4 results about "Cardiac status" patented technology

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.

The application discloses a non-contact electromagnetic heart monitoring method based on signal semantic deconstruction, comprising the following steps: collecting non-contact reflected electromagnetic signals to generate electromagnetic phase signal data; performing semantic bottleneck modeling to compress and retain key semantic information related to the heart to generate a latent semantic representation tensor; constructing a semantic invariant disturbance signal, which is input into an encoder together with the electromagnetic phase signal for self-supervised reconstruction; constructing an electrocardiogram semantic space and performing cross-modal semantic alignment between the latent semantic representation tensor and the electrocardiogram semantic space; outputting an enhanced latent semantic representation tensor by using an improved TSMixer model; extracting rhythm drift information based on the enhanced latent semantic representation, constructing an individual heart rhythm semantic atlas, and outputting a heart state recognition result. The application realizes semantic-level stable modeling of non-contact heart monitoring, improves the accuracy, robustness and explainability of heart rhythm recognition, and is suitable for intelligent health monitoring and remote medical scenarios.

This application provides a multi-source cardiac state assessment system based on adaptive weight allocation. The system includes: a signal quality assessment module for real-time calculation of the signal quality index of electrocardiogram (ECG), photoplethysmography (PPG), and echocardiography signals; a user motion state recognition module for identifying the user's current motion state; an adaptive weight allocation engine for dynamically allocating fusion weights for the ECG, PPG, and echocardiography signals in the cardiac state assessment based on the signal quality index and the user's motion state; and a cardiac state trend score generator for calculating a comprehensive trend score based on the weighted fusion features. The comprehensive trend score reflects the overall state and trend of cardiac function. This technical solution achieves continuous, personalized, and highly robust monitoring and risk warning of cardiac function.

The application provides a heart state risk prediction model training system, which comprises a data acquisition and preprocessing module, a multi-modal data adaptive fusion module, an attention mechanism-based deep prediction network and a risk prediction and visualization output module. The data acquisition and preprocessing module is used for acquiring multi-modal physiological signal data and preprocessing the data. The multi-modal data adaptive fusion module is used for receiving the preprocessed multi-modal physiological signal data, adaptively weighting and fusing the features of different modalities through a gating fusion mechanism and outputting a fused feature vector. The attention mechanism-based deep prediction network is used for receiving the fused feature vector, extracting time sequence features through a hybrid neural network and outputting a heart disease risk probability. The risk prediction and visualization output module is used for converting the heart disease risk probability into an understandable risk level and visually displaying the risk level. In the above technical solution, the individual differences of different populations and long-term health monitoring requirements are met.