360 results about "Electrocardiography" patented technology

The invention discloses a multi-module collaborative rapid electrocardiogram automatic diagnosis method and system, and belongs to the technical field of automatic diagnosis. The method comprises the following steps: preprocessing an original ECG signal; the preprocessed ECG signals serve as input, and after the preprocessed ECG signals are sequentially processed by a lightweight convolution sub-module, a deformation convolution sub-module and a non-local attention sub-module, multi-level features containing local details and global time sequence dependency are output; fusing the multi-level features, and sending the fused multi-level features into a classifier to complete prediction of heart rhythm types to obtain prediction probabilities of all categories; a sliding window mode is adopted, the ECG signal fragments with the fixed length are sent to the diagnosis model for reasoning, the diagnosis model continuously outputs diagnosis results, and alarm or diagnosis prompt information is output in time according to the diagnosis results. According to the method, by designing a lightweight network structure, the parameter quantity and calculation complexity of a diagnosis model are effectively reduced, and real-time online ECG signal automatic diagnosis is realized on the premise of ensuring high diagnosis accuracy.

The invention relates to the technical field of cardiovascular medicine, in particular to an automatic alarm system for cardiovascular medicine, which comprises a biomarker analysis module, a physiological parameter analysis module, a cardiovascular risk assessment module, a cardiovascular early warning adjustment module and an alarm trigger notification module. According to the invention, by integrating real-time monitoring of blood biomarkers and cardiovascular physiological parameters, the early recognition capability of cardiovascular disease risks is improved, key biomarkers are dynamically measured and compared with historical marker data, and subtle physiological changes can be captured at the early stage of asymptomatic conditions, so that potential cardiovascular events are warned; through correlation analysis of electrocardiogram, blood pressure and pulse waveforms, the system can accurately draw a comprehensive image of cardiovascular functions, so that more targeted monitoring and intervention are implemented, timeliness and accuracy of medical response are ensured, doctors can formulate targeted monitoring schemes according to real-time data, and the monitoring efficiency is improved. Therefore, deterioration or occurrence of cardiovascular events of patients can be effectively prevented.

Systems and methods are provided for monitoring and processing physiological signals (e.g., electrocardiogram signals) to detect and predict for possible dysfunction of an anatomical feature (e.g., cardiac dysfunction) or otherwise predict a likelihood of future cardiac dysfunction of the individual. For example, a system comprises a plurality of sensors, a physiological signal processing system, and a feature analysis system. The sensors are configured to monitor physiological signals from an individual that has undergone a medical procedure on an anatomical feature. The physiological signal processing system is configured to analyze the physiological signals and extract features from the physiological signals which are indicative of a function of the anatomical feature. The feature analysis system is configured to analyze the extracted features and predict a risk of the individual developing a post-procedural dysfunction of the anatomical feature as a result of the medical procedure on the anatomical feature.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

A medical monitoring system comprises a wearable garment incorporating multiple physiological sensors for real-time patient monitoring. The garment integrates an embedded processing unit that receives sensor data and transmits it wirelessly to a base station or remote server. A multi-modal physiological signal processing unit analyses the data to generate composite physiological indices, including metabolic rate, work of breathing, and fall risk assessments. The garment includes optional monitoring features such as transcutaneous carbon dioxide measurement, electrocardiography, pulse oximetry, blood pressure monitoring, and accelerometer - gyroscope-based motion tracking. The system provides early-warning alerts for clinical deterioration, enabling proactive therapeutic interventions. A wireless communication module ensures real-time data transmission, supporting remote monitoring and integration with hospital networks. The garment's modular design allows for non-invasive physiological assessment while maintaining patient mobility, making it suitable for critical care, ambulatory monitoring, and telemedicine applications.

The invention provides a sudden cardiac death high-risk group multi-modal data intelligent analysis and prediction method and system, and relates to the technical field of medical health monitoring, and the method comprises the steps: collecting multi-modal data, projecting an electrocardiogram to a three-dimensional coordinate system, extracting potential trajectory features, and calculating a space-time discrete index; performing dynamic response analysis on the blood pressure data, and matching with a space-time discrete index to calculate a cardiovascular coupling coefficient; and performing segmented correction according to the motion state data, monitoring a coupling coefficient deviation condition in real time, calculating a risk degree and generating an early warning. According to the method, heart abnormity can be found in advance, and sudden cardiac death can be effectively prevented.

