200 results about "Electrocardiography" patented technology

The invention discloses an electrocardiogram waveform real-time anomaly detection and early warning evaluation method and system, and the method comprises the following steps: S1, collecting the signal data of an electrocardiogram, and carrying out the preprocessing; s2, inputting the electrocardiogram segment sequence into an improved convolutional neural network, and performing feature extraction and aggregation; s3, inputting the fusion feature sequence into a graph attention neural network, and extracting a relation between waveform local dependence and fragment state transition; s4, inputting the global feature map into an anomaly evaluation network, and outputting an anomaly judgment result and a confidence score sequence; s5, generating a risk level tag sequence in combination with the individual parameters; s6, inputting the risk level label sequence into an early warning decision network, and generating a multi-level response label and an alarm trigger signal; and S7, controlling an early warning interface to execute alarm, and synchronously writing related data. According to the invention, real-time anomaly detection and multi-stage early warning response of the electrocardiogram waveform are realized, and the timeliness and reliability of abnormal heart rhythm recognition are improved.

The invention provides an electrocardiograph electromagnetic compatibility test data diagnosis method based on an intelligent sensor, and relates to the technical field of electrocardiograph electromagnetic compatibility test. The method includes generating structured test data. And calculating a theoretical interference reference threshold. And generating an initial interference judgment atlas arranged according to the interference time sequence. And constructing a dynamic compensation coefficient matrix, and injecting the dynamic compensation coefficient matrix into the initial interference determination map to obtain a dynamic interference determination map. And calculating the characteristic similarity between the real-time test data in each interference time sequence window and the dynamic interference judgment map, and diagnosing the electromagnetic compatibility state of the electrocardiograph. According to the invention, through multi-dimensional data acquisition and interference identification, through a dynamic reference threshold and a compensation mechanism, environment and equipment state changes are adapted, the diagnosis stability of a complex scene is improved, through real-time data comparison and traceability, the equipment state is rapidly judged, the interference source is positioned, the debugging time is shortened, and finally the measurement accuracy of the electrocardiograph is guaranteed.

The invention discloses a non-contact ECG signal monitoring method based on a millimeter wave radar, belongs to the field of ECG signal monitoring, realizes high-precision electrocardiogram reconstruction by simultaneously sensing chest vibration caused by heartbeat and carotid artery pulsation from the neck and fusing spatial-temporal characteristics, and solves the problems that a traditional scheme depends on electrodes and an existing radar technology cannot recover complete ECG. According to the method, non-contact ECG signal monitoring is carried out in a daily scene, and good privacy and comfort are achieved.

Embodiments of methods and systems for determining electrocardiogram (ECG) depth of electrical activity. Electrical activity from a plurality of electrodes of a catheter is received; and within the electrical activity, an electrical signal originating from a depth within cardiac tissue is identified. In some embodiments, spatial electrode signal analysis of the electrical activity may be performed for each electrode of the plurality of electrodes. In some embodiments, a linear and / or non-linear combination of signal components within the electrical activity may be calculated. In some embodiments, a neural network may be provided with the electrical signals and may determine an estimated distance to the nearest activation for at least one of the plurality of electrodes. A visualization of the identified electrical signals at a specified depth within the cardiac tissue may be provided for display.

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

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.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

A method for cardiac diagnostic analysis is provided. The system includes a processor and memory coupled to the processing unit, receiving data from standard 12-lead ECGs from an external device; at least one computer-readable storage medium containing instructions, including program code that causes the execution of procedures according to the programs encoded on a disk or other media capable of using advanced algorithms such as deep learning networks, so that they can be processed during any given procedure without substantially altering the existing clinical workflow.

