12 results about "ST segment" patented technology

The invention discloses a biomarker combination for predicting PCI postoperative LVR of an STEMI patient and application of the biomarker combination, and belongs to the technical field of biology. The research discovers that in a left ventricular remodeling (LVR) group and a non-left ventricular remodeling group after percutaneous coronary intervention (PCI) of a patient suffering from acute ST segment elevation type myocardial infarction (STEMI), a left ventricular endocardial lower myocardial overall longitudinal strain (GLS internal) value, a lung B-line integral, a serum sFRP5 level and Apelin-13 show significant differences. Through combined prediction analysis, it is found that the GLS internal value, the lung B-line integral, the serum sFRP5 and the Apelin-13 have excellent prediction performance on whether the LVR occurs after the PCI operation of the STEMI patient or not, high-risk groups, suffering from the LVR after the PCI operation, of the STEMI patient can be accurately and efficiently recognized, intervention is conducted as soon as possible, and the life quality of the patient is expected to be improved.

The application discloses an electrocardiogram ST segment shape quantitative analysis method, device and equipment and a storage medium. The method collects multi-lead electrocardiogram signals, performs linear regression fitting on corresponding electrocardiogram data when it is detected that the ST segment of each lead in the multi-lead electrocardiogram signals is depressed, obtains an ST segment trend line, and generates a shape quantitative classification result of the ST segment depression shape. The upward sloping ST segment corresponds to a conventional follow-up suggestion, the horizontal ST segment corresponds to a myocardial ischemia monitoring suggestion, and the downward sloping ST segment corresponds to an emergency coronary artery evaluation suggestion. The shape characteristics of the ST segment and the individual physiological characteristics of a patient are comprehensively considered, a risk assessment correction mechanism with the shape characteristics of the ST segment as the dominant factor and the individual physiological characteristics of the patient as the supplementary factor is adopted, a visual report containing a corrected risk assessment result and a corresponding clinical treatment suggestion is generated, and clear and operable risk assessment results and hierarchical diagnosis and treatment suggestions can be provided for clinicians.

The invention relates to the technical field of medical data mining, in particular to a personalized nursing follow-up visit method and system for coronary intervention patients. According to the method, the abnormal evaluation indexes are obtained according to the abnormal change conditions of the R-R interval and the ST section in the historical complex center dynamic period, and the abnormal period is screened; determining a psychological influence period according to the abnormal period distribution and the abnormal approximation degree; correcting the psychological influence period through the data deviation between the non-abnormal period and the psychological influence period; predicting all the periods after correction to obtain predicted electrocardiogram data, and determining a nursing follow-up review adjustment result by comparing the abnormal evaluation index during actual collection with the predicted electrocardiogram data. According to the method, abnormity preliminary screening is carried out through the R-R interval and the ST period of the historical cardiac cycle, abnormity influenced by psychological factors is further analyzed and corrected, the accuracy and reliability of follow-up and actual analysis are improved, and the effect of follow-up adjustment and nursing follow-up visit is better.

The invention provides an electrocardiograph monitoring processing method and system and electronic equipment, and relates to the technical field of electrocardiograph monitoring, and the electrocardiograph monitoring processing method comprises the steps that a first heart rate parameter and a first ST segment parameter are extracted from a first electrocardiosignal of a target patient; identifying an interference frequency point based on the spectrum data of the first electrocardiosignal, and filtering the first electrocardiosignal for the interference frequency point to obtain a second electrocardiosignal; extracting a second ST segment parameter from the second electrocardiosignal; determining a physiologic compensation path according to the reference heart rate parameter, the reference ST segment parameter and the first mapping parameter of the target patient; the first heart rate parameter and the second ST segment parameter are combined to form a monitoring coordinate point, and the path distance from the monitoring coordinate point to the physiologic compensation path is calculated; and determining a heart compensation state based on the path distance, and outputting a monitoring processing result according to the heart compensation state. According to the method, the physiological compensation path is constructed, the path distance is calculated, the cardiac compensation function is evaluated based on the individual benchmark, and the accuracy of electrocardiograph monitoring is improved.

The application discloses a robust electrocardiogram denoising method based on autoregressive induced residual generation, and belongs to the field of electrocardiogram signal denoising. The method is characterized in that: discrete wavelet transform is performed on a collected noisy electrocardiogram signal segment and filtering is performed to obtain a proxy signal after suppressing part of the noise, and the proxy signal is input into a latent space autoregressive module; on this basis, the original noisy signal is subtracted from the output of the first stage to obtain a noisy residual signal, and the noisy residual signal is input into a conditional generation network based on flow matching after a Gaussian disturbance is injected, so that fine reconstruction of high-frequency details and key waveform forms is realized; finally, the generated target residual signal is fused with the semantic structure of the first stage to obtain a denoised electrocardiogram signal, so that the integrity of diagnostic elements such as P waves, QRS complexes and ST segments is maintained while effectively suppressing multi-source complex noise, and the generalization, interpretability of the electrocardiogram denoising result and the accuracy of downstream tasks are significantly improved.

