180 results about "Atrial fibrillation" patented technology

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.

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 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.

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 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.

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.

According to the invention, atrial fibrillation and hypertension are combined, and a novel HFpEF double-strike animal model is developed. Specifically, the animal model disclosed by the invention can stably develop to a mild structure and functional disease stage within three weeks of double strike, reaches an end stage within two subsequent weeks and gradually dies, and the animal model is highly overlapped with the pathophysiological phenotype of the existing human HFpEF. The animal model stably and rapidly develops in a staged manner, is easy to implement, and provides a valuable tool for molecular mechanism research and drug development of disease occurrence and development.

The invention relates to the technical field of electrocardiosignal quality evaluation, and discloses an electrocardiosignal quality evaluation method based on an unsupervised cascade adaptive network, which comprises the following steps: S1, preprocessing an obtained original electrocardiosignal: removing power frequency interference through adaptive notch filtering, removing high-frequency myoelectricity noise through soft threshold wavelet filtering, and obtaining a pre-processed electrocardiosignal; keeping a potential pathological characteristic waveform in a filtering process to obtain a preprocessed electrocardiosignal; s2, unsupervised multi-modal feature extraction: an unsupervised feature extraction network is constructed based on an auto-encoder; a normal electrocardiosignal distribution rule is automatically learned through an auto-encoder, so that the dependence on labeling resources of professional doctors is reduced; meanwhile, by means of a two-stage self-adaptive evaluation network, significant noise is filtered first, then pathological signals and noise are accurately distinguished, it is effectively avoided that abnormal waveforms caused by diseases such as myocardial infarction and atrial fibrillation are misjudged as noise, the retention rate of the pathological signals is remarkably increased, and more complete effective data support is provided for clinical diagnosis.

To improve the visualization of the spatial location of suspected triggers for atrial fibrillation in the electroanatomical map of the heart. [Solution] A composite correlation score associated with a selected spatial location within the heart is determined based on a plurality of individual correlation values, each based on the correlation between the signal associated with a selected electrode located at the selected spatial location and the signal associated with another of the plurality of electrodes when the selected electrode is located at the selected spatial location. Composite correlation scores associated with other selected spatial locations in the heart are determined in the same manner. An electroanatomical map of the heart is displayed depicting information representing the composite correlation scores determined for the selected spatial locations, and this information includes information indicating that the spatial location is a suspected trigger for AF.

The utility model relates to a multifunctional stethoscope, which belongs to the field of stethoscopes, and comprises a stethoscope head, one side of the stethoscope head is provided with a temperature adjusting mechanism, the temperature adjusting mechanism comprises a sound guide tube fixedly installed on one side of the stethoscope head, one side of the sound guide tube is fixedly provided with a main sound tube, the outer side of the main sound tube is fixedly provided with a mounting seat, and the mounting seat is fixedly provided with a handle. Mounting boxes are fixedly mounted on the left side and the right side of the mounting base correspondingly. According to the multifunctional stethoscope, the stethoscope head can be placed in the skin area of a patient for auscultation, the doctor-seeing comfort of the patient can be improved conveniently, the sound collector is used for capturing sound signals in the body of the patient, the amplifier is used for amplifying the sound signals, and the recording module is used for recording the amplified sound signals. The sound analyzing and processing module is used for analyzing and processing the recorded sound signals in real time, extracting key diagnosis data and displaying the key diagnosis data to a doctor through the display screen, and the display screen can display the heart rate and the respiratory rate and prompt conditions such as atrial fibrillation and valve noise, so that the doctor can listen to the conditions more clearly.

A composite correlation score associated with a selected spatial location in the heart is determined based on a plurality of individual correlation values, each of which is based on a correlation between a signal associated with a selected electrode positioned at the selected spatial location and a signal associated with another one of the plurality of electrodes when the selected electrode is positioned at the selected spatial location. Composite correlation scores associated other selected spatial locations in the heart are determined the same way. An electro-anatomical map of the heart is displayed which depicts information representative of the composite correlation score determined for the selected spatial locations, including information indicating that a spatial location is a suspected trigger of AF.

