10 results about "Physiological significance" patented technology

The invention provides a quantitative risk assessment method and system for head and neck artery stenosis, and the method comprises the steps: collecting the medical image of the head and neck artery of a patient, the blood pressure of the upper arm artery, and the heart rate; a narrow local form, a cerebral artery network anatomical structure / geometric size and a microcirculation blood flow automatic regulation mechanism are comprehensively incorporated into hemodynamic analysis through a geometric multi-scale modeling technology, and information such as an arterial stenosis far-end and near-end pressure ratio, cerebral artery blood flow and hemodynamic parameters of a narrow part is output; quantitative information support is provided for risk assessment of head and neck artery stenosis; the method has the advantages that patient data are obtained only through noninvasive measurement, and the risk that complications are increased due to invasive measurement is avoided; according to the method, a geometric multi-scale modeling method is adopted to achieve integrated analysis of the three-dimensional flow field and the whole-brain circulation hemodynamic parameters of the stenosis part, the blood flow regulation effect of brain microcirculation is considered, and the physiological significance and clinical application value of the evaluation result can be remarkably improved.

The application discloses a method for generating electrocardiogram based on a diffusion model and synthesizing a customizable cardiac cycle, relates to an electrocardiogram generation method, and aims at solving the problems of the existing electrocardiogram generation method, such as data imbalance, poor privacy protection and the incapability of generating specific pathological signals. The application takes electrocardiogram semantic labels as conditional input, simultaneously inputs noise and diffusion time steps into a deep generation model, and generates electrocardiogram; the deep generation model takes a diffusion model as an overall architecture, introduces a converter model to learn long-term dependencies in electrocardiogram signals, and simultaneously introduces a semantic electrocardiogram batch normalization module to accurately learn local ECG semantic features. The electrocardiogram signal generated by the application can accurately follow the provided electrocardiogram semantic information, customize electrocardiogram with real physiological significance, and improve the data imbalance problem and the privacy protection.

The present invention addresses the problem of defining physiological significance and functions such as the influence of nicotinamide mononucleotide (NMN) contained in breast milk on the development of children. A composition for improving the athletic ability of a child is provided comprising a precursor of NAD. The improvement in athletic ability is preferably any one selected from the group consisting of promotion of skeletal muscle development and improvement in activity. A composition for reducing triglyceride levels in the blood of a child is provided comprising a precursor of NAD. A composition for reducing the blood glucose level in a child is provided comprising a precursor of NAD. A composition for inhibiting fat accumulation in children is provided, comprising a precursor of NAD.

The invention belongs to the technical field of computer vision and aquaculture intelligence, and particularly relates to a living fish activity detection method based on behavior characteristic capture and analysis. According to the method, two attention mechanisms of CBAM and SE are innovatively integrated into the YOLOv11 model for optimization, so that the model can better focus on key features of a fish body, the detection precision and robustness of a small target and fine motion in a complex underwater environment are remarkably improved, and compared with an original model, the mAP of the optimized model is improved to 99%, and the size of the model is reduced to 342 MB. According to the method, the limitation of single feature evaluation is broken through, 'movement speed ', 'movement distance' and'stress jump frequency 'are creatively fused, and a dual calculation and prediction model from behaviors to activity scores is constructed, so that the activity evaluation result is more objective, accurate and comprehensive, and has definite physical and physiological significance.

The application provides a kind of muscle and skeleton disease risk assessment and early warning method and system based on multi-source data, it is related to muscle, skeleton assessment and early warning technical field, the application is by collecting joint angle, electromyography and foot bottom load data, constructs time sequence consistent joint feature vector sequence, effectively make up the limitation of traditional single monitoring means, by K means clustering to joint angle sequence, form standard posture set with physiological significance, and construct transition edge weight based on angle change, muscle activation and load fluctuation weighted calculation, realize the physiological cost quantization expression of different posture transfer path, it is convenient to identify risk transition area, by calculating the deviation between latent variable prediction value and true value, sudden transition behavior in the movement process can be captured, and combined with its edge weight intensity in directed graph, by considering transition intensity and behavior abnormality degree simultaneously, the muscle and skeleton disease risk is effectively warned.

