112 results about "Heart rate rhythm" patented technology

The invention relates to an intelligent heart electrophysiological signal analysis and Jiangshi connecting line optimization system and method, and belongs to the technical field of electrocardio systems, the system comprises a plurality of modules for cooperative work, a data acquisition module is used for acquiring human body 12-lead electrocardiogram and intracardiac electrogram signals and executing a pace-making stimulation function; the signal preprocessing module filters received signals, the feature extraction module extracts time domain, frequency domain and nonlinear features, the deep learning module identifies abnormal heart rhythms by using a pre-training model, the parameter tuning module dynamically adjusts model parameters and optimizes and analyzes the parameters, and the Jiangshi connecting line optimization module improves signal quality. The intelligent monitoring module automatically monitors multiple indexes, the display interface module visually displays an electrocardiogram, the system compatible module is connected with multiple devices, and the control module coordinates all the modules to ensure efficient operation of the system.

The invention provides a health pre-assessment method and system based on information monitoring, and the method comprises the steps: obtaining ECG data of a user, analyzing a target ECG waveform, extracting heart electrophysiological parameters which comprise a heart rate, a heart rate variability standard deviation and a heart rate rhythm, recognizing a heart rate feature mode of the user, and carrying out the health pre-assessment of the user according to the heart rate feature mode. The periodic change of the electrocardio effect is evaluated, and heart rate characteristic mode information is obtained; based on the heart rate characteristic mode information, multiple physiological parameters of the user are collected in real time, the mutual influence relation between the multiple physiological parameters and heart mode measurement values is analyzed, the heart regulation efficiency of the user is evaluated, and a physiological correlation analysis result is obtained in combination with the correlation coefficient. Through the method and the corresponding system, important risks such as health deterioration and subclinical abnormality can be found in advance, and continuous and decision support is provided for clinical decision and individualized health management.

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.

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

A cardiac rhythm condition recognition method based on a seismocardiogram signal comprises the steps that 1, the seismocardiogram signal and a gold standard electrocardiogram signal are collected, and filtering and standardization preprocessing are carried out on the seismocardiogram signal and the gold standard electrocardiogram signal; step 2, automatically labeling the heart rhythm condition based on the gold standard electrocardiogram signal: extracting at least one heart rhythm feature through an electrocardiogram analysis algorithm, and labeling the heart rhythm condition of the electrocardiogram signal based on a multi-evidence fusion labeling rule; step 3, mapping the labeling result of the electrocardiogram signal to the synchronous seismocardiogram signal: mapping the electrocardiogram labeling result to a corresponding time window of the synchronous seismocardiogram signal through a time alignment and priority mapping strategy, and endowing each seismocardiogram signal time window with a heart rhythm condition label; 4, constructing a deep learning model fusing a residual convolutional neural network and a long-short-term memory network, and training by using the marked seismocardiogram signal data; and 5, training model deployment and heart rhythm condition real-time identification.

The invention relates to a multi-sensor fused vital sign and motion posture monitoring system and method. The system comprises a photoelectric volume pulse wave acquisition module, an electrocardio acquisition module, an inertia measurement module, a synchronization module, an artifact suppression module, a contact state recognition module, a data quality evaluation module, a vital sign extraction module and an anomaly detection module. Synchronously acquiring a photoelectric volume pulse wave signal, an electrocardiosignal and motion information; suppressing motion artifacts in the photoplethysmography signal and / or the electrocardiosignal based on the motion information; identifying the contact state and generating a data quality indicator related to the measurement reliability in combination with the de-artifact physiological signal; when the data quality meets a preset condition, heart rate, oxyhemoglobin saturation and / or breathing information are extracted, and vital sign parameters are formed; and carrying out conjoint analysis on the artifact-removed physiological signal and the motion information, and marking heart rate rhythm abnormity and / or posture abnormity. According to the invention, the reliability and availability of vital sign monitoring can be improved.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

Systems, devices, and methods provide up-to-date defibrillation shock recommendations. In an example method, multiple segments of an electrocardiogram (ECG) of an individual are detected from an individual receiving chest compressions. The multiple segments are evaluated to determine whether the individual is exhibiting a shockable heart rhythm. A medical device outputs a recommendation indicating whether a defibrillation shock is advised based on the most recent determination of the individual's heart rhythm. For example, the medical device outputs an up-to-date recommendation on-demand in response to an input signal from a user. In some examples, the medical device updates the recommendation based on ongoing analysis of the ECG.

