151 results about "Electrocardiography" patented technology

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.

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.

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.

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 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.

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.

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.

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.

“Artificial Intelligence” or “AI” technology can be applied to Wearable Cardioverter Defibrillators (“WCDs”) and other wearable medical equipment in various ways, including garment fitting and adjustment, analyzing electrocardiogram (“ECG”), other sensor data and / or other patient data in real time to detect / assess the patient's present condition and / or need for treatment for cardiac and other conditions (e.g., stroke, coughing, apnea, etc.), detect imminent failure of the wearable medical device components, capture and report data collected from the patient for presenting to clinicians, adjust thresholds for alarms and notifications based on patient's responses, improve patient compliance based on the patient's past non-compliant behavior and actions that resulted in the patient becoming compliant, provide tests to the patient, and learn the patient's responses to detect / assess the patient's present condition and / or need for treatment.

The invention discloses a sudden cardiac death risk prediction method, device and system and a storage medium, and the method comprises the steps: obtaining resting electrocardio data outputted through a plurality of electrocardiogram leads, processing the resting electrocardio data to obtain high-frequency QRS wave group data corresponding to each electrocardiogram lead, and obtaining high-frequency QRS features corresponding to each electrocardiogram lead; determining a lead positive index corresponding to each electrocardiogram lead according to the high-frequency form index corresponding to each electrocardiogram lead; determining whether there is a sudden cardiac death risk based on at least one of a plurality of conditions; and under the condition of determining that the sudden cardiac death risk exists, predicting the sudden cardiac death risk level according to the sum value of the high-frequency form indexes corresponding to the electrocardiogram leads. According to the scheme provided by the embodiment of the invention, the sudden death risk is quantified based on a plurality of (greater than or equal to 12) lead high-frequency form indexes and values reflecting the myocardial injury volume under the qualitative condition of a plurality of risk factors, and the missed diagnosis rate and accuracy can be taken into account.

A diagnostic tool includes a sensor for capturing at least one biosignal produced by a patient's heart and a computer device that implements a neural network iteratively trained via machine learning to generate a prediction about a heart condition of the patient. After the neural network is trained, the computer device can convert the at least one biosignal to a multi-dimensional input matrix for the deep neural network generated from a number (N) of biosignals captured by the sensor. The computer device then processes the multi-dimensional input matrix through the deep neural network, which subsequently outputs the prediction about the heart condition of the patient.

A wearable medical monitoring (WMM) system may be worn for a long time. Some embodiments of WMM systems are wearable cardioverter defibrillator (WCD) systems. In such systems, ECG electrodes sense an ECG signal of the patient, and store it over the long-term. The stored ECG signal can be analyzed for helping long-term heart rate monitoring of the patient. The heart rate monitoring can be assisted a) by special filtering techniques that remove short-term variations inherent in patients' short-term heart rate determinations, and b) by indication techniques that indicate when conditions hampered sensing of the ECG signal too much for a reliable heart rate determination.

A solution for estimating a cardiac pre-ejection period is disclosed. According to an aspect, a method includes: measuring, from a user by using a plurality of cardiac sensor devices, electrocardiogram measurement data, a first set of cardiac measurement data measured at a first location of the user's body a first distance from the user's heart, and a second set of cardiac measurement data measured at a second location of the user's body a second distance from the heart, the second distance different from the first distance, wherein the electrocardiogram measurement data is clock-synchronized with the first set of cardiac measurement data and second set of cardiac measurement data; determining, in the electrocardiogram measurement data, a first set of time instants associated with electric heart activations; determining, in the first set of cardiac measurement data, a second set of time instants associated with detections of blood pulses at the first location, the blood pulses resulting from the electric heart activations; determining, in the second set of cardiac measurement data, a third set of time instants associated with detections of the blood pulses at the second location; forming a set of scatter points on the basis of at least the second set of time instants and the third set of time instants and performing a fitting for the scatter points; and computing the cardiac pre-ejection period from at least one parameter describing the fitting.

Portable device of a set of lightweight, flexible wired digital stethoscope and ECG blood pressure monitor with water-repellent coating for collecting and delivering measurements via USB connection for telemetry (FIG.5).