705 results about "Cardiac arrhythmia" patented technology

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

System and methods are described for developing a delivery plan for a pulsed field ablation device for treating an arrhythmia. A method runs a lesion development simulation based on cardiac characteristics and a treatment plan that specifies a delivery plan and a target location to generate a simulated lesion having lesion characteristics. The method initializes a three-dimensional (3D) mesh representing a heart based on cardiac characteristics. The vertices of the 3D mesh are associated with cardiac tissue characteristics with some of the associated with cardiac tissue characteristics representing an arrhythmia source. The method adjusts the cardiac tissue characteristics of vertices of the 3D mesh to reflect the effect of an ablation resulting in formation of the simulated lesion. The method runs a lesion evaluation simulation based on the 3D mesh with the adjusted cardiac tissue characteristics to determine whether the simulated lesion would be effective at treating the arrhythmia.

A method comprising shaping and joining three loop members of a catheter is disclosed herein. The method includes coupling proximal ends of each loop member to a distal portion of an elongated shaft and overlapping the first loop member, the second loop member, and the third loop member at a common distal vertex such that when the first, second, and third loop members are unconstrained, at least one of the loop members is non-coplanar with one or both of the other loop members and when the shaft is manipulated to press the loop members to a planar surface, a majority of each loop member is moved to contact the planar surface.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a multi-label arrhythmia detection method based on multi-scale space-time dynamic graph convolution, and the method comprises the steps: capturing the space-time feature information of an ECG signal through series gating time convolution and dynamic graph convolution; a feature fusion module is provided, the single-scale representation capability is enhanced through statistical feature assisted global-local feature fusion, redundant information is successfully removed through orthogonal gating multi-scale fusion, and a gating mechanism dynamically adjusts the importance of each scale feature in the feature screening process, so that the accuracy of the feature screening is improved. The sending end and the receiving end are added to further filter information, so that the risks of multi-scale feature overfitting and information loss are effectively avoided, the robustness and accuracy of the model are improved, and the situation that the most valuable information is not fully reserved during multi-scale spatial-temporal feature fusion due to the fact that redundant features are difficult to effectively distinguish in a traditional method is avoided. The method not only improves the accuracy and stability of the model, but also has high practicability, and can effectively support automatic diagnosis of arrhythmia.

The present invention relates to a non-invasive cardiac monitoring device that records cardiac data to infer a physiological characteristic of a person, such as arrhythmia or other vital signs. Some examples of the invention allow for long-term monitoring of physiological signals. Other examples allow for processing ECG data and PPG data to calculate pulse time of arrival. Some examples include a wearable cardiac monitoring device on the chest including ECG and PPG sensors for long term attachment to a mammal to detect cardiovascular signals for a long term.

The invention relates to application of an MYZAP overexpression vector in preparation of a medicine for preventing and treating MIRI, and belongs to the technical field of biological medicine. In order to solve the problem that application of gene drugs in prevention and treatment of myocardial ischemia reperfusion injury is limited, the invention provides application of an MYZAP gene overexpression recombinant vector in preparation of drugs for prevention and treatment of myocardial ischemia reperfusion injury, and the MYZAP gene overexpression recombinant vector is an adeno-associated virus vector AAV9-MYZAP for overexpressing MYZAP. Experiments prove that MYZAP overexpression can improve the cardiac function after myocardial ischemia reperfusion, reduce the occurrence rate of arrhythmia and improve the functions of sodium ion channels, potassium ion channels and calcium ion channels of myocardial cells. On the basis, the invention further provides a medicine for preventing and treating myocardial ischemia reperfusion injury, a new strategy is provided for gene therapy of myocardial ischemia reperfusion injury, and the medicine has a wide clinical application prospect.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

Systems and methods for managing machine-generated alert notifications of medical events detected from one or more patients are described herein. An embodiment of a data management system may receive an adjudication of a medical event episode including an episode characterization. A storage unit stores an association between one or more episode characterizations and corresponding detection algorithms for detecting a medical event having respective episode characterizations. An episode management circuit may detect from a subsequent episode, using the stored association, a medical event having an episode characterization of at least one medical event episode presented for adjudication, and schedule presenting at least a portion of the subsequent episode based on the detection.

The invention relates to application of an MYZAP overexpression recombinant vector in preparation of a medicine for preventing and treating arrhythmia, and belongs to the technical field of biological medicines. In order to solve the problem that application of gene drugs in prevention and treatment of arrhythmia after myocardial infarction is limited, the invention provides application of a myocardial microbelt adhesion protein MYZAP overexpression recombinant vector in preparation of drugs for prevention and treatment of arrhythmia, and the MYZAP overexpression recombinant vector is an adeno-associated virus vector AAV9-MYZAP of an overexpression MYZAP gene. In-vivo experiments prove that MYZAP overexpression can reduce the occurrence of arrhythmia after myocardial infarction by increasing the content of MYZAP protein in cardiac myocardial cells, and improve cardiac functions and cardiac electrical conduction disorders after myocardial infarction. On the basis, the invention further provides a medicine for preventing and treating arrhythmia after myocardial infarction, a new strategy is provided for gene therapy of arrhythmia after myocardial infarction, and the medicine has a wide clinical application prospect.