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

The disclosed systems and methods provide systems and methods for rhythm recognition and the analysis of ECG signals.

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.

The invention belongs to the technical field of remote health monitoring, medical diagnosis and intelligent health equipment, and discloses a non-contact heartbeat monitoring method and system based on millimeter wave signals. According to the non-contact heartbeat monitoring system, heartbeat monitoring is achieved by transmitting and receiving millimeter wave signals reflected from the chest of the human body through the millimeter wave radar technology. Tiny movement generated by cardiac pulsation can cause tiny changes of the chest of the human body, and the changes are shown through reflected millimeter wave signals. By processing and analyzing the reflected signals in real time, the influence of heartbeat on the signals can be accurately extracted, so that the heartbeat frequency and physiological indexes related to the electrocardiogram can be deduced.

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 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.

The invention provides a cardiovascular and cerebrovascular disease risk prediction method and system, and relates to the technical field of data processing.The method comprises the steps that real-time physiological parameters of a target user are obtained, and the real-time physiological parameters comprise the ambulatory blood pressure fluctuation rate, the serum lipoprotein level, the heart rate variability frequency domain index and the sleep apnea hypopnea index; the method comprises the following steps: preprocessing real-time physiological parameters, extracting instantaneous waveform features of blood pressure signals by adopting wavelet transform, and extracting features related to health conditions, including blood pressure level, blood fat level, blood sugar level, electrocardiogram abnormal indexes and cardiac ultrasound abnormal indexes, to form a feature vector set; according to the method, the real-time physiological parameters are acquired, the feature vector set is formed through preprocessing, the krill swarm algorithm is used for optimization, the three-level fusion model is constructed, finally, the cardiovascular and cerebrovascular disease risk degree of the target user is accurately evaluated, early warning information is output, and the accuracy and timeliness of cardiovascular and cerebrovascular disease risk prediction are effectively improved.

The invention discloses a dynamic electrocardiogram waveform signal analysis method based on improved DTW and machine learning, and relates to the technical field of medical monitoring and human factor engineering. Firstly, denoising and baseline correction are carried out on an original signal through fast adaptive filtering and wavelet threshold denoising; then adopting an adaptive Sakoe-Chiba constrained improved DTW algorithm, dynamically planning and generating a minimum cumulative cost path through a limited alignment path bandwidth based on dynamic electrocardiogram signal physiological features, and calculating a normalized DTW distance; and finally, in combination with an XGBoost classifier and an SHAP interpretable framework, carrying out classification training by utilizing a DTW alignment distance and time-frequency domain features, and quantifying feature contribution through a Shapley value to generate a classification result. The method solves the problems of low efficiency of the traditional DTW and black box of the traditional model, has the capabilities of real-time processing and long-time data analysis, and provides efficient and reliable technical support for heart health monitoring.

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.

The invention relates to a heart premature beat detection device and method, a ventricular assist system, a storage medium and equipment, and the detection device comprises a first sampling module which is configured to collect operation data of a drive motor in a ventricular assist device, and the operation data at least comprise rotation speed data; the calculation module is configured to determine a current rotating speed maximum value and a historical average maximum value according to the rotating speed data; the judgment module is configured to judge that premature beat occurs when the current rotating speed maximum value is larger than the gear rotating speed and smaller than the historical average maximum value. Compared with a traditional premature beat detection mode, premature beat detection can be carried out in real time in the operation process of the percutaneous ventricular auxiliary device, and traditional external monitoring equipment such as electrocardiogram or color Doppler ultrasound does not need to be relied on any more.

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.

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 present disclosure relates to a method of reconstructing an arterial blood pressure (ABP) signal corresponding to the morphological feature of a combination signal of ECG and PPG on the basis of the morphological feature. The method of reconstructing an ABP signal according to an embodiment of the present disclosure includes: generating an ECG-PPG signal by combining ECG and PPG signals corresponding to an ABP signal; generating a plurality of ECG-PPG signals for learning by applying a plurality of noises, which is different in intensity, to the ECG-PPG signal; training a neural network model to receive the ECG-PPG signals for learning and output the ABP signal; and reconstructing an ABP signal of a target user by inputting an ECG-PPG signal of the target user into the neural network model.

