31 results about "T wave" patented technology

Techniques are disclosed for segmenting electrocardiogram (ECG) signals. In one example, a method to segment an electrocardiogram (ECG) signal may include detecting consecutive heartbeats in an ECG signal. The method also includes segmenting the ECG signal into multiple ECG segments surrounding the detected consecutive heartbeats and generating an ECG data set by joining consecutive ECG segments. The generated the ECG data set represents the detected heartbeats. In some such examples, each ECG segment is of a duration to include a QRS complex, a P wave, and a T wave.

According to one aspect of the present invention, provided is a method for classifying an electrocardiogram signal, the method comprising the steps of: converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave; and outputting the classification result of the electrocardiogram signal by processing the first graph data by using a graph convolutional network (GCN)-based classification model.

The invention discloses an electrocardiogram ventricular escape real-time judgment method and system, relates to the technical field of electrocardiograms, solves the problem of lack of multi-dimensional analysis and dynamic characteristic verification of electrocardiogram waveforms, and aims at solving the problem that a time period in which waveform abnormity really exists is focused through continuous analysis of fluctuation points of a to-be-recognized section, so that the accuracy of the electrocardiogram ventricular escape real-time judgment is improved. A symmetric space vector range of a normal P wave is used as a reference, whether a to-be-analyzed waveform contains the P wave is visually judged through matching of the form and the position of a to-be-verified vector, missed judgment caused by low amplitude of the P wave or overlapping of the P wave in a T wave is avoided, and the accuracy of the judgment is improved. A complete analysis chain from electrocardiosignals to clinical diagnosis is constructed through a four-layer logic architecture of pacing point positioning, feature waveform matching, abnormal time capturing and escape pacing mechanism verification, high-precision recognition of ventricular escape pacing is achieved, and the accuracy of ventricular escape pacing recognition is improved through deep fusion of a physiological mechanism and an algorithm. And an interpretable and traceable technical path is provided for automatic electrocardio diagnosis.

The invention discloses a portable blood circulation auxiliary device, a control method and a blood circulation auxiliary system. The portable blood circulation auxiliary device integrates the functions of blood oxygen and electrocardiogram detection and multi-part electrical stimulation, is small in size, supports portable use, gets rid of dependence of a traditional air bag type device on a fixed power source, does not limit activities of a user and meets daily scene requirements. Moreover, by combining double-index dynamic adjustment of the electrocardio waveform and the blood oxygen data, the discharge frequency is determined through the real-time heart rate, the discharge intensity is adjusted based on the blood oxygen data, precise and personalized intervention is achieved, and the defect that a traditional device lacks self-adaptive adjustment is overcome to a certain degree. Besides, electric stimulation is released according to the time sequence that the crus delays K1 milliseconds at the peak of the R wave and the thigh delays K2 milliseconds at the peak of the R wave, the time sequence is synchronously continued to the starting point of the T wave, the physiological characteristics of lower limb muscles and the blood pumping rhythm of the heart are met, the use comfort is improved, meanwhile, blood backflow is efficiently promoted, and the heart burden is relieved.

The application provides an arrhythmia signal detection method and system based on expert knowledge, wherein the method comprises the following steps: performing signal filtering on an acquired electrocardio signal to filter out high-frequency noise and baseline drift in the electrocardio signal; performing P-QRS-T positioning detection on the signal-filtered electrocardio signal to obtain a QRS complex and P / T wave; performing expert-knowledge-based feature mapping on the obtained QRS complex and P / T wave to obtain a category feature of the electrocardio signal; comparing the obtained category feature of the electrocardio signal with a preset threshold to determine an arrhythmia signal, thereby completing detection of the arrhythmia signal. Meanwhile, a corresponding terminal and medium are provided. The application improves the detection accuracy; fully considers the morphology of an abnormal heartbeat and the timing of positioning the P / T wave, can effectively avoid the case that the P-QRS-T positioning is inaccurate due to the abnormal heartbeat, has high robustness, and has low computational complexity.