The invention discloses an electrocardio data processing and coronary heart disease risk prediction method, device and system and a medium. The method comprises the steps of obtaining exercise electrocardio data output through at least one electrocardiogram lead; analyzing the exercise electrocardiogram data to obtain a heart rate sequence corresponding to each electrocardiogram lead; extracting a QRS wave group corresponding to each heart beat from the exercise electrocardio data; dividing a plurality of heart rate intervals according to the obtained heart rate sequence; according to the heart rate interval to which the heart rate corresponding to each QRS wave group belongs, classifying the QRS wave group into a group corresponding to the corresponding heart rate interval; determining an average QRS wave group corresponding to each heart rate interval according to all the QRS wave groups in each group; extracting the high-frequency energy of the average QRS wave group corresponding to each heart rate interval; and determining an energy sequence according to the high-frequency energy corresponding to each heart rate interval. According to the scheme, high-frequency components and dynamic load information which are more sensitive to ischemia are utilized, so that the detection rate and the identification capability of coronary heart diseases, particularly early and mild lesions, can be remarkably improved.

The invention discloses an emotion classification method and system for heart electromagnetic signals of heart rate variability based on topology analysis, and relates to the technical field of biomedical signal processing and emotion calculation, and the emotion classification method comprises the following steps: preprocessing magnetocardiogram signals or electrocardiogram signals; positioning R waves, extracting an RR interval sequence, and extracting statistics of topological feature points of the persistent graph as topological features; and performing fusion through the time sequence branch, the topological branch and the fusion layer to generate a fusion vector, and classifying the emotional state based on the fusion vector. According to the method, the signal quality and the anti-interference capability are remarkably improved, topological data analysis (TDA) is innovatively introduced, quantitative characterization of a heart rate variability nonlinear dynamic structure is achieved, and the capturing capability of the model to a key physiological mode is enhanced; the problem of overfitting under small samples is effectively relieved, the accuracy, robustness and cross-individual generalization ability of emotion classification are remarkably improved, and a new method is provided for emotion calculation.

A wearable device (100) for monitoring cardiac function during apnoeic episode. Wearable device including ballistocardiography (BCG) sensor (102A) to measure mechanical forces generated by heart and respiratory movement; electrocardiography (ECG) sensor (102B) to measure electrical activity of heart; and processor (102C) operatively connected to BCG sensor and ECG sensor to generate BCG data from measured mechanical forces and ECG data from measured electrical activity, respectively. Processor configured to: filter respiratory signals and heart signals from BCG data; detect apnoeic episode based on low respiratory signals or absence of respiratory signals for predetermined time period in filtered BCG data; determine compensatory mechanical output of heart using at least one of filtered BCG data or ECG data for apnoeic episode, and predict risk of cardiac condition based on compensatory mechanical output during apnoeic episode. Also a system (300) and method thereof.

A dual-mode measurement body fat scale disclosed by the present invention comprises a scale body and a handle, the scale body is provided with a first power supply module, a first main control module, a foot electrode module and a weighing module, the handle is provided with a second power supply module, a second main control module and a hand electrode module, the foot electrode module comprises four foot electrodes, and the weighing module comprises a first main control module and a second main control module. The hand electrode module comprises four hand electrodes and further comprises an ECG measuring module and a channel switching module. The channel switching module is connected with the hand electrode module and has a first state and a second state; the function reuse of the hand electrode module is realized through the channel switching module, and when the channel switching module is in a first state, the hand electrode is connected with the first main control module and is matched with the foot electrode to form a complete body fat measuring loop; when the module is switched to the second state, the hand electrode is connected with the ECG measurement module to form an ECG measurement loop, electrocardiogram data of a user can be conveniently collected, and the cardiovascular health monitoring requirement is met.

A system and method for determining and modifying the electromagnetic activity of a patient's heart includes a sensor array, a controller, software, and a stimulator array. The sensor array includes one or more sensors, such as an ECG electrode, that detect the electromagnetic activity of a patient's heart. The sensor array is in data communication with the controller. The controller runs software that outputs in human-format sensor data provided by the sensor array. The software receives data from the controller, analyzes the data, presents the data to the user, and sends the data to the stimulator array via a data communication link. The stimulator array includes one or more electromagnets and at least one or more light-emitting diodes. The stimulator array emits an electromagnetic (and / or light) field to modify the electromagnetic activity of a patient's heart based on the data it receives from the controller. The system can be portable.

