first output port configured to deliver ablation energy through a first conductive wire of a catheter and a second output port configured to receive a sensing signal from a thermocouple junction formed at a catheter electrode. The generator includes a first power driver for producing waveforms suitable for radiofrequency ablation (RFA) and a second power driver for generating pulsed waveforms suitable for pulsed field ablation (PF A). A switching circuit selectively couples one of the power drivers to the first output port based on a selected ablation modality. A control circuit receives an input indicating the selected modality and controls the switching circuit accordingly. The system enables dual-modality ablation using a shared catheter interface, facilitating seamless transition between RFA and PF A without requiring hardware reconfiguration.

Providing quinazolindione compounds and their uses. [Solution] Quinazoline dione compounds are provided for treating cardiac indications such as hypertrophic cardiomyopathy and diastolic dysfunction. Diseases treated by the methods described herein include, but are not limited to, cardiomyopathy, hypertrophic cardiomyopathy (HCM), abnormal heart rhythms, aortic disease, Marfan syndrome, coronary artery disease, heart attack, heart failure, rheumatic heart disease, peripheral vascular disease, stroke, deep vein thrombosis, and pulmonary embolism.

The invention discloses a sudden arrhythmia death risk prediction method and device, a storage medium and equipment, and relates to the technical field of biomedicines.The sudden arrhythmia death risk prediction method comprises the steps that multi-channel magnetocardiogram signals of a subject are collected, fidelity enhancement processing is carried out on the multi-channel magnetocardiogram signals, and core characteristics capable of representing heart magnetic field characteristics are extracted; constructing a topological geometric model of the heart fiber plexus, and extracting topological invariants of the heart electrical conduction system based on the core features and the topological geometric model of the heart fiber plexus; non-equilibrium phase change dynamics detection is carried out on the heart fiber plexus topological geometric model, and phase change characteristics reflecting the stability of a heart electrical conduction system are obtained; inputting the topological invariants and the phase change features into a preset topological neural network, and completing quantitative analysis of the chaos degree of the heart electrical conduction system; and based on the quantitative analysis result, generating a risk score and an early warning index of the subject about sudden arrhythmia death. According to the invention, accurate early warning of sudden death risk of arrhythmia in an early stage can be realized.

To provide an irregular pulse classification method of a 12-lead electrocardiogram signal and a device for performing the method.SOLUTION: A device for performing irregular pulse classification of a 12-lead electrocardiogram signal according to one embodiment of the present invention is a computing device provided with one or more processors and a memory for storing one or more programs executed by the one or more processors. The device includes a data input module which receives input of the 12-lead electrocardiogram signal and a classification module which outputs a classification result about the 12-lead electrocardiogram signal by using the technique of a machine learning base based on the input 12-lead electrocardiogram signal.SELECTED DRAWING: Figure 1

Pharmaceutical compositions comprising an antiarrhythmic agent for treatment of a heart condition via inhalation. Methods of treating a heart condition include administering by inhalation an effective amount of at least one antiarrhythmic pharmaceutical agent to a patient in need thereof. Nebulized drug product and kits are also disclosed.

This invention discloses a method and system for early warning of arrhythmias in smartwatches based on deep learning. The method first acquires PPG signals using a photoplethysmography (PPG) sensor and performs noise reduction and standardization using an adaptive filtering algorithm. Then, it uses a wavelet transform peak detection algorithm to identify heartbeat locations and obtain a heart rate variability (HRV) feature sequence. Next, it uses an improved Gramian angular field transform algorithm to convert the one-dimensional HRV feature sequence into a two-dimensional heart rhythm image. Then, it uses a ResNet-50 convolutional neural network to train an arrhythmia classification model, achieving automatic identification of different types of arrhythmias. Finally, it uses sliding window technology and an early warning mechanism to achieve continuous monitoring and timely alarms. This invention effectively solves key technical problems in existing arrhythmia detection technologies, such as low detection accuracy, poor real-time performance, high equipment costs, and unsatisfactory user experience, providing an innovative solution for heart rate health monitoring in smart wearable devices.