The invention discloses a non-contact electrocardiogram signal reconstruction method for a millimeter-wave radar, which is applied to the cross technical field of millimeter-wave radar and biomedical signal processing, and aims to solve the problems that weak characteristics such as P waves and T waves are lost due to neglect of time-frequency dynamic characteristics of electrocardiogram signals in the conventional electrocardiogram reconstruction technology based on the millimeter-wave radar; and hardware non-ideal noise and motion interference in radar signals are not processed in a targeted manner, and the standardized root-mean-square error of reconstructed signals is generally high. The method comprises the steps of firstly eliminating hardware noise and breathing interference in radar original signals, then capturing time-frequency details of an electrocardiogram through multi-scale short-time Fourier transform, strengthening key feature focusing in combination with an attention-guided U-shaped network, and finally optimizing a model by using a time domain-frequency domain joint loss function, so as to obtain a time domain-frequency domain joint loss function. The core characteristics of P waves, QRS wave groups, T waves and the like are reserved, and meanwhile motion artifacts and multi-source noise are remarkably restrained.

The present invention relates to an electrocardiogram feature signal extraction method and terminal, which obtain an input electrocardiogram signal; filter and preprocess the electrocardiogram signal; amplify the electrocardiogram signal using a square function, and process the electrocardiogram signal using a moving window integration algorithm; sequentially detect the electrocardiogram signal using a feature point detection algorithm, wherein the types of feature point detection algorithms include R wave peak point detection algorithm, P wave peak point detection algorithm, T wave peak point detection algorithm, S wave valley point detection algorithm, and Q wave valley point detection algorithm; during the process of detecting the electrocardiogram signal, an adaptive threshold value corresponding to the type of feature point detection algorithm is obtained using a threshold adaptive algorithm, and whether the electrocardiogram signal meets the preset detection conditions of the feature point detection algorithm is determined. The electrocardiogram signal that meets the preset detection conditions is used as a feature point peak value / feature point valley value and is saved. Therefore, the present invention can accurately and completely detect the feature data of the electrocardiogram signal.

A system that enables safe cardiac pulsed field ablation by using multiple types of sensors on a patch to detect and cross-check that the heartbeat is in a safe phase to receive an ablation pulse. The patch may contain for example any or all of ECG electrodes, PPG sensors, accelerometers, and microphones. The patch may also contain one or more pulse return electrodes for unipolar ablation. A signal analyzer coupled to a pulse generator may receive sensor data from the patch and may analyze this data to determine the heartbeat phase. If data from different sensors are inconsistent, then pulse generation may be disabled as a safety feature. Otherwise, pulses may be enabled when the heartbeat is in a safe phase; for example, pulses may be excluded during the T wave.

Techniques are disclosed for segmenting electrocardiogram (ECG) signals. In one example, a method to segment an electrocardiogram (ECG) signal may include detecting consecutive heartbeats in an ECG signal. The method also includes segmenting the ECG signal into multiple ECG segments surrounding the detected consecutive heartbeats and generating an ECG data set by joining consecutive ECG segments. The generated the ECG data set represents the detected heartbeats. In some such examples, each ECG segment is of a duration to include a QRS complex, a P wave, and a T wave.

The present disclosure provides an identity recognition method and device, electronic equipment and computer storage medium. The identity information of a user is automatically recognized, and the efficiency of user identity recognition is improved. The method comprises: acquiring electrocardio data of at least two leads of a user, wherein the leads comprise limb leads and / or chest leads; performing contour recognition on each electrocardio data to obtain the contour of each wave of a specified type in the electrocardio data, wherein the each wave of the specified type comprises at least one of a P wave, a T wave and a QRS wave; projecting the contour of each wave corresponding to each electrocardio data in a specified plane to obtain an electrocardio vector ring of the user; respectively matching the electrocardio vector ring of the user and each preset template electrocardio vector ring to obtain each matching value, wherein each preset template electrocardio vector ring corresponds to different user identity information; and determining the identity information of the user according to the each matching value.