This invention discloses a multi-parameter fusion method and system for real-time risk assessment of cardiac rehabilitation exercise, relating to the field of medical and health information technology. The method includes: collecting electrocardiogram signals, heart rate, and acceleration data; extracting ST segment offset, QT interval, and heart rate variability features; calculating heart rate reserve percentage and metabolic equivalent; inputting the multi-dimensional features into a bidirectional LSTM network to output risk probabilities; determining the risk level based on the comprehensive risk index and generating exercise intensity recommendations. This invention achieves prospective risk prediction and adaptive intensity regulation, improving the safety of cardiac rehabilitation training.

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 application discloses a power frequency filtering method and device based on R wave detection and self-adaption and a storage medium. The method comprises the following steps: inputting electrocardio data; performing QRS complex wave detection on the electrocardio data; judging whether the current electrocardio data is within the range of the QRS complex wave; if the current electrocardio data is within the range of the QRS complex wave, the weight parameter of the self-adaptive filter is not updated; if the current electrocardio data is not within the range of the QRS complex wave, the weight parameter of the self-adaptive filter is updated according to the error between the output of the self-adaptive filter and the expectation; and the electrocardio data is filtered to remove power frequency interference according to the weight parameter of the self-adaptive filter. The application can avoid the ringing effect, so that the ST segment analysis and arrhythmia analysis are not affected by the power frequency filtering, and the waveform characteristics can be retained to a large extent.

The present disclosure provides an electrocardio data ST segment variability event monitoring method, device and program product. The method of the present disclosure comprises: acquiring valid heartbeats and determining a Q point voltage value as a reference voltage value; calculating a difference value between a J point voltage value and the reference voltage value to obtain a J point voltage offset; according to the fluctuation amplitude of the Q point voltage value, screening trusted heartbeats from the valid heartbeats; calculating an average value of the J point voltage offset as a J point offset global reference value; using a sliding window median algorithm to smooth the J point voltage offset of each trusted heartbeat corresponding to each lead to obtain a J point offset trend value; calculating a difference value between the J point offset trend value of each lead in each sliding window and the J point offset global reference value of the corresponding lead to obtain a relative offset increment; taking a lead satisfying a relative offset increment greater than an offset increment threshold as a affected lead, and determining whether to give a warning according to the number of affected leads.

Systems and methods for a signal processing device for a patient management system are disclosed. In one embodiment, a signal processing apparatus includes a communication circuit configured to receive a cardiac signal from an AMD, where the received cardiac signal is a frequency filtered cardiac signal generated from a wider band cardiac signal sensed using the AMD; and signal processing circuitry configured to restore the received cardiac signal to a wider band cardiac signal; detecting an elevated ST segment in the recovered wider band cardiac signal; and generating an alert of myocardial infarction in response to detecting the elevated ST segment.

The invention discloses a device and method for early evaluation of mouse myocardial infarction modeling success, and the device comprises an input module which is configured to input an electrocardiosignal of a mouse, the mouse is an operation mouse subjected to left anterior descending branch coronary artery ligation, and the left anterior descending branch coronary artery ligation is an operation mouse subjected to left anterior descending branch coronary artery ligation; the electrocardiosignal comprises an electrocardiosignal Saft of the operated mouse after the left anterior descending branch coronary artery ligation and an electrocardiosignal Sbef before the operation; the evaluation module is configured to compare the electrocardiosignal Saft of the mouse subjected to the operation with the electrocardiosignal Sbef before the operation and give an evaluation conclusion whether the mouse successfully performs myocardial infarction modeling or not; if the post-operation electrocardiosignal Stime at the t moment is raised relative to the ST segment of the II lead of the pre-operation electrocardiosignal Sbef and the ST segment raising amplitude delta ST is greater than a preset threshold value Y1, it is prompted that the probability that the operation mouse is successfully constructed into the myocardial infarction model mouse is high; and the output module is configured to output the evaluation conclusion. The invention provides a standard for evaluating an electrocardiosignal lead change time point of successful mouse MI modeling in an early stage, so that the stability and the effectiveness of mouse MI disease model research are improved.

Real-time electrocardiogram (“ECG”) monitoring system for wearable devices. Embodiments of the invention are based on parallel Delta modulator architecture with local maximum point and local minimum point algorithms to detect QRS and PT waves. The parallel Delta modulators preferably convert ECG signals to two channels of three-state Delta modulated bitstreams. Using embodiments of the invention, real-time PR and RT intervals, as well as ST segment measurements, can be achieved in long-term wearable ECG recording devices.