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 a multi-person electrocardiogram reconstruction method based on a millimeter-wave radar, which is applied to the technical field of millimeter-wave radar detection, and aims at solving the problem that in the prior art, only simple heart rate estimation of a plurality of targets is considered, but the estimation of heartbeat signals is not involved. The method comprises the following steps: firstly, converting a radar phonocardiogram into a frequency domain signal; extracting a top-k frequency component and converting the top-k frequency component into a time domain signal; then, multi-head cross attention and multi-head self-attention mechanisms are used for carrying out self-correlation reconstruction on the signals; the obtained output is then input into a frequency Gaussian weighting module, and then a reconstructed ECG signal is output. Compared with traditional heartbeat information analysis of multi-target signals, more accurate electrocardiogram reconstruction information can be provided.

The disclosure relates to a computer-implemented method for monitoring diaphragmatic response to phrenic nerve stimulation. The method comprises receiving in real-time a diaphragmatic CMAP signal. The method comprises computing a baseline value of a characteristic of the CMAP signal. The characteristic represents a diaphragmatic response intensity to a phrenic nerve stimulation. The method comprises determining a threshold value of the characteristic, representing a boundary of values of the characteristic indicative of upcoming diaphragmatic palsy. The determining of the threshold value includes shifting the baseline value. The method comprises receiving in real-time a ECG signal. The method comprises repeating in real-time: detecting a QRS complex in the ECG signal, monitoring the CMAP signal, computing a real-time value of the characteristic, comparing the real-time value to the threshold value, and outputting an alert when the threshold is passed. The real-time value of the characteristic is asynchronous to the QRS complex.

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

An exemplary system and method are disclosed that measures electrocardiographic (ECG) and photoplethysmographic (PPG) signals from the sternum of postpartum women using a stretchable electrode array assembly and a flexible PPG circuit assembly, respectively, and predict an estimated blood pressure using the ECG and PPG signals in real-time using machine learning models. The exemplary system and method may also determine a heart rate parameter, a respiration rate parameter, a heart rate variability parameter, and a blood oxygen saturation parameter from the measured ECG and / or PPG signals. The determined heart rate parameter, respiration rate parameter, heart rate variability parameter, blood oxygen saturation parameter, and estimated blood pressure may be outputted to a mobile device or a healthcare portal to provide a prolonged vital sign monitor for the user.

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.

A wearable cardiac monitoring and treatment system configured to remotely identify and / or verify treatable and / or alertable cardiac arrhythmias in an ambulatory patient is provided. The system includes a wearable cardiac monitoring and treatment device including ECG electrodes, therapy electrodes, and a medical device controller. The system also includes a remote server in electronic communication with the controller. The controller includes one or more processors configured to generate an ECG signal, determine one or more ECG segments corresponding to a suspected arrhythmia, transmit the ECG segment(s) to the remote server, receive, from the remote server, a remote indication as to whether the patient is experiencing an arrhythmia, independently analyze, at the medical device controller, the ECG segment(s) to generate a local indication as to whether the patient is experiencing an arrhythmia, and determine whether to deliver a therapeutic shock to the patient based on the remote and local indications.

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 a physiological signal monitoring method and wearable equipment, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of heart state judgment for an electrocardiogram in the prior art. Comprising the steps that basic information of a user and physiological signals collected in real time are obtained, the physiological signals comprise electrocardiosignals, electromyographic signals and acceleration signals of the heart, the physiological signals are collected based on a multi-mode sensor on the wearable device, and the wearable device is fixed to the body of the user through a connecting assembly of a strip type structure; the physiological signals are subjected to multi-modal fusion based on a multi-modal fusion algorithm, physiological parameter data are obtained, a monitoring task is determined, and the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; and calling a monitoring prediction model which is matched with the monitoring task and has completed model training to perform monitoring processing on the physiological parameters and the user basic information to obtain a monitoring result, and performing feature comparison based on the monitoring result.