The invention relates to a device for monitoring and optimizing the time-triggered stability of extracorporeal circulation support, as well as to an extracorporeal circulation support control unit comprising such a device and to a corresponding method. Accordingly, the present invention proposes a method for monitoring and optimizing time-triggered stability of extracorporeal circulation support, comprising the steps of: receiving an electrocardiogram signal of a supported patient within a predetermined duration; determining a P wave, an R wave and a T wave from the received electrocardiogram signal; a predetermined trigger signal is determined from the electrocardiogram signal of the current cardiac cycle, taking into account the at least one detected P-wave and / or the at least one detected T-wave.

The present application relates to a kind of ECG feature wave segmentation method and FS-Net model, ECG feature wave segmentation method specifically includes the following steps: S1.ECG signal denoising;S2.Using Pan-Tompkins algorithm demarcation ECG signal R peak information, calculate RR interval information, then using sliding window to data normalization processing;S3.ECG signal waveform correction and feature enhancement: first, ECG signal is carried out smooth processing, then the QRS wave of ECG signal, P wave and T wave are corrected, finally feature enhancement is carried out;S4.FS-Net model is constructed;S5.ECG signal after processing in step S1-S3 is input into FS-Net model and is trained, realizes the segmentation to feature wave in ECG signal;S6.post-processing algorithm;S7.ECG signal segmentation: using the FS-Net model after training to the measured ECG signal is segmented.The present application is corrected and enhanced to ECG signal, effectively enhances the performance of P wave and T wave in electrocardiogram, improves the accuracy of feature wave detection.

The present application proposes a method for calculating an ST offset value, which includes: acquiring an electrocardiogram (ECG) signal, wherein the ECG signal includes an ST segment located between a QRS complex and a T wave; extracting the R wave site of the ECG signal; determining a first Q wave starting point set and a first S wave ending point set in the ECG signal based on the R wave site of the ECG signal and a preset square formula; determining a second Q wave starting point set and a second S wave ending point set in the ECG signal based on the R wave site of the ECG signal and a preset differential formula; determining the isoelectric point position of the ECG signal based on the first Q wave starting point set and the second Q wave starting point set; determining the ST point position of the ECG signal based on the first S wave ending point set and the second S wave ending point set; and determining the ST offset value of the ECG signal based on the isoelectric point position and the ST point position.

The present application relates to a kind of digital electrocardiogram waveform identification and time limit parameter measurement method, the present application selectively according to the signal type of the need to retain one or more of these interference signals and part electrocardio feature signal is decomposed to obtain the feature signal of the layer needed to retain, again by the selective superposition integration of each layer effective signal and feature signal, join the method for identifying J point, QRS wave, P wave, T wave, retain the effective signal data of 3 wave bands finally for doctor auxiliary diagnosis in each layer, then join positioning method is used to realize the accurate positioning of the start point and end point position of PR interval, QT interval, QRS time limit, then the corresponding effective value is determined in conjunction with the amplitude of each wave band, finally join the algorithm for the calculation of heart rate to realize the calculation of heart rate.The present application can shorten the effective time of doctor to view electrocardiogram, improve the efficiency of doctor to read electrocardiogram, reduce the misdiagnosis rate caused by long time reading.

Systems and methods for ambulatory detection of Q wave-to-T wave (QT) interval prolongation are discussed. A medical-device system comprises a controller circuit and a user interface device. The controller circuit includes a long QT syndrome (LQTS) detector that measures a QT interval from a subcutaneous cardiac signal sensed from a patient using implantable electrodes, and detects an indication of QT prolongation using the measured QT time interval and a programmable threshold received as a user input from the user interface. The control circuit can adjust device operation based on the detected indication of QT prolongation. An output unit can generate a programmable alert of the QT prolongation corresponding to the user input of the programmable threshold.

The signal analysis device includes an analysis unit that obtains at least a part of the parameters for determining at least any one of the first unimodal distribution, the first cumulative distribution function, the second unimodal distribution, and the second cumulative distribution function when approximating the time waveform of the R wave by using the waveform generated by the difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains at least a part of the parameters for determining at least any one of the third unimodal distribution, the third cumulative distribution function, the fourth unimodal distribution, and the fourth cumulative distribution function when approximating the time-axis reversed waveform of the time waveform of the T wave by using the waveform generated by the difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains the parameters indicating the activity of the myocardium of the heart through the calculation of the obtained parameters.