The invention provides an arrhythmia classification method based on multi-scale space-time learning and an adaptive selection neural network. Aiming at the problems that electrocardiosignals have different complex wave band information of various scales, pathological changes have the characteristics of transience and intermittency in time sequence, and different channels in space contain different pathological change information, a multi-scale thought is introduced in the invention to extract the complex wave band information; a multi-scale sparse time sequence attention module is designed to capture local tight and remote sparse time sequence feature information, a multi-scale residual convolution module is designed to interact and integrate spatial feature information in different channels, and an adaptive selection module is designed to dynamically select different scales and time steps. In some public data sets, the method has better classification performance and higher diagnosis speed.

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

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 an abnormal heart beat detection method and system based on a magnetic cardiogram, and relates to the technical field of medical data processing, and the method comprises the steps: carrying out the preprocessing of original magnetic cardiogram data; magnetic cardiogram features are extracted based on discrete wavelet transform, model parameters are adjusted, and an abnormal heart beat detection model is obtained; and judging whether abnormal heart beat exists or not and the position. According to the method, the problem that the magnetocardiogram data lacks a large number of heart beat level labeling samples is effectively solved, dependence on labeling data is reduced, automatic, efficient and accurate detection of abnormal heart beats is achieved, the good clinical application prospect is achieved, and powerful support can be provided for early screening, auxiliary diagnosis and risk assessment of arrhythmia.

The invention relates to a three-dimensional electrocardio scatter diagram analysis method and device based on heart rate variability and a medium, and relates to the technical field of biomedical signal processing. According to the method, the heart rate data of the user can be mapped into the three-dimensional points in the three-dimensional coordinate system after being preprocessed, the coordinate axes of the three-dimensional coordinate system are the time domain index, the frequency domain index and the nonlinear index, the heart rate change is displayed from three dimensions, compared with a traditional two-dimensional scatter diagram, the heart rate change characteristics can be more comprehensively and stereoscopically displayed, and the user experience is improved. According to the method, more potential heart disease related information is captured, heart rate data features are deeply excavated by constructing a three-dimensional electrocardiogram scatter diagram containing more heart rate change information, heart rate scatter indexes are quantified, and risk levels of heart diseases such as arrhythmia and atrial fibrillation are scientifically and accurately evaluated according to the scatter indexes. And the accuracy and reliability of heart disease risk assessment are improved.

The present invention relates to a voltage-gated sodium channel Nav1.8 selective inhibitor and the use thereof in the preparation of a related drug. The drug is used for treating diseases responsive to the inhibition of voltage-gated sodium channel NaV1.8, such as chronic pain, enterodynia, neuropathic pain, musculoskeletal pain, acute pain, inflammatory pain, cancer pain, idiopathic pain, post-operative pain, visceral pain, multiple sclerosis, Charcot-Marie-Tooth syndrome, incontinence, pathological cough or arrhythmia. Specifically, the present invention relates to a compound as shown in formula (X), and an isomer, nitrogen oxide, hydrate, solvate, metabolite, pharmaceutically acceptable salt or prodrug thereof.

The invention discloses a multifunctional cardiovascular monitoring system which comprises an electrocardiosignal acquisition module, a conduction block feature extraction module, an arrhythmia classification processing module, a cardiac function parameter calculation module, an intelligent diagnosis analysis module and a monitoring data output module. The electrocardiosignal acquisition module acquires and transmits original signals, the conduction block feature extraction module extracts feature parameters by using an exclusive algorithm, the arrhythmia classification processing module obtains classification results through a multi-dimensional classifier, and the cardiac function parameter calculation module generates calibration parameters by using a dynamic calibration model in combination with the classification results. The intelligent diagnosis analysis module generates diagnosis data through comprehensive analysis of the auxiliary platform, and the monitoring data output module processes the data and then outputs the data. The system realizes module cooperation and data interaction, improves the monitoring efficiency, guarantees data accuracy by using a professional algorithm and a model, and provides reliable support for clinical cardiovascular monitoring.

System for arrhythmia detection and confirmation includes implantable medical device (IMD) having a sensing circuit for sensing cardiac activity (CA) for one or more cardiac cycles and generating one or more CA signals. An implantable pressure sensor (IPS) includes IPS sensing circuit for sensing pressure during the one or more cardiac cycles and generating one or more pressure signals. IMD and IPS include communications circuits for communicating with each other and / or an external device. One or both of IMD or IPS includes memory for storing program instructions and processor(s) for analyzing one of the CA or pressure signals, for one or more cardiac cycles, to detect a candidate arrhythmia. In response to detecting candidate arrhythmia, the processor(s) obtain another one of CA or pressure signals for cardiac cycles corresponding to the one or more cardiac cycles, and confirm or deny candidate arrhythmia based on the other one of the signals.

