4 results about "T wave" patented technology

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.

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.

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.