The invention relates to an arrangement (200) comprising a non-transvenous implantable cardioverter-defibrillator device (210) for stimulating a human or animal heart, the non- transvenous implantable cardioverter-defibrillator device (210) comprising a stimulation unit (270) for emitting stimulation pulses (140) externally to the heart; and a programming device (220) operatively coupled to the non-transvenous implantable cardioverter- defibrillator device (210); wherein the arrangement is capable to predict an expected time of occurrence of a future T wave (Bn) by determining the future T wave (Bn) on the basis of a function using first coefficients (ai), second coefficients (ci) and / or third coefficients (di).

The present invention discloses a method, apparatus, terminal device and storage medium for processing ECG data for heart failure assessment, which obtains ECG data of a patient; performs peak screening processing on an ECG in the ECG data to obtain a plurality of ECG waves; determines R waves, P waves, Q waves, S waves and T waves in the plurality of ECG waves; extracts ECG acquisition parameters and ECG waveform parameters corresponding to each ECG wave in the ECG data based on generation time; establishes a coordinate system for each ECG wave with the ECG waveform parameter type of the ECG wave as the horizontal coordinate and the ECG waveform parameter value as the vertical coordinate, determines the ECG waveform type of the ECG wave based on the parameter value range of historical ECG waveform types, and summarizes the ECG waveform type and the ECG acquisition parameters to generate ECG analysis data of the patient; performs heart failure assessment based on the obtained accurate ECG analysis data in combination with the user's body impedance, which is beneficial to improving the accuracy of heart failure assessment.

A system that enables safe cardiac pulsed field ablation by using multiple types of sensors on a patch to detect and cross-check that the heartbeat is in a safe phase to receive an ablation pulse. The patch may contain for example any or all of ECG electrodes, PPG sensors, accelerometers, and microphones. The patch may also contain one or more pulse return electrodes for unipolar ablation. A signal analyzer coupled to a pulse generator may receive sensor data from the patch and may analyze this data to determine the heartbeat phase. If data from different sensors are inconsistent, then pulse generation may be disabled as a safety feature. Otherwise, pulses may be enabled when the heartbeat is in a safe phase; for example, pulses may be excluded during the T wave.

This application proposes a classification model, construction method, and application of myocardial ischemia based on 12-lead ECG. By detecting the ST-T segment beat-by-beat changes using entropy domain, frequency domain, and Lyapunov domain analysis methods, spatiotemporal ECG feature parameters related to myocardial ischemia are obtained. A machine learning model for predicting myocardial ischemia is established, ultimately achieving optimal ECG feature selection related to myocardial ischemia. This addresses the problem that conventional 12-lead ECGs often lack clear ST segment or T wave features related to myocardial ischemia, severely affecting the sensitivity and accuracy of myocardial ischemia diagnosis. The method includes: acquiring a 12-lead ECG; calculating the ST-T segment sample entropy of the 12-lead ECG; converting the 12-lead ECG into a 3-lead ECG vector map; extracting the ST-T segment from the 3-lead ECG vector map and calculating its spatial and temporal feature values; and using a grid search method to select the optimal ECG features reflecting myocardial ischemia. Therefore, the sensitivity and accuracy of myocardial ischemia diagnosis are improved.

The invention relates to an electrocardiogram R peak detection method based on nonlinear enhanced wavelet transform and windowing adaptive threshold. The method comprises the following steps: (1) acquiring and preprocessing electrocardiosignal data; (2) R peak candidate point detection based on nonlinear enhanced wavelet transform; (3) positioning an R peak based on a windowing self-adaptive threshold value; according to the invention, based on wavelet transform, a polarity-retaining square operation and sliding window integration nonlinear enhancement method is introduced, so that the detection reliability of the QRS wave group is improved. According to the method, the amplitude of the QRS wave group wavelet coefficient is enhanced, the polarity information of the QRS wave group wavelet coefficient is reserved, and the features are further aggregated through sliding integration, so that the enhanced wavelet coefficient is smoother than the original wavelet coefficient, P / T waves and noise components are remarkably inhibited, and the detection precision of the QRS wave group extreme value pair is improved. According to the method, a windowing self-adaptive threshold strategy is provided, and the detection rate of a low-amplitude R peak is increased.

