261 results about "Atrial fibrillation" patented technology

Systems and methods are disclosed for the treatment and prevention of cardiac dysrhythmias and / or hypertension, and more specifically to systems and methods of treating cardiac dysrhythmias, including atrial fibrillation, as well as hypertension through noninvasive peripheral nerve stimulation.

Methods and system for atrial fibrillation ablations and other cardiac procedures utilizes a cardiac mapping system for navigation and guidance, and includes an additional virtual reality (VR) system and an augmented reality (AR) or mixed reality (MR) system. The Cardiac mapping system utilizes medical images and patient's electrical signals. The virtual reality (VR) system is utilized pre-procedure for visualization and pre-procedure planning. The augmented reality (AR) or mixed reality (MR) system is utilized pre-procedure or intra-procedure. Methods and system are disclosed utilizing pre-built virtual device models which interact with 3D volume rendered structures from patient's CT or MRI of the region of interest (ROI) for the different type of cardiac procedure. With virtual reality (VR) the operator utilizes hand-held sensors. With augmented reality (AR) or mixed reality (MR) a combination of hand gestures and an X-box controller is used to manipulate virtual devices, anatomical structures in the Hologram.

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.

Systems and methods are disclosed for the treatment and prevention of cardiac dysrhythmias and / or hypertension, and more specifically to systems and methods of treating cardiac dysrhythmias, including atrial fibrillation, as well as hypertension through noninvasive peripheral nerve stimulation.

The present invention includes systems, devices and methods for treating and / or diagnosing a heart arrhythmia, such as atrial fibrillation. Specifically, the present invention provides a system including a diagnostic catheter and an ablation catheter. The diagnostic catheter includes a shaft, multiple dipole mapping electrodes and multiple ultrasound transducers. The ablation catheter is slidingly received by the diagnostic catheter shaft.

The invention relates to the technical field of electrocardiogram detection, in particular to an electrocardiogram atrial fibrillation prediction method based on multi-modal deep learning, which comprises the following steps: S1, collecting electrocardiogram signal data, S2, performing signal preprocessing and feature fusion, and S3, performing model training and atrial fibrillation prediction. According to the method, multi-modal features are extracted through a complex multi-scale attention mechanism and a feature self-adaptive attention mechanism, a deep learning model based on the combination of a convolutional neural network and a long-short term memory network is combined, multi-dimensional features and time sequence information of electrocardiosignals are fully utilized, and the multi-modal features of the electrocardiosignals are extracted. Therefore, the efficiency, the accuracy and the reliability of the atrial fibrillation prediction method are greatly improved, and the problem that the sensitivity and the specificity cannot meet the requirements when the prior art faces complex and diversified electrocardiosignals is solved.

The invention discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

A method including obtaining first data based on one or more unipolar EGMs recorded in an atrium of a living human afflicted with atrial fibrillation and obtaining second data based at least in part on wavelet processing of the obtained first data as part of a process to develop the second data, wherein the second data is data indicative of ventricular far-field artifact in the obtained first data.

The invention discloses an electronic sphygmomanometer with an atrial fibrillation detection function, an atrial fibrillation detection method and device and electronic equipment, and belongs to the technical field of medical instruments. The electronic sphygmomanometer comprises a cuff, a processor, a detection result output component, a pressure signal detection component and a piezoelectric signal detection component, the pressure signal detection component and the piezoelectric signal detection component are respectively used for collecting a pressure signal in the cuff in a pressure release stage in the current blood pressure measurement process and a Korotkoff sound signal generated by brachial artery pulsation; the processor is used for analyzing and processing the pressure signal and the Korotkoff sound signal, acquiring the signal time position and the amplitude of the Korotkoff sound signal, acquiring a pulse interval sequence of the current blood pressure measurement based on the signal time position, and acquiring an atrial fibrillation state classification result corresponding to the current blood pressure measurement based on the pulse interval sequence and the amplitude; and the detection result output component is used for outputting an atrial fibrillation state classification result. The electronic sphygmomanometer can accurately detect whether atrial fibrillation occurs or not.

