113 results about "Physiological model" patented technology

Methods and apparatus for qualifying and quantifying excitation-dependent physiological information extracted from wearable sensors in the midst of interference from unwanted sources are provided. An organism is interrogated with at least one excitation energy, energy response signals from two or more distinct physiological regions are sensed, and these signals are processed to generate an extracted signal. The extracted signal is compared with a physiological model to qualify and/or quantify a physiological property. Additionally, important physiological information can be qualified and quantified by comparing the excitation wavelength-dependent response, measured via wearable sensors, with a physiological model.

A method and system for physiological image registration and fusion is disclosed. A physiological model of a target anatomical structure in estimated each of a first image and a second image. The physiological model is estimated using database-guided discriminative machine learning-based estimation. A fused image is then generated by registering the first and second images based on correspondences between the physiological model estimated in each of the first and second images.

A method for non-rigid registration and fusion of images with physiological modeled organ motions resulting from respiratory motion and cardiac motion that are mathematically modeled with physiological constraints. A method of combining images comprises the steps of obtaining a first image dataset (24) of a region of interest of a subject and obtaining a second image dataset (34) of the region of interest of the subject. Next, a general model of physiological motion for the region of interest is provided (142). The general model of physiological motion is adapted with data derived from the first image data set (140) to provide a subject specific physiological model (154). The subject specific physiological model is applied (172) to the second image dataset (150) to provide a combined image (122).

The invention discloses a voice enhancing method based on a multiresolution auditory cepstrum system and a deep convolutional neural network. The voice enhancing method comprises the following steps:firstly, establishing new characteristic parameters, namely multiresolution auditory cepstrum coefficient (MR-GFCC), capable of distinguishing voice from noise; secondly, establishing a self-adaptivemasking threshold on based on ideal soft masking (IRM) and ideal binary masking (IBM) according to noise variations; further training an established seven-layer neural network by using new extracted characteristic parameters and first/second derivatives thereof and the self-adaptive masking threshold as input and output of the deep convolutional neural network (DCNN); and finally enhancing noise-containing voice by using the self-adaptive masking threshold estimated by the DCNN. By adopting the method, the working mechanism of human ears is sufficiently utilized, voice characteristic parameters simulating a human ear auditory physiological model are disposed, and not only is a relatively great deal of voice information maintained, but also the extraction process is simple and feasible.

A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.

A probabilistic digital signal processor using data from multiple instruments is described. In one example, a digital signal processor is integrated into a biomedical device. The processor is configured to: use a dynamic state-space model configured with a physiological model of a body system to provide a prior probability distribution function; receive sensor data input from at least two data sources; and iteratively use a probabilistic updater to integrate the sensor data as a fused data set and generate a posterior probability distribution function using all of: (1) the fused data set; (2) an application of Bayesian probability; and (3) the prior probability distribution function. The processor further generates an output of a biomedical state using the posterior probability function.

A pulse oximeter system comprises a data processor configured to perform a method that combines a sigma point Kalman filter (SPKF) or sequential Monte Carlo (SMC) algorithm with Bayesian statistics and a mathematical model comprising a cardiovascular model and a plethysmography model to remove contaminating noise and artifacts from the pulse oximeter sensor output and measure blood oxygen saturation, heart rate, left-ventricular stroke volume, aortic pressure and systemic pressures.

A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step. The comprehensive patient-specific model of the heart representing anatomy, dynamics, hemodynamics, and fluid structure interaction can be used for non-invasive assessment and diagnosis of the heart, as well as virtual therapy planning and cardiovascular disease management. Parameters of the comprehensive patient-specific model are changed or perturbed to simulate various conditions or treatment options, and then the patient specific model is recalculated to predict the effect of the conditions or treatment options.

The invention discloses a remote home health care system. The home health care system comprises a fusion sorting subsystem used for receiving physical sign data parameters collected by a sensor in real time, conducting fusion sorting processing on the physical sign data parameters and diagnosing and feeding back the body condition of a user in real time according to physiological data and physiological models in a physiological model library, a resource optimization subsystem used for regularly optimizing the physiological data in a physiological database, generating an individualized physiological model for the user according to the historical physiological data in the physiological database, storing the physiological model in the physiological model library, and updating the physiological models in the physiological model library according to the latest physiological data in the physiological database and a comprehensive assessment subsystem used for predicting the physical sign change tread and the physical sign dynamic change range of the user according to the physiological data in the physiological database and the physiological models in the physiological model library and assessing the health of the user according to the physiological data and a predicating result.

The invention relates to a self-adapting testing instrument and a testing method for heat-moisture comfort performance of fabric. The testing instrument comprises a testing main body and a testing accessory body, wherein the testing main body comprises a testing head; the testing head sequentially comprises a heating plate, a copper plate, a simulated skin, a simulated skin and a sample clamp clamped with a sample bottom to top; a temperature and humidity sensor is arranged between the sample and the simulated skin; a membrane couple is attached to the surface of the simulated skin device; multiple sweating micropores connected with a control valve of a peristaltic pump are disposed on the simulated skin device; the testing accessory body comprises a clothed human body heat physiological model module, a control module and a surface heat flow calculation module; the surface heat flow calculation module is used for calculating the surface heat flow according to the sweating amount of the sweating micropores and the temperature of the membrane couple; and the clothed human body heat physiological model module is used for real-time sending signals for controlling the peristaltic pump and the heating plate to the control module by taking the calculated surface heat flow as a boundary condition. The self-adapting testing instrument can simulate human body heat physiological characteristics changing along with the ambient change.