This application provides a method, apparatus, system, and computer-readable medium for classifying atrial fibrillation (AFib). One method for classifying AFib includes: receiving patient-associated electrocardiogram (ECG) data; extracting one or more RR intervals from the received ECG data; determining a prior RR interval for each extracted RR interval; and aggregating features associated with the RR intervals and the prior RR intervals. The method further includes classifying patterns associated with the aggregated features and determining that a sample of the ECG data contains patterns associated with atrial fibrillation, the sample of ECG data corresponding to one or more classified patterns.

The present disclosure provides an atrial fibrillation recurrence prediction method, system, electronic device and storage medium, the prediction method comprising: training an atrial fibrillation recurrence prediction model based on a plurality of sets of multimodal first sample training data; each set of first sample training data comprises a first number of sample intracavity ultrasound images and at least one other modal data, calculating a first contribution value of each frame of sample intracavity ultrasound image to the training of the atrial fibrillation recurrence prediction model, the matching degree between each frame of sample intracavity ultrasound image and each other modal data, and the cross-modal credibility factor of each other modal data, to determine the target contribution value of each frame of sample intracavity ultrasound image to the training of the atrial fibrillation recurrence prediction model, and obtain different target intracavity ultrasound image types; obtaining a plurality of frames of actual intracavity ultrasound images of target data and inputting them into the atrial fibrillation recurrence prediction model to obtain a target atrial fibrillation recurrence prediction result, so as to ensure the prediction efficiency and accuracy of atrial fibrillation recurrence.

The application provides an intelligent atrial fibrillation anticoagulation bleeding risk assessment system and method. The system comprises a patient data acquisition module for acquiring gene mutation site data, drug exposure data, anticoagulation activity data, clinical feature data and follow-up bleeding event data; a data checking and fusing module for quality checking, time alignment and feature fusing of the data to generate anticoagulation bleeding correlation features; a site correlation modeling module for constructing a conduction correlation model between the gene mutation site and the drug exposure offset, the anticoagulation activity offset and the follow-up bleeding event based on the anticoagulation bleeding correlation features; and a risk assessment module for determining the anticoagulation bleeding risk representation of a target atrial fibrillation patient according to the conduction correlation model, decomposing the anticoagulation bleeding risk representation into risk sources, and generating an intelligent atrial fibrillation anticoagulation bleeding risk assessment result containing risk level and risk source information. The application can improve the assessment accuracy, enhance individual identification and improve the result interpretability.

The invention discloses a diagnosis marker for diabetes complicated with atrial fibrillation and application of the diagnosis marker, and relates to the technical field of biological medicine. The diagnostic marker is hematopoietic cell kinase (HCK). The invention finds that HCK can effectively predict the occurrence risk of diabetes mellitus combined with atrial fibrillation and provide an effective treatment target for diabetes mellitus combined with atrial fibrillation. The invention verifies that the knock-down HCK can effectively inhibit AGEs-induced HL-1 cell CaMKII phosphorylation, myocardial fibrosis, mitochondrial membrane potential damage and ROS generation, and alleviates the inhibition effect on AMPK / mTOR signal channel activation, thereby reducing the atrial fibrillation susceptibility. The invention also proves that the risk (AUC is 0.853) of the diabetic with atrial fibrillation can be accurately identified by detecting the HCK level in the plasma. The invention provides a new medical approach for early risk assessment and targeted intervention of diabetes mellitus combined with atrial fibrillation.

The current methods and compositions involve treating atrial fibrillation with bucetinol after determining that the patient, including the patient with heart failure, is homozygous for Arg389 in the β1-adrenergic receptor gene.

The invention discloses an atrial fibrillation detection method, device and equipment based on non-invasive blood pressure pulse waves, and the method comprises the steps: detecting the average pressure in the pressurization process, collecting pulse wave signals under the detected average pressure, carrying out the pulse wave detection, and obtaining a candidate pulse wave sequence; performing quality analysis on the candidate pulse wave sequence to obtain an effective pulse wave sequence, performing waveform clustering on the effective pulse wave sequence to remove isolated abnormal pulse waves to obtain a target pulse wave sequence, and extracting a gap sequence and an amplitude sequence based on the target pulse wave sequence; whether a dominant interval rhythm exists in the gap sequence or not is judged based on clustering analysis, and if the dominant interval rhythm does not exist, the possibility of atrial fibrillation exists; whether a dominant amplitude trend exists in the amplitude sequence or not is judged based on clustering analysis, and if the dominant amplitude trend does not exist, the possibility of atrial fibrillation exists; if the interval deviation is larger than a first threshold value and the amplitude deviation is larger than a second threshold value, it is judged that atrial fibrillation occurs. The method can adapt to the average arterial pressure interval, improves the overall quality of the collected data, and remarkably improves the accuracy and anti-interference capability of atrial fibrillation detection.