Method for reconstructing ECG signals through imaging for arrhythmia detection and its detection system, wherein the method for reconstructing ECG signals through imaging for arrhythmia detection involves receiving images of the human skin, which contain color changes on the skin surface caused by heartbeats and heart rhythms. The method comprises performing noise reduction on these color changes and analyzing the color variations to extract a remote photoplethysmographic (rPPG) signal that corresponds to the heart rates and heart rhythms. Frequency domain analysis is conducted to identify the frequency features within the rPPG signal, and feature extraction is performed on these frequency characteristics to obtain a photoplethysmographic (PPG) signal. The PPG signal undergoes feature extraction to derive time domain features and frequency domain features, which are then fused to output a reconstructed ECG signal. Finally, the waveform characteristics of the reconstructed ECG signal are interpreted, allowing for the identification of classifications corresponding to arrhythmias based on these waveform features.

Described herein are implantable medical devices and systems, and methods for use therewith, for reducing T-wave oversensing and arrhythmia 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.

The present invention is directed to novel compounds that inhibit Nav1.8 sodium channels, methods of making, and methods of using thereof. The present disclosure is further directed to administering the disclosed compounds as therapeutic solutions for chronic pain, primary pain, idiopathic pain, gastrointestinal pain, neuropathic pain, musculoskeletal pain, acute pain, inflammatory pain, cancer-related pain, idiopathic pain, postoperative pain, visceral pain, multiple sclerosis, Summer-Marr-Tuff syndrome, incontinence, pathological cough, or arrhythmia to a subject in need thereof.

The invention provides an arrhythmia auxiliary diagnosis method and system fusing spatial-temporal features and physiological constraints, relates to the technical field of medical artificial intelligence, and aims to solve the problems that in the prior art, feature extraction is insufficient, spatial-temporal relevance, doctor interpretability and constraint are lacked, and a model is poor in recognition capacity for complex arrhythmia. The method comprises the following steps: acquiring an original 12-lead electrocardiosignal, and normalizing the original 12-lead electrocardiosignal; inputting the normalized electrocardiosignals into an initial convolution module to extract initial features; the initial features are sequentially input into four-stage residual error convolution modules for feature extraction, and fourth-stage features are obtained; performing adaptive average pooling on the fourth-stage features, and outputting an arrhythmia classification probability through a full-connection layer; and according to the arrhythmia classification probability, obtaining an auxiliary diagnosis result of arrhythmia. According to the method, the defects in the prior art are overcome, and the recognition capability of the model on complex arrhythmia is improved.

The invention relates to the technical field of intelligent medical treatment, in particular to an arrhythmia diagnosis system based on multi-sensor fusion, and the system comprises an electrocardiosignal module which comprises an electrocardiosignal sensor and is used for collecting electrocardiosignals; the pulse wave signal module comprises a pulse wave sensor and is used for collecting pulse wave signals; the three-axis acceleration module comprises an acceleration sensor and is used for collecting acceleration signals; the main control chip module serves as a data processing module and is used for receiving and analyzing data to obtain an arrhythmia diagnosis result; the Bluetooth module serves as a communication module and is used for transmitting the electrocardiosignal, the pulse wave signal, the acceleration signal and the arrhythmia diagnosis result to a mobile terminal; and the power management module comprises a charging circuit, a power supply circuit and a switching circuit and is used for providing power supply for the system. The system provided by the invention is stable, portable and low in power consumption, and has important significance on prevention of cardiovascular diseases and electrocardiogram monitoring of the Internet of Things.

The embodiment of the invention provides a disease dynamic prediction method and device based on multi-source factors, a medium and a program product, and relates to the field of intelligent medical treatment. Through adaptive segmentation normalization and multi-source feature fusion technologies, the heterogeneity problem of multi-source physiological data is effectively solved, and the robustness of feature expression is remarkably improved while key physiological events are reserved; according to the dynamic prediction model based on the space-time-disease association tensor and the disease collaborative gating, explicit modeling of the three-dimensional relationship of the disease category, the physiological index and the time step is realized for the first time, so that a specific physiological index time sequence mode dependent on diseases such as arrhythmia and the like is accurately captured; the accuracy of multi-label prediction is greatly improved through coding disease co-occurrence prior; in combination with a dynamic width full-connection layer and a gradient-driven adaptive optimization strategy, the model can automatically adjust a network structure and training parameters according to the complexity of input features, and the accuracy is improved.