Methods and systems for detecting internal hemorrhaging in a person are provided. In an exemplary embodiment, one method includes the steps of measuring physiological conditions associated with the person and processing the measured physiological conditions using a probabilistic network to determine if the person has internal hemorrhaging. The method also includes the steps of determining the severity of any internal hemorrhaging by determining the amount of blood lost by the person and classifying this loss as non-specific, mild, moderate, and severe. The physiological measurements include an electrocardiogram, a photoplethysmogram, and oxygen saturation, respiratory, skin temperature, and blood pressure measurements. The probabilistic network included with one system determines whether there is internal hemorrhaging based on a number of factors including a physiological model, medical personnel inputs, transfer function, statistical, and spectral information, short and long term trends, and previous hemorrhage decisions.

A system for estimating the glucose levels in blood is developed in the present invention. Said system establishes a physiological model of the pulse wave and its energy, which are also correlated with the glucose metabolic function, for generating a fixed length vector containing the values of the previous model combined with other variables related to the user such as, for example, age, sex, height, weight, etc. . . . This fixed length vector is used as an excitation of a function estimation system based on “random forests” for the calculation of the interest variable. The main advantage of this parameter estimation system lays in the fact that it does not apply any restriction a priori on the function to be estimated, and that it is robust in front of heterogeneous data, such as in the case of the present invention.

Decades of investigations were focused on finding “gold standard” for evaluation of plasma dilution and osmolality, blood loss evaluation and prediction of bleeding or transfusion induced changes in hematocrit and hemoglobin concentration. Addressing deficiencies of existing methods, the current invention created new combined mathematical-physiological model applicable to manually operated nomograms and software in medical monitors. The mathematical model HBS Trends is used in blood transfusion and infusion therapy nomogram—HBS Nomogram—which is based on blood hemoglobin concentration and hematocrit. It is also an easy and practical tool for recording and dynamical interpretation of plasma osmolality, blood hemoglobin concentration, hematocrit and mean corpuscular hemoglobin concentration. The HBS Nomogram is a practical system for organizing blood test results in a patient's medical records. It can be used alone or, in line with existing guidelines for infusion and transfusion therapy making them more practical, cost effective and time saving in decision making.

In one embodiment of the invention, a portable device with multiple integrated sensors for vital signs scanning and method of using said device is disclosed. The portable personal scanning device includes multiple sensors such as a plurality of ECG, thermometer, PPG, accelerometer, and microphone for determining a user's vital signs. The method includes concurrently scanning with one or more sensors, validating and enhancing the results of each sensor scan with other concurrent sensor scan and patient interaction models, processing the sensor scans separately or in combination to extract user's vital signs, validating the vital signs extracted by comparison to physiological models, and fusing the similar vital signs extracted from more than one process according to a determination of the measure of quality of the process that produced the vital sign.

The present invention relates to a system for the estimation of the systolic (SBP), diastolic (DBP) and average (MAP) blood pressure. Said system establishes a physiological model of the pulse wave combined with its energy for, afterwards, generating a fixed length vector containing the previous model's values with other variables related to the user like, for example, age, sex, height, weight, etc. . . . This fixed length vector is used as an input of a function estimator system based on “random forests” for the calculation of the three variables of interest. The main advantage of this function estimator lies in that it does not impose any restriction beforehand over the function to be estimated, and it is also very reliable with heterogeneous data, as in the present invention's case.

A method is provided including determining at least one range of phases of a cardiac cycle from which to select a selected phase based on at least one of patient demographic information, patient physiological information, or a general physiological model. The method also includes generating corresponding intermediate images for each of the phases of the at least one range of phases. Further, the method includes selecting the selected phase based on at least one image quality (IQ) metric of the intermediate images. Also, the method includes generating an image for diagnostic use using imaging information from the selected phase.

A biomedical sensor has conducting elements disposed at least partly over a skin-facing surface. A sensing element detects a signal representative of a physiological parameter of a body using the conducting elements. A storage device stores a physiological model. A processor determines sensor placement quality by comparing the signal to the model and operates an indicator to indicate the determined quality. A method of measuring using the sensor includes computing a measurement acceptance criterion using numerous measurements, determining whether a subsequent test measurement corresponds to the measurement acceptance criterion obtained from the computing step, and indicating the results via the indicator. A system for measuring a physiological property of the body includes the sensor, a user interface device to receive measurements from the sensor, and a processor associated with the user interface device and configured to provide feedback if the measurement does not meet a selected acceptance criterion.

A method is described for achieving resolutions beyond the resolution of a diagnostic imaging using multilevel computational analysis. This is accomplished by coupling the diagnostic image with images obtained at higher resolutions or with physiological models constructed at size scales beyond the resolution of the original diagnostic image. Using a discretization function, the image data or models are converted or generated so that they can be processed by a multilevel computational analysis function. The multilevel analysis then mathematically couples the models across two or more length scales generating parameters of interest at each level of analysis. A post-processing function provides the interface for the user to view the parameters of interest in the model derived from the original diagnostic image and/or the image itself and then “zoom-in” to a region by accessing the higher resolution image and/or model associated with it and viewing the parameters of interest.

A pulse oximeter system comprises a data processor configured to perform a method that combines a sigma point Kalman filter (SPKF) or sequential Monte Carlo (SMC) algorithm with Bayesian statistics and a mathematical model comprising a cardiovascular model and a plethysmography model to remove contaminating noise and artifacts from the pulse oximeter sensor output and measure blood oxygen saturation, heart rate, left-ventricular stroke volume, aortic pressure and systemic pressures.