The invention discloses an electrocardiosignal anomaly classification method and device. The method comprises the following steps: receiving a multi-lead electrocardiosignal data set acquired by multi-lead acquisition equipment; aiming at each lead electrocardiosignal sequence, constructing a lead internal graph structure for representing the internal rhythm change of the electrocardiosignal waveform; based on waveform synchronism and correlation between different leads, an external graph structure between the leads is constructed; further, based on the lead internal graph structure and the lead external graph structure, determining an external distribution mode of the electrocardiosignals under different physiological states or individual differences; fusing the graph structure and the external distribution mode to generate global graph representation of the electrocardiosignal; and inputting the graph representation into a classifier to obtain an abnormal heart rhythm classification result. According to the method, the local waveform morphological characteristics, the cross-lead correlation characteristics and the distribution change caused by cross-crowd difference of the electrocardiosignals can be modeled at the same time, so that the accuracy, the robustness and the cross-subject generalization ability of an electrocardiograph anomaly classification task are effectively improved.

To provide suitable devices, systems, and methods for percutaneous pneumatic cardiac support. [Solution] The cardiac support system includes a pneumatic effector implanted beneath the pericardium and across the myocardial surface covering the patient's left ventricle. A port receives an implanted, percutaneously introduced cannula. The port is connected to supply the drive gas received from the cannula to the pneumatic effector. An external drive unit includes a pump assembly and a control circuit that operates the pump to activate the pneumatic effector in response to the patient's sensed cardiac rhythm. A connecting tube has a pump end connected to the pump and a percutaneous port connection end attached to the implantable port.

A card capable of measuring ECG signals includes a card body, a control unit, an electronic control unit, a conduction unit, an ECG signal processing unit, a storage unit, a link unit and a light source processing unit. The card allows a person to be tested to touch the conduction unit simultaneously with the fingers of both hands, so that a closed circuit is formed between the heart of the person to be tested, the arms of the person to be tested, the conduction unit and the ECG signal processing unit, so as to carry out the measurement of the heart rhythm, the ECG data generated after measurement is stored in the storage unit, the reading device reads the ECG data through the link unit, relevant signals are generated to the light source processing unit for a light source demonstration during the measurement process and after measurement.

The invention relates to an electrocardiosignal processing method, electronic equipment, a medium and a product, and the electrocardiosignal processing method comprises the steps that an electrocardiosignal is obtained, the heart rate type to which the electrocardiosignal belongs is recognized through a preset model, and probability information of the heart rate type to which the electrocardiosignal belongs is determined to obtain a processed electrocardiosignal, the probability information comprises a probability value of the heart rhythm type to which the electrocardiosignal belongs, and obtaining the heart rhythm type to which the processed electrocardiosignal belongs and the probability information of the heart rhythm type to which the processed electrocardiosignal belongs. According to the invention, the wearable device prompts the user of the possibility of the heart rhythm type to which the obtained electrocardiosignal belongs in a probabilistic manner, so that the misjudgment and false alarm of the heart rhythm type to which the obtained electrocardiosignal belongs due to the limitation of the hardware condition or the use condition of the electronic device are effectively avoided; panic and pressure of customers caused by false alarms are avoided, and user experience is improved.

The wearable device obtains heart rate data of a user; and determines a first feature and a second feature of the heart rate data based on the heart rate data and a Poincare plot. The first feature is a standard deviation of a distance from an origin of the Poincare plot to a first straight line, and the second feature is a standard deviation of a distance from the origin of the Poincare plot to a second straight line. The wearable device inputs the target feature to an identification model, to obtain an identification result. The identification model is configured to identify, based on the input, whether a heart rhythm is abnormal.