A wearable defibrillator for use in dry and non-dry locations includes a harness, ECG sensing electrodes, therapy electrodes configured to deliver defibrillating shocks, and a user interface pod configured to be attached to the harness. The user interface pod includes a speaker configured to provide an audible alarm and one or more buttons configured to be pressed by the patient to indicate the patient is conscious. The wearable defibrillator also includes a waterproof or water-resistant control unit configured to determine whether the patient is experiencing a cardiac arrhythmia, issue the audible alarm, and deliver the defibrillating shocks. The wearable defibrillator further includes a connection pod configured to electrically couple the therapy electrodes to the control unit. The user interface pod is configured to be electrically coupled to the control unit via a waterproof removable connector, which forms a water-tight seal with the control unit when connected to the control unit.

The invention relates to the technical field of medical instruments, in particular to a heart health state assessment method, a processing device, a system and a computer readable storage medium. Motion electrocardiogram data, resting electrocardiogram data and conventional electrocardiogram data output by electrocardiogram leads are obtained and analyzed to determine the possibility of myocardial ischemia, coronary artery lesion, myocardial bridge and coronary microcirculation disturbance, and the heart health state is qualitatively evaluated according to the determination result. And under the condition that the possibility of myocardial ischemia and / or coronary artery lesion exists is determined, quantitative evaluation is carried out on myocardial ischemia and / or coronary artery lesion, and finally the heart health state is evaluated according to the qualitative evaluation result and the quantitative evaluation result. According to the technical scheme provided by the embodiment of the invention, the heart health state can be more accurately qualitatively and quantitatively analyzed by combining the exercise electrocardio data, the resting electrocardio data and the conventional ECG data.

Wearable electrocardiogram (ECG) systems configured to record ECG from ears (auricular ECG systems), fingers (ear-finger or finger-finger ECG systems) or toes, comprising a processing unit configured to analyze the ECG data to detect serious cardiac arrhythmias or impending serious cardiac arrhythmia. Automatic anti-arrhythmia systems comprise one of the ECG systems and a transcutaneous auricular vagus nerve stimulation (taVNS) unit. When serious cardiac arrythmias are detected, the anti-arrhythmia systems may automatically send pre-determined electric stimuli to a vagus nerve to alleviate or stop the cardiac arrhythmias. The ECG electrodes and the stimulating electrode of the taVNS unit are located on a surface of a housing structure made of flexible elastic and adaptable material such that these electrodes will be naturally and snugly contacting the wearer's skin. These wearable ECG and anti-arrhythmia systems are suitable for easy self-installation, self-removal and daily ambulatory use.

A method, device, system and medium for electrocardio data processing and coronary heart disease risk prediction, motion electrocardio data output through at least one electrocardio lead is acquired; the motion electrocardio data is analyzed to obtain a heart rate sequence corresponding to each electrocardio lead; each QRS complex corresponding to each heartbeat is extracted from the motion electrocardio data; a plurality of heart rate intervals are divided according to the obtained heart rate sequence; each QRS complex is classified into a group corresponding to a heart rate interval according to the heart rate interval to which the heart rate corresponding to the QRS complex belongs; the average QRS complex corresponding to each heart rate interval is determined according to all QRS complexes in each group; the high-frequency energy of the average QRS complex corresponding to each heart rate interval is extracted; and an energy sequence is determined according to the high-frequency energy corresponding to each heart rate interval. The present scheme uses high-frequency components and dynamic load information which are more sensitive to ischemia, and can significantly improve the detection rate and discrimination ability for coronary heart disease, especially early and mild lesions.

The invention discloses a portable state analysis method and system based on multi-mode electroencephalogram and electrocardio, and belongs to the field of state analysis. The portable state analysis method comprises the steps that electroencephalogram signals and electrocardio signals of a subject are synchronously collected; performing preprocessing and feature extraction on the signals to obtain electroencephalogram features and electrocardio features; according to the values of the electrocardio characteristics in different physiological states, constructing difference value characteristics and ratio characteristics representing differences between the states; the electroencephalogram features, the electrocardio features and the new construction features are fused, and multi-modal features are obtained; and finally, inputting the fusion features into a pre-trained table priori data fitting network model, and outputting a classification result or a quantitative prediction value for representing the state of the subject. According to the method, through systematic state comparison feature engineering and an efficient table data model, the accuracy, generalization ability and automation level of multi-modal physiological signal analysis are improved, and the whole scheme is realized based on portable equipment and is suitable for state evaluation of various non-laboratory scenes.