This invention discloses a non-contact ECG generation method based on radar signals and enhancement strategies. First, synchronously acquired radar heart sound signals and ECG signals are preprocessed and enhanced in multiple dimensions to construct training samples. Then, an encoder network is used to extract deep features from the radar heart sound signals step by step. Next, a multi-head self-attention mechanism and a bidirectional long short-term memory network are used to capture long-range waveform dependencies and temporal context. A high-fidelity ECG is then reconstructed via a hierarchical upsampling decoder. Finally, a four-dimensional loss function that integrates amplitude, phase, R-wave localization, and frequency domain consistency is used to optimize the model parameters. This method can reconstruct clinically diagnostic ECGs with high accuracy from non-contact radar signals. While effectively preserving key waveform morphologies such as the P wave, QRS complex, and T wave, it significantly improves waveform correlation, key feature localization accuracy, and the accuracy of long-term heart rate variability analysis.

The invention provides an automatic detection method and device for waveform characteristics of a dynamic electrocardiogram and a server, and relates to the technical field of automatic detection.The automatic detection method comprises the steps that original electrocardiogram data are obtained, data preprocessing is conducted on the original electrocardiogram data, and target input electrocardiogram data are determined; performing dynamic window query processing on the target input electrocardiogram data based on a preset window range set by using a preset adaptive threshold algorithm and a preset feature point detection algorithm, determining an R-wave group, and extracting a real position of an R point from the R-wave group; performing waveform feature extraction processing on the target input electrocardio data by taking the real position of the R point as a reference through a preset local transformation algorithm, determining a Q wave and an S wave, and determining waveform features of the QRS wave group according to the real positions of the Q wave, the S wave and the R point; and based on the position of the QRS wave group, extracting waveform characteristics of a P wave and a T wave from the target input electrocardiogram data. The accuracy and the detection efficiency of dynamic electrocardiogram waveform feature detection can be remarkably improved.

A heart phase-locked transcranial magnetic stimulation method and system based on heart-brain axis navigation positioning, the method comprising inputting a preprocessed electrocardiogram signal into a trained and converged TimeMixer model to output a predicted electrocardiogram waveform signal; splicing the predicted electrocardiogram waveform sequence and the original electrocardiogram waveform sequence to obtain a global electrocardiogram sequence, identifying effective R peaks and T wave peaks and determining T wave endpoints on the global electrocardiogram sequence; dividing the systole and diastole of the heart using the effective R peaks and the T wave endpoints, and assigning and encoding the systole and diastole of the heart; determining the target position of the transcranial magnetic stimulation according to the encoding labels corresponding to the systole and diastole of the heart and the transcranial magnetic stimulation target position determined by the heart-brain axis navigation mode, and controlling the parameters of the transcranial magnetic stimulation; by combining the electrocardiogram prediction of the TimeMixer model with the individualized stimulation target point determined by the heart-brain axis navigation, the problems of ignoring individual physiological state in the existing open-loop control, complex closed-loop acquisition being easily disturbed, and inaccurate target point positioning are solved.

The invention discloses a biomedical signal noise reduction method and device based on multi-dimensional information fusion and a medium, and relates to the technical field of biomedical signal processing.The method comprises the steps that an original ECG signal, an original ACC signal and an original GYRO signal of a target user in a dynamic electrocardiogram monitoring scene are preprocessed; performing layered noise reduction on the preprocessed first ECG signal; and according to the preprocessed first ACC signal and the first GYRO signal, carrying out artifact suppression on ECG features extracted from the second ECG signal subjected to layered noise reduction, inversely mapping the target ECG features subjected to artifact suppression to the space of the second ECG signal, and determining a target ECG signal which is high in signal-to-noise ratio and contains a complete P-QRS-T wave group. According to the method and the device, the cooperative suppression of various noise types in dynamic electrocardiogram monitoring is realized, the clinical diagnosis value of the ECG signal is guaranteed, and the noise reduction effect and the signal availability rate of the ECG signal in a dynamic scene are improved.