The invention relates to a method for processing and analyzing pulse condition information of an atrial fibrillation patient in the technical field of pulse condition analysis and recognition, which comprises the following steps of: S1, acquiring a pulse signal and converting the pulse signal into a digital signal; s2, noise reduction preprocessing is conducted on the digital pulse signals through an empirical mode decomposition method in combination with self-adaptive threshold value processing; s3, Fourier transform FT is carried out on the preprocessed pulse signal, the pulse signal is converted into a frequency domain signal, Hilbert-Huang transform HHT is combined to process a non-stationary signal, frequency components of local instantaneous transform are extracted, and frequency spectrum features of the pulse are comprehensively obtained; s4, key characteristic parameters including frequency, amplitude, period and fluctuation amplitude are extracted from the frequency spectrum, and matching analysis is carried out on the key characteristic parameters and a predefined pulse condition database; according to the processing and analyzing method, automation, digitization and quantification of pulse condition analysis are achieved through the modern signal processing technology combining FT-HHT, and then objectivity, accuracy and wide applicability of pulse diagnosis are improved.

The invention discloses a method for constructing an atrial fibrillation small animal model through succinic acid induction and application, and the method comprises the following steps: in an animal model construction period, enabling a small animal to ingest an aqueous solution containing succinic acid or all pharmaceutically acceptable salts of succinic acid every day, and finishing the modeling period to obtain the animal model. Compared with a traditional angiotensin AngII administration method, the atrial fibrillation small animal model has the advantages that the cost of a medicine for constructing the animal model is lower, the economic benefit is higher, and the atrial fibrillation induction effect is better; the model represents the most common atrial fibrillation type in clinic, and compared with an acute transient atrial fibrillation model, the research and application range is wider; compared with tail vein injection, intraperitoneal injection, subcutaneous pump burying and other modes, the non-creative model has the advantages that damage to small animals is smaller, and the requirement for the operation technology is low.

The invention relates to the technical field of medical health data management, in particular to a method for constructing a cross-institution atrial fibrillation recurrence prediction model based on federal learning. According to the method, a hierarchical federated framework is established, each mechanism node locally performs standardized preprocessing on multi-source heterogeneous data, a multi-task basic model fusing an atrial fibrillation recurrence prediction main task and a data quality evaluation auxiliary task is constructed and distributed, a federated distillation mechanism is adopted, and local training is performed in combination with an encrypted confrontation sample. Minimizing teacher-student model prediction difference and calculating parameter update quantity, dynamically calculating aggregation weight based on data quality score and effective sample quantity, updating a global model by adopting weighted average, and stopping iteration when convergence conditions are met by synchronously monitoring three indexes of global loss value, prediction accuracy stability and parameter consistency. And finally, a self-adaptive cross-mechanism prediction model is generated, so that the robustness and prediction accuracy of the model under multi-center heterogeneous data are improved.

The invention discloses an AI-based atrial fibrillation patient health risk prediction method, and belongs to the technical field of medical information, and the method specifically comprises the steps: receiving a physiological parameter record and an intervention treatment record of an atrial fibrillation patient, carrying out the time sequence alignment operation, and forming structured patient data; extracting risk-related features from the structured patient data, wherein the risk-related features are divided into basic risk features and intervention response features; inputting the basic risk features into a basic risk prediction network, and outputting a basic risk score; inputting the intervention response characteristics and the basic risk score into an intervention effect separation network together to generate a post-intervention risk score; calculating a net effect value of treatment intervention according to the difference between the basic risk score and the post-intervention risk score; combining the basic risk score, the post-intervention risk score and the net effect value to generate a patient individualized long-term risk prediction trajectory; and accurate prediction of the long-term health risk of the atrial fibrillation patient is realized.

The present disclosure generally relates to displaying atrial fibrillation data. A computer system concurrently displays a first representation of atrial fibrillation data for a first period of time and a first representation of non-heart data the first period of time.