A locator assembly (100) for determining a location of an arrhythmogenic foci (632) in or near a heart (101). The locator assembly (100) having a longitudinal axis (100a) and a circumference (100c). The locator assembly (100) includes a device body (112) and a plurality of components that are coupled to the device body (112). The plurality of components are distributed about the longitudinal axis (100a) and the circumference (100c). The device body (112) is movable between (i) a contracted state wherein the device (112) body is contracted, and (ii) an expanded state wherein the device body (112) is expanded outwardly from the contracted state so that the device body (112) has an increased circumference (100c).

Apparatus and method are described including an array (22) of multiple electrodes (24) at least a portion of which are configured to be placed in contact with tissue of a heart of a subject at respective locations within the subject's heart. A computer processor (28) drives stimulating currents via respective electrodes according to a sequence and pacing parameters, and detects electrical signals at others of the electrodes (24) resulting from the stimulating currents. Based upon the detected electrical signals, the computer processor (28) derives indications of conduction and / or repolarization at the locations within the subject's heart at which respective electrodes (24) contact the tissue of the heart. In response thereto, the computer processor (28) identifies at least some of the locations within the subject's heart as being sources of a heart rhythm abnormality. Other applications are also described.

A system for characterizing a heart rhythm configured to implement, by computer, the following procedures generating an input vector, the components of the input vector comprising, among indicators of sets of indicators of probability of presence only each indicator of probability of presence of a selection, and generating, using a global classifier, global indicators of probability of presence of arrhythmias in a time window from the input vector, the system including a selector making it possible to select and / or deselect at least one of the indicators of probability of presence such that the selection is composed of each selected indicator of probability of presence.

The application relates to the technical field of medicines, in particular to a K + Application of a channel blocker in preparation of a medicine for treating liver fibrosis. The application provides a K + New use of an old drug, a channel blocker (sotalol), sotalol is clinically used as an antiarrhythmic drug, and simultaneously has the characteristics of inhibiting K + Channel, experiments show that sotalol can effectively inhibit liver fibrosis and reduce the expression of fibrosis-related protein a-SMA, and the specific approach is to regulate the expression and / or function of autophagy and EMT molecules to realize the inhibition of liver stellate cell fibrosis, thereby providing a new drug selection for clinically treating liver fibrosis.

Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrythmia undersensing that occur due to inappropriate filtering of a signal indicative of cardiac electrical activity. A method includes obtaining a signal indicative of cardiac electrical activity, and using a first bandpass filter to produce a first filtered version thereof, using a second bandpass filter to produce a second filtered version thereof, wherein the first bandpass filter passes frequencies within a first frequency range, and the second bandpass filter passes frequencies within a second frequency range that is wider than the first frequency range. The method also includes selectively changing from using the first filtered version of the signal to monitor for a VS event, to using the second filtered version of the signal to monitor for a VS event, based on first criteria, and vice versa, based on second criteria.

This invention discloses an intelligent human health monitoring system based on multi-sensor fusion and edge computing, relating to the fields of biomedical engineering, Internet of Things, and embedded systems. Using a microcontroller as the computing core, it integrates heart rate and blood oxygen sensors and temperature sensors to collect physiological signals. It abandons traditional blocking delay logic and constructs a deterministic scheduling kernel based on time-slice polling. To address motion artifacts in photoplethysmography (PPG) pulse waves, a fixed-point Kalman filter algorithm is introduced for real-time signal reconstruction. TinyML edge inference technology is used to run a lightweight decision tree model locally for real-time arrhythmia identification. A transient thermal prediction algorithm based on Newton's law of cooling is used to compensate for the thermal inertia of the temperature sensor. The respiratory rate is derived through amplitude modulation analysis of the PPG signal. This invention achieves multi-dimensional synchronous monitoring of heart rate, blood oxygen, body temperature, and respiratory rate, featuring low power consumption, high precision, and intelligence, making it suitable for home health monitoring and telemedicine.

The invention relates to the technical field of arrhythmia classification, and discloses a method for classifying various arrhythmia signals based on spectral analysis. The method comprises the steps that electrocardiosignal data of a target object are collected and preprocessed, and a standardized electrocardiosignal sequence is generated; performing multi-scale time-frequency transformation on the sequence, extracting a time-frequency domain characteristic matrix, and constructing a frequency spectrum energy distribution map and a phase change track map according to the time-frequency domain characteristic matrix; dividing a dominant frequency band interval based on the former and marking an energy ratio weight; and calculating the phase offset of the adjacent cardiac cycles according to the latter, and generating a phase stability index. And fusing the two to obtain an initial classification feature vector, and generating an optimized feature vector by means of spatial correlation dynamic compensation of adjacent signal segments. The method comprises the following steps: selecting a plurality of arrhythmia signals, matching the arrhythmia signals with a preset arrhythmia type feature library to determine a candidate set, performing hierarchical screening according to feature similarity, and outputting a final classification result, so that accurate classification of various arrhythmia signals can be realized.