Electrocardiography (ECG) data acquired from a subject and processed with a machine learning model to determine whether the subject has a particular cardiac condition, such as a cardiac arrhythmia, myocardial ischemia and / or myocardial infarction, or the like. The machine learning model generates classified feature data that are indicative of the presence and / or likelihood of a particular cardiac condition.

The present invention relates to a smart ring device for electrocardiography. The smart ring device comprises: a first electrode which is in contact with a finger of a user while worn on the finger; an insulating spacer surrounding the first electrode; a circuit board which is disposed outside the insulating spacer and connected to the first electrode; an arc-shaped insulating cover covering the circuit board; a second electrode which is exposed to the outside of the insulating cover while connected to the circuit board and can come into contact with the body of the user; a third electrode which is coupled to the outside of the insulating spacer and connected to the circuit board, and can come into contact with the body; and a signal processing unit which is mounted on the circuit board and processes biosignals transmitted from the first, second, and third electrodes. The smart ring device for electrocardiography of the present invention having the above configuration is in the form of a ring and thus very convenient to carry, and in particular, can simultaneously collect electrical signals from three different parts of the body through the three electrodes, and thus is able to obtain more accurate electrocardiography data.

The present invention relates to a detection and alert system for a garment (G) and an application module (APP) which comprise means for triggering an on-demand ECG recording, the application module (APP) being configured - to allow the storage of personal and medical data of the patient, as well as of at least one prior reference ECG recording, - to transmit, to a remote interface module (MD), an alert message (ME) containing thus stored personal data and medical data, - the reference ECG recording also stored, and - the on-demand ECG recording that has been performed.

This application relates to the field of electrocardiography (ECG), specifically providing an ECG electrode and its preparation method. The electrode includes a substrate layer, a reservoir cavity on one side of the substrate layer, and a gel portion inside the reservoir cavity. A carbon-based fabric electrode layer is fixedly disposed on the side of the substrate layer with the reservoir cavity, and the carbon-based fabric electrode layer is bonded and fixedly connected to the substrate layer at its edge. The carbon-based fabric electrode layer covers the reservoir cavity, and the area of ​​the carbon-based fabric electrode layer is larger than the sum of the areas of the open ends of the reservoir cavity. The size of one open end of the reservoir cavity is larger than the size of one bottom end of the reservoir cavity. The electrode preparation method includes the following steps: Step 1: Prepare the substrate layer; Step 2: Prepare the reservoir cavity on the substrate layer; Step 3: Fill the reservoir cavity with conductive hydrogel; Step 4: Deposit the carbon-based fabric electrode layer. Therefore, the ECG electrode of this invention is suitable for detecting ECG signals, does not cause allergies, has low contact impedance with the skin, low noise, and high detection accuracy.

An intelligent blood pressure monitoring device and method for a patient with atrial fibrillation, and a processor. The device comprises a blood pressure measurement module, an electrocardiogram measurement module, and a processing module. The blood pressure measurement module is used for measuring arterial blood pressure data of a target subject. The electrocardiogram measurement module is used for acquiring electrocardiogram data of the target subject and generating electrocardiographic data. The processing module is used for performing time alignment of the electrocardiographic data and the arterial blood pressure data, determining R-R interval stability in the electrocardiogram data, and selecting arterial blood pressure data or calculating an average value of the arterial blood pressure data on the basis of the R-R interval stability, P wave variability, and / or atrial fibrillation status in the electrocardiogram data. The device and method can recognize a period of stable heart rhythm, ensuring the reliability and practicability of a measurement result. By dynamically adjusting the measurement frequency and duration, and selecting data that is less affected by atrial fibrillation, the number of unnecessary measurements is effectively reduced, and meanwhile, the accuracy of blood pressure data is improved.

