18 results about "T wave" patented technology

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.

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.

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 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.

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.

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 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.

The application discloses a method for locating electrocardio feature points based on double U-Net fusion of long and short-range context information, comprising: preprocessing electrocardio signals, including resampling to a unified sampling rate and removing noise interference; segmenting the electrocardio signals, cutting into single heart beat segments and adjacent multi-heart beat segments based on R peak positions; applying data enhancement operations to the segmented heart beat segments; extracting features using double U-Net, including extracting local semantic features of single heart beat segments by a first U-Net and extracting global semantic features of multi-heart beat segments by a second U-Net; fusing the local semantic features and the global semantic features through a cross-level double gating module to generate purified fusion features; and locating feature points of the electrocardio signals based on the fusion features, including positions of P waves, QRS wave groups and T waves. The application improves the precision and robustness of feature point location.