The invention provides a detection method and system for non-invasively evaluating the degree and position of functional myocardial ischemia. The method comprises the following steps: acquiring 12-lead high-frequency electrocardiosignal data of a testee; preprocessing the signal, and extracting a QRS wave band signal; performing signal averaging on the qualified QRS wave bands according to leads to generate 12 average high-frequency QRS; extracting a feature combination reflecting the functional myocardial ischemia and having clinical physiological significance from the signals; mapping leads to the heart apex, the front wall, the dividing wall, the lower wall and the side wall and aggregating regional features; obtaining a region score through linear combination; monotonicity is calibrated as regional ischemia percentage by adopting isotonic regression; and outputting the total heart ischemia percentage and the ischemia position in an equal-weight average manner. The method is highly related to the SPECT / CT gold standard, the degree and position of functional myocardial ischemia can be noninvasively and accurately detected, the method is not limited by detection sites, a new method is provided for checking myocardial ischemia, and the incidence rate of malignant heart diseases is reduced.

The invention provides an in-vitro liver perfusion function evaluation method, device and medium based on hyperspectral imaging, a hyperspectral multi-index quantitative model of the liver in the normal-temperature mechanical perfusion process is established, and mapping calculation of oxygen saturation, hemoglobin, perfusion, moisture and hemoglobin index physiological parameters is achieved; spectral features are directly converted into functional parameters with clear physiological significance through a wave band absorption difference ratio and an integral reflection algorithm; a multi-index fusion model (LVI) is provided, and quantitative coupling between image parameters and metabolic parameters (lactic acid and pH) is achieved; a dynamic time sequence evaluation mechanism is formed, the activity change of the liver tissue can be tracked noninvasively, the function recovery trend can be predicted, and evaluation is provided for liver perfusion maintenance and subsequent liver transplantation.

The invention provides a high-density electromyographic signal generation method based on a muscle cooperative guidance peak diffusion model. The method comprises the implementation steps that a training sample set is acquired; constructing a muscle cooperative guidance peak diffusion model and carrying out iterative training on the muscle cooperative guidance peak diffusion model; and obtaining the generated high-density electromyographic signals. According to the cooperative condition generation branch in the muscle cooperative guidance peak diffusion model, non-negative matrix factorization is performed on a training sample, and a space-time cooperative mode of the training sample is explicitly extracted, so that physiological priori constraints of the training sample are represented; the generated high-density electromyographic signals have a potential space-time cooperation mode and real physiological significance necessary for muscle activities, and data enhancement is performed on the reconstructed high-density electromyographic signals and corresponding training samples, so that the diversity of the generated signals is enriched, and the generation quality is improved; a spike neuron layer in the back diffusion model SpURD realizes sparse calculation during signal reconstruction, and energy consumption in the reconstruction process is reduced.

This invention discloses an intelligent state analysis method for anesthetized patients based on physiological data, comprising the following steps: S1, organizing and annotating historical anesthesia data to form executable state recognition rules and establishing a standardized historical case reference library. The beneficial effects of this invention are: by decomposing key interactive substructures such as nerve-circulation, brain-circulation, and respiration-perfusion from dynamic network profiles, coordinating indicators with clear physiological significance, such as baroreflex function index, brain oxygen-blood pressure coupling coefficient, and ventilation-perfusion matching degree, are calculated; these indicators can quantify the coordination of physiological systems under anesthesia from multiple dimensions; the system performs multi-dimensional similarity matching of these real-time indicators with an internalized anesthesia state knowledge base (containing multiple preset state modes), realizing accurate and interpretable independent determination of the anesthesia depth state in three dimensions: sedation, analgesia, and muscle relaxation, overcoming the limitations of traditional single index monitoring.

The invention discloses a multi-modal feature decoupling and classification method and device for foot physiological statuses, and belongs to the technical field of data mining and medical artificial intelligence. The method comprises the following steps: based on a foot multi-modal physiological feature vector provided by a preorder patent, mapping the vector to a decoupling feature space by using a pre-trained feature decoupling network; obtaining at least one decoupling feature subset related to the specific physiological factor; the physiological state category of the foot is then determined through a classifier based on the decoupled feature subset. According to the method, hidden variables with independent physiological significance can be separated from coupled physiological features, interpretable and high-precision automatic classification of foot physiological states is achieved, and a complete technical closed loop from collection to classification is formed by the method and preorder patents.