The invention provides a millimeter-wave radar heart rhythm detection method based on comparative learning. The millimeter-wave radar heart rhythm detection method comprises the steps that S1, echo signals generated after millimeter-wave radar signals act on a to-be-detected object are acquired; s2, based on the obtained echo signals, reflected signals from the chest area in the three-dimensional space are extracted; s3, weakening the influence of respiratory movement based on the phase change of the reflected signal to obtain a heart mechanical activity signal; s4, creating a model, pre-training the heart mechanical activity signal based on comparative learning, and training the ability of a feature extractor in the model to extract features related to heart mechanical activity; s5, on the basis of the feature extractor obtained through training, heart rhythm feature tags are added for fine adjustment, and model training is completed through a loss function; s6, heart mechanical activity signals of the to-be-tested object are input into the trained model, and a heart rhythm classification result of the to-be-tested object is obtained.

An insulin delivery device includes an insulin injection device in communication with a controller for controlling the insulin injection device. The controller is configured to receive a heart signal from one or more heart sensors, and a blood glucose signal from one or more blood glucose sensors. The controller is further configured to analyze changes in the heart rhythm of the subject based on the heart signal and determine, based on the changes in the heart rhythm and the blood glucose signal, whether the subject is and / or will be experiencing an adverse event. Upon determination that the subject is or will be experiencing an adverse event, the controller determines one or more parameters of delivery of insulin to be delivered to the subject. Finally, the controller is configured to control the injection device to deliver insulin to the subject in accordance with the determined one or more parameters of delivery.

The invention discloses an AI accompanying toy and method based on an emotional acoustic wave algorithm, and relates to the technical field of man-machine interaction, a core processing layer establishes an independent virtual heart rate system for each AI role, and generates a personalized basic heart rate and an emotional intimacy initial value according to role attributes; dialogue content and AI emotional state are analyzed in real time based on data acquired by the input layer, and emotion type and intensity are identified; calculating a target heart rate and a spectrum modulation parameter according to the emotional state; dynamically adjusting parameters according to the interaction time, the dialogue depth and the emotion intimacy; the heart rate rhythm and spectrum modulation are applied to AI voice in real time; and the output layer presents the fused information of the emotional sound wave and the emotional state in a graph or color mode. Therefore, personification and life feeling of the AI role are greatly enhanced, man-machine interaction is upgraded from simple information exchange to accompanying experience with emotional depth and physiological resonance, and interaction naturalness and emotional attachment of the user are remarkably improved.

1. Name of the product in this design: Veterinary stethoscope. 2. Purpose of this design: To determine whether an animal's heart has murmurs, abnormal heart rhythms, etc. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

A defibrillator (200) employing a shock delivery circuit (207) and a defibrillation controller (204) for advising an administration of a cardiopulmonary resuscitation procedure on a heart of a patient by defibrillator (200) in accordance with a rescue protocol including a cardiopulmonary resuscitation protocol and a shock protocol by defibrillator (200). During an ongoing execution of the cardiopulmonary resuscitation protocol by defibrillator (200), the defibrillation controller (200) determines a probable rescue outcome of the heart of the patient or an improbable rescue outcome of the heart of the patient from a shockable cardiac rhythm of an electrocardiogram of the heart of the patient. Based on a determination of the probable rescue outcome of the heart of the patient, the controller (204) pretimely terminates the ongoing execution of the cardiopulmonary resuscitation protocol by the defibrillator (200) and initiates a succeeding execution of the shock protocol by the defibrillator (200) for the shock delivery circuit (207) to deliver the defibrillating shock to the heart of the patient.

This invention discloses a timing device that integrates multiple emergency operation buttons and generates structured medical records in real time, belonging to the field of emergency medical technology. It includes an intelligent terminal recording module, a data integration and communication module, and an intelligent processing and output module. These three modules work together to achieve real-time recording of emergency operations, integrated data transmission, and automatic generation of structured medical records. The intelligent terminal recording module is a dedicated emergency recording terminal equipped with a timing chip and a multi-touch screen. The touch screen interface is dynamically designed according to the ACLS / BLS resuscitation process and features one-button recording for various emergency operations such as chest compressions, bag ventilation, defibrillation, heart rhythm analysis, and adrenaline administration. The timing chip provides millisecond-level timestamps for emergency operation records, enabling precise binding of operation content to the time of occurrence. This significantly reduces the need for clinical manpower, eliminating the need for a dedicated recording nurse through an "operation-as-record" model, and also eliminating the need for manual re-entry of medical documents after resuscitation.