The invention relates to the technical field of vital sign monitoring, and discloses a non-invasive vital sign monitoring system and method.The system comprises a basic vital sign monitoring module, an extended vital sign monitoring module, a hemodynamic monitoring module and a monitoring and early warning module; the basic vital sign monitoring module is used for monitoring breathing related indexes and stress indexes; an oxyhemoglobin saturation monitoring unit of the extended vital sign monitoring module is used for acquiring a PPG signal through a photoelectric volume sensor and monitoring oxyhemoglobin saturation according to the PPG signal; the hemodynamics monitoring module is used for carrying out cuff-free blood pressure monitoring on the basis of pulse transmission time delay and monitoring output quantity per stroke and cardiac output quantity according to the electrocardiogram signal, the PPG signal and the pulse transmission time delay; and the monitoring and early warning module is used for determining the comprehensive health index of the patient according to the monitoring data of the basic vital sign monitoring module, the extended vital sign monitoring module and the hemodynamics monitoring module, and carrying out prediction and early warning on the physiological state of the patient.

Example systems, devices, and techniques are disclosed. An example system includes an implantable medical device (IMD) configured to obtain the cardiac EGM of the patient. The IMD is configured to determine, based on the cardiac EGM, a first event and a second ventricular event. The IMD is configured to determine a first time interval between the first event and the second ventricular event satisfies a first time interval threshold, indicating a short interval event pair. The IMD is configured to determine whether the short interval event pair is a result of oversensing. The IMD is configured to increment or refrain from incrementing a counter based on the determination of whether the short interval event pair is the result of oversensing. The IMD is configured to determine, based at least in part on a count of the counter, whether to send a lead integrity alert.

A system for providing electrocardiogram (ECG) information to a healthcare provider (HCP) is provided. The system includes a network interface configured to receive drug prescription information from a computing device associated with the HCP and ECG data from a medical device associated with a patient. The system also includes a memory configured to store the drug prescription information and the ECG data. The system can further a processor configured to select a period of time over which to review the received drug prescription information and the received ECG data, retrieve the received ECG data for the selected period of time, derive one or more ECG metrics including at least one heart rate parameter, and generate a graphical representation indicating a trend in the one or more ECG metrics and a relationship between the one or more ECG metrics and the drug prescription information.

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

This invention discloses a non-contact electrocardiogram (ECG) measurement method using millimeter-wave radar. First, the original chest wall displacement signal acquired by the millimeter-wave radar is preprocessed to obtain a displacement feature branch input. Then, the original displacement signal undergoes cardiac sound band enhancement, wavelet denoising, and envelope extraction to obtain radar cardiac sound features. A hidden semi-Markov model is constructed to model the states of the first heart sound, systole, second heart sound, and diastole in the cardiac cycle, obtaining the posterior probability of the state at each time point. Next, the displacement features, radar cardiac sound features, and state probability features are input into a multi-branch convolutional fusion network, combined with a bidirectional long short-term memory network to achieve temporal correlation modeling, finally outputting the reconstructed human ECG result. This invention achieves non-contact ECG measurement without skin electrode contact by fusing the original mechanical displacement of the chest wall, the mechanical vibration features of the heart sounds, and the explicit state prior information of the cardiac cycle.

An ergonomic and unobtrusive cardiac monitoring and treatment device with separable monitoring and treatment components includes a first wearable portion, a second wearable portion separable from the first wearable portion, and a device controller. The device controller includes at least one processor and an output device. The first wearable portion includes an elongated strap configured to encircle a thoracic region, ECG sensing electrodes permanently formed into the strap, a driven ground electrode, and conductive wires configured to couple the sensing electrodes with the controller. The second wearable portion includes a wearable substrate configured to be worn over at least one shoulder, one or more treatment electrodes configured to provide a therapeutic shock to the patient, and at least one additional conductive wire configured to releasably connect the treatment electrode(s) to the controller. The output device is configured to provide a notification instructing the patient to wear the second wearable portion.