This application discloses a method and system for processing electrocardiogram (ECG) signals, relating to the field of biomedical signal processing. The method comprises acquiring an ECG signal, segmenting the ECG signal into beats to obtain multiple beats, and obtaining the positional information of the QRS wave, T wave, and ST wave segment of each beat using the Pan-Tompkins algorithm; extracting the dynamic characteristics of each beat based on the positional information; and using a random forest algorithm to assess the importance of the dynamic characteristics, screening key features, and performing serial fusion to obtain comprehensive feature information. This application uses multi-dimensional feature extraction and fusion to more comprehensively capture subtle changes in complex ECG signals, thereby improving the accuracy and efficiency of ECG signal analysis.

This invention belongs to the field of electrocardiogram (ECG) signal feature recognition, and discloses a method and related device for extracting PT wave interval features from ECG signals based on the DE algorithm. The invention first preprocesses the acquired ECG signal, then performs two types of interval peak finding to determine the R-peak position. The interval is truncated based on the peak finding results. Baseline drift is removed from the truncated ECG signal, and after normalization, phase allocation is performed. Feature templates are extracted based on the phase allocation results. Finally, the parameters of the parabolic fitting feature template are optimized using the DE algorithm, resulting in the P and T wave peak finding intervals and approximate peak positions. Since the feature template is extracted from the patient's ECG signal, it contains the patient's ECG characteristic waveform distribution information. Therefore, the final optimized parameters can be used as prior information to predict the subsequent PT wave peak finding interval, helping to ensure or improve the safety and reliability of medical devices that require real-time ECG signal monitoring and feature recognition.

The application provides an electrocardiogram P wave recognition method based on machine learning. The method collects p wave forms andqrst wave forms of different people at different times to obtain original electrocardiogram signal data, and then obtains P waves, QRS waves and T waves after segmentation. Then, various types of electrocardiogram data are generated according to preset electrocardiogram waveform rules, and then the electrocardiogram data is randomly combined and added with noise to obtain an electrocardiogram signal set to be verified. The recognizer is trained based on the training data set to ensure that the P wave position in the electrocardiogram data can be accurately found.

Disclosed herein are methods, systems, and devices for identifying increased likelihood of non-ST elevation myocardial infarction (NSTEMI) in a patient based on ECG data. The methods can include determining based on the ECG data that the patient lacks ST elevation (STE) and that the patient exhibits a ventricular repolarization dispersion (VRD) score that exceeds a predetermined threshold value. The VRD score can be based in part on a T wave complexity ratio that serves as a temporal marker of VRD for the patient. Other markers of spatial and time qualities of repolarization can also be included in the VRD score. An elevated VRD score in the absence of STE can indicate a likelihood of NSTEMI in the patient and a potential major adverse cardiac event.

The present invention discloses a method and system for correcting the position of an electrocardiogram (ECG) electrode, which relates to the field of ECG signal acquisition technology. The method comprises the following steps: obtaining an original ECG collected by a glove acquisition front end; preprocessing the original ECG and locating the PQRST band based on the preprocessed original ECG to obtain the P wave, Q wave, R wave, S wave, and T wave corresponding to the target lead ECG signal; the target lead ECG signal is a II lead ECG signal, a V1 lead ECG signal, a V3 lead ECG signal, and a V4 lead ECG signal; determining the measurement position of the glove acquisition front end based on the amplitude changes of the P wave, Q wave, R wave, S wave, and T wave corresponding to the target lead ECG signal; and correcting the measurement position of the glove acquisition front end when the measurement position of the glove acquisition front end is not in the standard position. The present invention can reduce the risk of ECG signal waveform changes caused by incorrect ECG electrode placement.