A siRNA for inhibiting LPA gene expression, and a modifier and the use thereof, belonging to the field of biomedicine. Provided are a double-stranded RNA molecule targeting an LPA gene, and a modifier thereof. Both in vivo and in vitro experiments show that the provided double-stranded RNA molecule can effectively inhibit LPA gene expression, and can be used for preventing and / or treating Lp(a)-related diseases, such as thrombosis, atherosclerotic thrombosis, coronary artery disease, ischemic strokes, aortic valve stenosis, heart failure, atrial fibrillation, and peripheral artery disease.

The application relates to a signal detection method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: obtaining a target photoplethysmogram signal of a to-be-detected object; the target photoplethysmogram signal comprises multiple sub-signals; performing quality assessment on each sub-signal to obtain a quality assessment result; the quality assessment result comprises any one of a high-quality signal, a general signal and a poor-quality signal; according to the quality assessment results of the multiple sub-signals, performing feature extraction on the multiple sub-signals to obtain multiple feature information; each sub-signal corresponds to one feature information; and according to the quality assessment results of all the sub-signals and the multiple feature information, determining an abnormal condition of the target photoplethysmogram signal. The signal detection method provided by the application can improve the accuracy of detecting the abnormal condition of the target photoplethysmogram signal, thereby improving the accuracy of detecting whether the to-be-detected object has an atrial fibrillation event.

The current methods and compositions relate, in certain aspects, to treatment of atrial fibrillation with bucindolol in patients, including patients with heart failure, after being determined to have non-internalizing ß-adrenergic receptors.

This invention provides a method and system for predicting atrial fibrillation (AF) load based on intermittent PPG signals, belonging to the field of data processing technology. The method includes: training a first neural network using a first PPG signal with known AF results to obtain an AF probability prediction model; using the AF probability prediction model to predict the AF probability of a second PPG signal within a target time period and mapping it to an AF risk value; determining the AF probability at any given time based on the risk values ​​of two adjacent second PPG signals, integrating to obtain the total AF time, and thus obtaining the AF load; training a second neural network using historical AF loads from multiple time periods to obtain an AF load prediction model, used to predict the AF load for multiple future time periods. This invention achieves quantitative assessment and future trend prediction of AF load from discrete PPG signals to continuous AF load.

The application discloses a kind of dabigatran bleeding risk prediction methods based on machine learning, it is related to computer-aided drug risk management technical field, the method is by obtaining patient baseline and follow-up data, multiple imputation method is handled missing value;With HAS-BLED score as the basis, candidate variables are screened from potential risk factors using LassoCV algorithm, and anemia, insulin use and antifungal agent use are determined as new prediction variables through clinical correlation analysis to construct a set of prediction variables;Finally, a random forest or gradient boosting algorithm is used to train the model to output the bleeding risk assessment results of the individual to be tested.The application combines machine learning algorithm, improves the prediction accuracy of dabigatran bleeding risk in Chinese non-valvular atrial fibrillation patients, solves the problem of insufficient prediction accuracy of traditional HAS-BLED score, and provides a more reliable basis for clinical individualized anticoagulant therapy decision.

This disclosure provides a model training method and an atrial fibrillation heartbeat detection method. The method includes: inputting a target electrocardiogram (ECG) segment into a feature extraction network to output ECG features; inputting the ECG features and a first positional encoding into an encoder to output ECG encoded features; repeating the following operations until the number of iterations matches the number of decoder layers to obtain an atrial fibrillation heartbeat detection model: inputting the ECG encoded features, the first positional encoding, the decoder embedding, and the second positional encoding into the y-th decoding layer to obtain the output of the y-th decoding layer, using this output as the input to the bounding box prediction head to output the offset, and using the offset to update the predicted bounding box; inputting the classification prediction head to output the heartbeat classification prediction result; performing loss calculations on the category label and bounding box label of the target ECG segment with the heartbeat classification prediction result and predicted bounding box to obtain classification loss values ​​and bounding box loss values, thereby adjusting the model parameters of the model to be trained to obtain the adjusted model.