The present invention discloses an atrial fibrillation detection method and device based on the analysis of the pulse oscillation wave characteristics of blood pressure measurement, wherein the device includes a signal acquisition module, an atrial fibrillation diagnosis module, a local storage module, and a display module. The signal acquisition module obtains the pulse oscillation wave in the process of blood pressure measurement. The work of the atrial fibrillation diagnosis module includes the following steps in sequence: filtering and denoising the obtained signal and removing the baseline; extracting the RR interval sequence, and preliminarily locating the atrial fibrillation by the RR interval sequence and the difference; extracting the trough interval sequence and screening out the premature beat signal; multi-feature extraction, including the main wave peak height, the dicrotic wave peak value, and the pulse width at 1 / 5 of the main wave peak height; obtaining multiple one-dimensional arrays based on the extracted features, calculating the corresponding indicators, and obtaining the atrial fibrillation detection results. The local storage module records the pulse oscillation wave and the atrial fibrillation detection results. The display module displays the atrial fibrillation detection results and waveforms in real time. The present invention can detect atrial fibrillation based on the pulse oscillation wave, which has positive significance for the diagnosis and treatment of atrial fibrillation.

The embodiment of the invention provides an atrial fibrillation classification method, electronic equipment, a readable storage medium and a program product, and relates to the technical field of artificial intelligence and medical data analysis. Specifically, the method comprises the following steps: performing classification processing on electrocardiosignal data through an AI network to obtain classification information related to atrial fibrillation; performing classification processing on the electrocardiosignal data through an AI network, including: extracting feature data of at least two scales for the electrocardiosignal data through a multi-scale convolution module; performing depth separable convolution processing on the feature data through a first feature processing module, and performing channel attention and space attention processing to obtain a first feature; extracting, by a second feature processing module, a second feature including context information based on the first feature; and determining classification information related to the atrial fibrillation based on the second feature through a classification module. According to the invention, the classification accuracy and generalization ability of the model can be improved, and the complexity of the model is reduced.

Systems, catheter and methods for treating Atrial Fibrillation (AF) are provided, which are configure to illuminate a heart tissue having a lesion site; obtain a mitochondrial nicotinamide adenine dinucleotide hydrogen (NADH) fluorescence intensity from the illuminated heart tissue along a first line across the lesion site; create a 2-dimensional (2D) map of the depth of the lesion site along the first line based on the NADH fluorescence intensity; and determine a depth of the lesion site at a selected point along the first line from the 2D map, wherein a lower NADH fluorescence intensity corresponds to a greater depth in the lesion site and a higher NADH fluorescence intensity corresponds to an unablated tissue. The process may be repeated to create a 3 dimensional map of the depth of the lesion.

The present invention is directed to a system for monitoring and evaluating electrogram signals representing electric activities of a heart chamber by computing the entropy of the electrogram signals, said system comprising a signal input that is connected to a mapping catheter comprising at least one electrode pole for picking up electric potentials and generating electrogram signals from the picked up electric potentials, along with the 3D location of the catheter at the time of recording and a signal processing and evaluation unit for processing and evaluating electrogram signals received at the signal input. The invention is further to a non-transitory computer readable medium for use in a system for monitoring and evaluating electrogram signals representing electric activities of a heart.

The invention provides an atrial fibrillation prediction method and system based on local and global feature fusion. The atrial fibrillation prediction method comprises the following steps that S1, electrocardiosignals are preprocessed; s2, constructing an atrial fibrillation prediction model, wherein the atrial fibrillation prediction model comprises a DenseNet and multi-feature fusion module constructed based on depth separable convolution, and a bidirectional Transform; s3, inputting the preprocessed electrocardiosignals into an atrial fibrillation prediction model; extracting multi-scale local features in the electrocardiosignals by a DenseNet constructed based on depth separable convolution and a multi-feature fusion module; performing inversion on the extracted multi-scale local features to obtain inversion features, inputting the multi-scale local features and the inversion features into a bidirectional Transform, and capturing global dependence of the signal from two directions; and performing classification based on the local and global fusion features extracted by the bidirectional Transform, and outputting a classification result to realize atrial fibrillation prediction. According to the invention, the performance of the model on an atrial fibrillation prediction task can be improved.