The invention provides an arrhythmia classification method and device based on compressed sensing and electronic equipment, and relates to the technical field of arrhythmia classification. The method comprises the following steps: acquiring electrocardiosignal data to be classified, and calculating to obtain compressed electrocardiosignal data according to the electrocardiosignal data and a deterministic binary diagonal matrix; wherein the deterministic binary diagonal matrix comprises a diagonal block area, the size of the diagonal block area is determined according to a preset compression ratio, and the structural similarity between the compressed electrocardiosignal data and the electrocardiosignal data is kept; carrying out R wave detection on the compressed electrocardiosignal data according to a preset sampling frequency, and determining an R wave position; carrying out heart beat segmentation on the compressed electrocardiosignal data according to the R-wave position to obtain segmented heart beat data; and inputting the segmented heart beat data into the trained classification model to obtain an arrhythmia classification result. The data processing efficiency can be improved.

The invention discloses an arrhythmia prediction method of wearable equipment based on an improved Transform model, and belongs to the technical field of wearable equipment. The method comprises the following steps: collecting multi-modal physiological signals such as electrocardio, heart rate change, electrodermal response, body temperature, blood oxygen concentration and melatonin of a user, extracting rhythm features, neural fluctuation and biochemical change trends, constructing a fusion feature map, inputting the map and an adjustment path, jointly mapping the map and the adjustment path to a high-dimensional nuclear space, carrying out nonlinear feature transformation by adopting an RKS algorithm, and obtaining a high-dimensional nuclear space model; the method comprises the following steps: extracting high-dimensional features from a wearable device, improving the expression efficiency and reducing the calculation burden, inputting the high-dimensional features into an improved Transform model, compressing the calculation complexity by introducing a Linform attention mechanism based on low-rank linear projection, executing long-time sequence modeling and context correlation extraction, generating an arrhythmia prediction result, and feeding back the result to the wearable device in real time. The method gives consideration to performance and energy consumption, and is suitable for edge deployment scenes.

A method of increasing sodium current in a cardiac cell generally includes introducing into the cardiac cell an miR-448 inhibitor in an amount effective to decrease miR-448 suppression of SCN5A mRNA transcription, thereby increasing sodium current in the cardiac cell. A method of increasing translation of SCN5A mRNA in a cell generally includes introducing into the cell an miR-448 inhibitor in an amount effective to decrease miR-448 suppression of SCN5A mRNA transcription. A method of decreasing arrythmia in a cardiac cell generally includes introducing into the cardiac cell an miR-448 inhibitor in an amount effective to decrease miR-448 suppression of SCN5A mRNA transcription. A method of treating arrythmia in a patient having, or at risk of having, arrythmia generally includes administering to the patient an miR-448 inhibitor in an amount effective to decrease the likelihood or extent of arrythmia in the patient. In some embodiments of all methods, the miR-448 inhibitor can be an miR-448 antagomir or an miR-448 sponge.

An adaptive electrocardiogram rhythm recognition method based on a variational autoencoder, the method comprising: S1: creating and training a model; S2: importing data according to the trained model to recognize results and classification; and S3: adaptive updating. In S1, the step of creating and training the model comprises: 1) collecting a large amount of electrocardiogram data; and preprocessing the collected data; 2) two-dimensionizing the collected and preprocessed electrocardiogram data; 3) creating a neural network model; and 4) clustering a classifier. In a pre-training process, the electrocardiogram is encoded by using an unsupervised variational autoencoder, the normal electrocardiogram and different types of arrhythmia are distinguished by using a clustering method, a general model with good performance is obtained, and the newly collected electrocardiogram data is classified. Then, the new data can also be added to the clustering, thereby forming a new clustering model to better adapt to different physical conditions and environmental changes.