Resorbable implants comprising poly(butylene succinate) and copolymers thereof have been developed. The implants implants are preferably sterilized, and contain less than 20 endotoxin units per device as determined by the limulus amebocyte lysate (LAL) assay, and are particularly suitable for use in procedures where prolonged strength retention is necessary, and can include one or more bioactive agents. The implants may be made from fibers and meshes of poly(butylene succinate) and copolymers thereof, or by 3d printing, and the fibers may be oriented. Coverings and receptacles made from forms of poly(butylene succinate) and copolymers thereof have also been developed for use with cardiac rhythm management devices and other implantable devices. These coverings and receptacles may be used to hold, or partially / fully cover, devices such as pacemakers and neurostimulators. The coverings and receptacles are made from meshes, webs, lattices, non-wovens, films, fibers, and foams, and contain antibiotics such as rifampin and minocycline.

The application discloses an emergency monitoring system and method integrating vital sign monitoring and automatic defibrillation applied to the field of medical devices, which first fuses three vital sign data of heart rate, blood pressure and blood sugar with AED heart rhythm analysis data, reduces single data misjudgment rate through an AI algorithm, realizes accurate prediction of high risk of cardiac arrest, realizes whole-process automation of'monitoring-prediction-defibrillation-help', compresses the golden 4-minute rescue time to 1-2 minutes, and greatly improves the survival rate of cardiac arrest patients; voice guidance+one-key SOS operation, non-professionals can quickly complete first aid, and the risk of operation failure is avoided; a multi-vital sign fusion algorithm is combined with AI technology, single data misjudgment rate is reduced, and high risk of cardiac arrest is predicted in advance; data is synchronously transmitted to a 120 dispatch center through a whole-link, patient information and position are acquired in advance, and the response time of first aid personnel is shortened; automatic self-checking and double power supply mode ensure that the equipment is available all day long, and equipment failure, loss and other problems are avoided.

The invention discloses a real-time heart rhythm monitoring and early warning system and method based on intelligent recognition, and relates to the field of heart rhythm monitoring, and the system comprises a data collection module which is used for obtaining heart rhythm original data, body temperature data and motion data including real-time rotation, running and jumping actions of a user in real time; the heart rhythm analysis module is used for analyzing the heart rhythm data in the current monitoring period, identifying and extracting characteristic parameters of heart rhythm fluctuation and judging whether abnormal fluctuation exists or not; the attention management module is used for starting an attention period when the abnormal fluctuation is identified, and judging whether the accumulated number of times of the abnormal fluctuation triggers strong attention or not; according to the method, physiological cardiac rhythm acceleration and pathological arrhythmia caused by movement are effectively distinguished, the false alarm rate is greatly reduced, the overreaction of single accidental fluctuation is avoided, it is ensured that early warning aims at a continuous abnormal mode, an early warning signal output by the system has higher specificity and clinical reference value, and unnecessary psychological burden of a user is relieved.

Embodiments of a wearable cardioverter defibrillator (WCD) system include a support structure for wearing by an ambulatory patient, a posture detector and at least one processor. When worn, the support structure maintains electrodes on the patient's body, and using the posture detector and the patient's ECG received via the electrodes, the processor determines the patient's posture, formulates posture-based templates of QRS complexes, and the patient's heart rate. The processor can use these determinations to distinguish between VT and SVT and make no-shock, and shock decisions.

The application discloses a liquid metal-silver-porous flexible substrate patterned circuit and method for flexible wearable electrocardio monitoring. A flexible substrate is porous styrene-butadiene-styrene block copolymer fiber membrane; after the porous flexible substrate is prepared through electrospinning, the substrate is placed in a silver solution to fully attach silver ions, then dried, and a method combining area selective reduction and electrochemical plating enhancement is used to construct a high-conductive silver microcircuit through a printing plate, then eutectic gallium-indium liquid metal is introduced to be adsorbed in the silver conductive network, and a liquid metal-silver composite patterned circuit with flexibility, air permeability, stretchability and self-repairing performance is obtained. The product has excellent conductivity, mechanical stability and skin adhesion, can be integrated with high-density electronic components, applied to wearable electrocardio monitoring devices, realizes high-fidelity acquisition of heart rate and complete electrocardio signal waveform, and can be used for detection and diagnosis of complex heart rhythm.