The invention discloses an automatic atrial fibrillation and ventricular fibrillation recognition system suitable for electrocardiogram acquisition equipment. The system comprises a data preprocessing module, a feature extraction module, a feature integration module, a time sequence analysis module and a decision recognition module. Wherein the data preprocessing module is responsible for segmenting heart beat and standardizing data; the feature extraction module independently extracts features in each lead by using a grouping convolution technology; the feature integration module strengthens information exchange between leads through a dense connection architecture; the time sequence analysis module captures time dependence and long-distance characteristics of the electrocardiogram by utilizing a self-attention mechanism; and the decision recognition module synthesizes the output of the modules and provides an accurate electrocardiogram recognition result for the electrocardiogram acquisition equipment. In an MIT-BIH comprehensive database test, the accuracy rates of the system in a patient and between patients are 99.81% and 99.61% respectively, the accuracy rates still exceed 97% even under the signal-to-noise ratio of 6dB, excellent electrocardiogram recognition performance is shown, and an accurate and effective automatic recognition scheme is provided for electrocardiogram collection equipment.

The invention provides an atrial fibrillation prediction method based on deep learning and polygene scoring, which combines electrocardiogram (ECG) signals, clinical feature data and polygene risk scoring (PRS) based on multi-modal data fusion. By integrating the deep learning model, clinical risk score and polygene risk score, the accuracy and stability of AF prediction are significantly improved. The technical value is embodied in that: multi-source data fusion: combining ECG signals, clinical data and gene information in a single system for the first time, and providing comprehensive individualized risk assessment; real-time analysis capability: based on ECG real-time data processing capability, the real-time analysis capability can be used for dynamic risk assessment; and clinical decision support: a scientific and quantitative AF prediction report is provided for medical staff, and formulation of an intervention strategy is assisted.

Small molecule inhibitors of Nav1.8 voltage-gated sodium ion channel, including compounds of formula (I), (II), (III), (IV), and (V) are described. Also described are pharmaceutical compositions containing a compound of formula (I), (II), (III), (IV), and (V) and uses of the compounds and pharmaceutical compositions for inhibiting Nav1.8 voltage-gated sodium channels and treating Nav1.8 mediated diseases, such as pain and pain-associated diseases and cardiovascular diseases, such as atrial fibrillation.

The invention relates to an electrocardio atrial fibrillation classification detection method, system and device capable of resisting individual difference and a storage medium. The system comprises a signal acquisition module used for acquiring ECG signals; the invention discloses an anti-individual-difference preprocessing circuit. The signal segment reading module is used for reading a signal segment according to an overlapping segmentation strategy, the Hanning window weighting module is used for performing window function weighting on the signal segment and only storing a first half coefficient through address mapping multiplexing by utilizing the symmetry of a window function, and the FFT frequency domain transformation module is used for converting a time domain signal into a frequency domain; the QRS frequency domain enhancement filtering module is used for enhancing QRS spectrum components and attenuating individual difference frequency components, the IFFT time domain reconstruction module is used for converting frequency domain signals back to a time domain, and the amplitude normalization output module is used for eliminating signal amplitude differences among individuals; and the deep learning classification module is used for performing atrial fibrillation detection on the standardized signal. Based on a CPU and FPGA heterogeneous architecture, the accuracy and robustness of atrial fibrillation detection are improved, and the method is suitable for the fields of wearable monitoring, telemedicine and the like.

The present disclosure provides antibodies that bind to catalytic domains of Factor XI (FXI) and methods of use thereof. According to certain embodiments, the antibody is an antagonist antibody that inhibits blood clot formation achieved through an intrinsic pathway without affecting hemostasis, as shown by the effect of the antagonist antibody on extension of aPTT without affecting PT. As such, these antagonist antibodies can be used in therapeutic regimens for the treatment of blood clotting diseases or disorders or with clot formation as risk factors, such as, but not limited to, atrial fibrillation. In certain embodiments, the present disclosure comprises antibodies that bind to FXI and mediate clot formation or thrombosis. The antibodies of the present disclosure may be fully human, non-naturally occurring antibodies.