Techniques for catheter path planning for a cardiac ablation procedure to treat an arrhythmia of a patient are provided. One technique receives data that includes a three-dimensional (3D) image of the heart of the patient and an electrocardiogram collected from the patient and that includes a catheter specification. The technique applies a path planning system to the 3D image, a location (e.g., source location or target location), and the catheter specification to generate a candidate path from a starting point to a target point that is based on the location. The technique outputs an indication of the candidate path to help inform the ablation procedure to treat the arrhythmia of the patient.

The invention discloses an arrhythmia detection method based on a pulse neural network. The method comprises the following steps: performing incremental modulation pulse coding and pooling operation on a target electrocardiosignal to obtain a corresponding pulse sequence; and inputting the pulse sequence into a trained multi-level pulse neural network classification model to obtain an arrhythmia detection result. Wherein the multi-level pulse neural network classification model comprises a weight sharing layer and a plurality of cascaded classifiers, the weight sharing layer is used for extracting electrocardiosignal feature information from the pulse sequence, and the plurality of classifiers are used for obtaining an arrhythmia detection result based on the electrocardiosignal feature information. According to the method, on the premise that the accuracy is equivalent, the classification result is more refined, and the requirements for hardware storage resources and computing resources are reduced.

An assessment of basal cardiac fitness uses noninvasively obtained physiological measures to determine the presence of a basal physiological state and cardiometric measures to determine cardiac fitness. In the presence of preload independence and cardiac vagal control, cardiometric signals are acquired that are sensitive to cardiac function and include information indicative of the opening and closing of the individual's aortic valve. The cardiometric signals are analyzed by a cardiac fitness assessment system for the determination of basal cardiac fitness. The invention assures preload independence through the assessment of observational physiological parameters or the use of physiological parameters obtained following alterations in venous return. The invention assures the presence of cardiac vagal control by assessment of measured parameters, including the presence of respiratory sinus arrhythmia. The resulting test is specific for cardiac function without dependencies on physical activities associated with heart rate elevation or respiratory capabilities.

The invention relates to an ECG arrhythmia classification method based on time-frequency attention enhancement and heterogeneous fusion driving. The method comprises the following steps: acquiring an original ECG signal; de-noising and overlapping slicing are carried out on the original ECG signal to obtain an ECG signal segment with a fixed length; performing time-frequency attention enhancement on the ECG signal segments to obtain time-frequency fusion ECG features; constructing a heterogeneous fusion network, and fusing channel information and position information into time-frequency fusion ECG features; constructing a full-connection network layer driven by heterogeneous fusion, fusing the key features extracted from different scales, and generating a prediction probability distribution vector; an arrhythmia recognition model is constructed based on time-frequency attention enhancement, a heterogeneous fusion network and a full-connection network layer driven by heterogeneous fusion, a loss function is designed, and ECG signal arrhythmia classification is achieved. According to the method, accurate ECG signal classification in a complex scene is realized, and the robustness of the model to individual difference and noise interference is enhanced.

The invention relates to the technical field of heart rate monitoring, in particular to an arrhythmia early warning system based on individual heart rate variability analysis, which comprises a feature expression acquisition module, a division module, a heart rate fluctuation rule degree acquisition module, a non-disease possibility acquisition module and a correction module, dividing a heart rate fluctuation time period in the heart rate time sequence into an increasing trend section and a decreasing trend section, determining a main change trend of the heart rate fluctuation time period, and obtaining the heart rate fluctuation rule degree of the heart rate fluctuation time period according to the association between the increasing trend section and the decreasing trend section in combination with the characteristics of the main trend section. The non-disease possibility of the heart rate fluctuation time period is obtained based on the characteristic expression and the heart rate fluctuation rule degree of the heart rate fluctuation time period, and reverse correction is performed on the heart rate variability of the heart rate fluctuation time period according to the non-disease possibility, so that the credibility of the heart rate variability can be improved, and the accuracy of the heart rate variability is ensured; therefore, the accuracy of early warning of arrhythmia of the patient is improved.

The present disclosure presents arrhythmia analysis systems and methods. One such method comprises processing an acquired ECG waveform signal to remove noise artifacts and form a denoised ECG waveform signal; processing the denoised ECG waveform signal to remove low quality segments and form a high quality ECG waveform signal; analyzing the high quality ECG waveform to detect a presence of a beat-independent ventricular arrhythmia; processing the denoised ECG signal to extract beat (R-peak) locations corresponding to QRS complexes from the denoised ECG signal; analyzing the denoised ECG signal to detect a presence of a beat-dependent ventricular arrhythmia based on the extracted beat (R-peak) locations; analyzing the denoised ECG signal to detect a presence of one or more supraventricular arrhythmias based on the extracted beat locations of the ECG signal; and outputting a report containing one or more arrhythmias detected by the analyzing steps. Other methods / systems are also provided.