The method is for treating an individual having atrial fibrillation (AF) with administration of oral magnesium supplementation for converting atrial fibrillation to sinus rhythm. The method includes monitoring cardiac rhythms frequently (e.g. daily), importance of remaining hydrated, adding potassium if needed, and administering a dose of oral magnesium supplements for at least four to six weeks, for example. The method includes titrating the dose of magnesium supplements until conversion of atrial fibrillation to sinus rhythm, and additional titrating of the dose of magnesium supplements based upon recurrence of atrial fibrillation, until conversion of atrial fibrillation to sinus rhythm. Monitoring ‘Heart Rate Variability” in certain circumstances may forecast the presence or recurrence of atrial fibrillation.

A defibrillator using an ECG analyzer and a shock impedance advisor for improving defibrillation of a patient's heart by a defibrillation discharge circuit associated with the defibrillator. In operation, the ECG analyzer derives a shock delivery decision from detecting a shockable rhythm in the patient's ECG waveform in response to a shock delivery decision, and the shock impedance advisor measures the non-shock impedance of the defibrillation discharge circuit, estimates a pre-shock impedance of the defibrillation discharge circuit from the measured non-shock impedance of the defibrillation discharge circuit, and communicates a lower shock impedance advisory to a responder operating the defibrillator when the pre-shock impedance of the defibrillation discharge circuit exceeds a shock impedance threshold.

A defibrillator for customizing cardiac therapy treatment of a patient while a responder is administering cardiopulmonary resuscitation (CPR) to the patient's heart. During operation, the defibrillator monitors an electrocardiogram (ECG) waveform of the patient's heart while the responder is administering CPR to the patient's heart. When the ECG waveform monitored by the defibrillator while the responder is administering CPR to the patient's heart indicates a shockable rhythm of the patient's heart, the defibrillator derives a treatment advisory from the quality of the CPR administered to the patient's heart by the responder, the vitality of the shockable rhythm of the patient's heart, and optionally other factors.

The invention relates to a pectoral girdle type physiological monitoring method for monitoring air arrhythmia and flight fatigue, belongs to the field of aeronautical medicine and machine learning, and solves the problem that the existing method cannot accurately monitor the air arrhythmia and the flight fatigue in real time. Comprising the steps that physiological data of a pilot are obtained in real time through chest strap type monitoring equipment, and the physiological data comprise single-lead electrocardiosignals, oxyhemoglobin saturation and respiratory wave signals; obtaining a Scalogram wavelet scale map and an HRV (High Rate Velocity) feature on the basis of the preprocessed electrocardiosignal; the respiration rate, the respiration amplitude and the respiration irregularity are obtained based on the respiration wave signals; inputting the Scalogram wavelet scale diagram into the trained heart rhythm classification model to obtain the heart rhythm grade of the pilot; inputting the HRV features, the breathing rate, the breathing amplitude, the breathing irregularity and the oxyhemoglobin saturation into a trained fatigue classification model to obtain a pilot fatigue level; based on the pilot heart rhythm level and the fatigue level, the comprehensive risk level of the pilot is obtained through double-layer threshold value fusion and used for evaluating the physiological state of the pilot.

The invention discloses an electrocardiosignal identification method based on a diffusion model and Bi-LSTM (Bidirectional Long Short Term Memory). The method comprises the steps that firstly, an electrocardiosignal is preprocessed, and wavelet transform is used for conducting noise reduction on the electrocardiosignal; then learning real electrocardiosignal data characteristics by using a diffusion model; the method comprises the following steps of: firstly, acquiring heart beat data, then utilizing a convolutional neural network and a Bi-LSTM feature extractor to learn heart beat features, finally, inputting the learned features into a full connection layer, and then utilizing softmax to obtain the probability that the heart beat data belong to a corresponding category, thereby realizing classification of electrocardiosignals. Finally, in order to detect the heart state of the patient in real time, the fully trained classification model is stored and migrated to a cloud end, then signals, collected by electrocardiosignal collecting equipment in real time, of the patient are input into the model, whether the heart rhythm of the patient is normal or not is judged, a result is fed back to a user, and therefore the patient can conveniently see a doctor and treat the patient in time.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.