The present invention provides a long non-coding RNA (lncRNA) biomarker composition in serum exosomes, comprising LOC105377989, LOC105375434, LOC107986997, and LOC101927073, for diagnosing atrial fibrillation on the basis of the results of profile analysis of lncRNA in serum exosomes of patients with atrial fibrillation. When used, the lncRNA biomarker composition enables non-invasive in-vitro diagnosis, and exhibits an excellent effect in atrial fibrillation diagnosis, and thus is highly applicable as a useful diagnostic biomarker for atrial fibrillation.

A method of establishing a model for detecting atrial fibrillation (AF) and sleep apnea (SA) is implemented by a computer system that stores training electrocardiograms, and includes steps of: dividing the training electrocardiograms into training segments, each of which contains a common length of time of recorded electrical activity of a heart; for each of the training segments, labeling the training segment with a symptom indicator that indicates whether the training segment is related to AF and whether the training segment is related to SA; for each of the training segments, performing feature extraction on the training segment to obtain an entry of feature data; and establishing the model by using a machine learning algorithm based on the entries of feature data and the symptom indicators.

The invention relates to the technical field of traditional Chinese medicines, in particular to a traditional Chinese medicine composition with the effects of treating atrial fibrillation and improving anxiety, a medicine and a preparation method and application thereof. When the traditional Chinese medicine composition provided by the invention is combined with auricular point pressing beans to treat patients with atrial fibrillation and anxiety, clinical symptoms of atrial fibrillation and anxiety can be further improved, specifically, the score of traditional Chinese medicine syndromes is reduced, the effective rate of electrocardiograms and the clinical effective rate of anxiety are improved, the life quality of the patients is improved, and no obvious adverse reaction exists. Meanwhile, a traditional Chinese medicine oral administration and traditional Chinese medicine external treatment combined method is adopted, syndrome differentiation treatment can be carried out from the holism concept, the holographic therapy of external treatment of internal diseases can be utilized to the maximum extent, and a simple, convenient, safe and effective treatment thought is provided for early prevention, early diagnosis and early treatment of more diseases.

This document discusses, among other things, systems and methods to receive cardiac electrical information of a subject, detect a premature ventricular contraction (PVC) event in a first detection window using the received cardiac electrical information, determine a count of detected PVC events in the first detection window, remove cardiac electrical information associated with the detected PVC event from the first detection window based on the determined count of detected PVC events, and detect an indication of atrial fibrillation of the subject for the first detection window using remaining cardiac electrical information in the first detection window.

Technologies for determining a risk of developing atrial fibrillation may include a compute device. The compute device may include circuitry configured to obtain patient data indicative of an electrocardiogram to be analyzed for a likelihood that a corresponding patient will develop atrial fibrillation. The circuitry may also be configured to determine, based on the patient data and a prediction model that includes an ensemble of gradient boosted weak prediction submodels, the likelihood that the patient will develop atrial fibrillation.

The present application relates to the medical health technical field, propose a kind of atrial fibrillation detection early warning method and electrocardiogram data relay device, by training obtain a light atrial fibrillation detection model, in convolution layer alternately use conventional convolution and depth separable convolution, whether the real-time detection of collected electrocardiogram waveform is abnormal in offline state can be realized, to issue corresponding alarm prompt.The main body of the electrocardiogram data relay device is composed of main controller module, bluetooth module, OTG module, storage module, cellular network and WIFI module, power supply module, key module, LCD module, LED module, while embedding the above light atrial fibrillation detection model into main controller, and the electrocardiogram data received by bluetooth and OTG module is collected, electrocardiogram data is sent to remote server for detection by WIFI or cellular network, and the electrocardiogram data can also be placed in storage module, to reduce the workload of electrocardiogram acquisition equipment.