Other

Streamline Automation LLC

Vital Metrix, Inc.

Rodrigo E Teixeira

Alton J Reich

H Waite H Dykes, Jr

Roberto Di Salvo

The invention comprises a method and apparatus for estimating state of a cardiovascular system, comprising a cardiac stroke volume analyzer, comprising: (1) a blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person; (2) a system processor connected to the blood pressure sensor; and (3) a dynamic state-space model; the system processor receiving cardiovascular input data, from the blood pressure sensor, related to a transient pressure state of the cardiovascular system; at least one probabilistic model, of the dynamic state-space model, operating on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system; iteratively updating the probability distribution function using output from the blood pressure sensor; and processing the probability distribution function to generate a non-pressure state output related to stroke volume of a heart of the person and arterial compliance of the person.

Cardiovascular state determination apparatus and method of use thereof

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.

Iterative probabilistic parameter estimation apparatus and method of use therefor

A probabilistic digital signal processor using data from multiple instruments is described. In one example, an analyzer is configured to: receive discrete first and second input data, related to a first and second sub-system of the system, from a first and second instrument, respectively. A system processor is used to fuse the first and second input data into fused data. The system processor optionally includes: (1) a probabilistic processor configured to convert the fused data into at least two probability distribution functions and (2) a dynamic state-space model, the dynamic state-space model including at least one probabilistic model configured to operate on the at least two probability distribution functions. The system processor iteratively circulates the at least two probability distribution functions in the dynamic state-space model in synchronization with receipt of updated input data, processes the probability distribution functions, and generates an output related to the state of the system.

Probabilistic parameter estimation using fused data apparatus and method of use thereof

A system for non-invasively measuring cardiac output, stroke volume, or both comprises a pulse oximeter, a data processor, and means for generating an output reporting measured one or more CO or SV values to a user. A method for non-invasively measuring cardiac output, stroke volume, or both comprises collecting plethysmographic waveform data of a patient, providing the plethysmographic waveform to a data processor, and calculating measured values for CO or SV. The data processor comprises a mathematical model of the cardiovascular system integrated in a dynamic state space model (DSSM).

System and method for noninvasively measuring ventricular stroke volume and cardiac output

A probabilistic digital signal processor is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates using a physical model on data from a mechanical system or a medical meter or instrument, such as an electrocardiogram. Output of the physical model yields an enhanced output of the original data, an output to a second physical parameter not output by the medical meter, or a prediction, such as an arrhythmia warning.

Biomedical parameter probabilistic estimation method and apparatus

A probabilistic digital signal processor for medical function is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and/or a blood pressure.

Probabilistic biomedical parameter estimation apparatus and method of operation therefor

A probabilistic data signal processor used to determine health of a system is described. Initial probability distribution functions are input to a dynamic state-space model, which iteratively operates on probability distribution functions, such as state and model probability distribution functions, to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled and used as an input to the dynamic state-space model in the iterative algorithm. In various embodiments, the probabilistic data signal processor is used to filter output from any mechanical device using appropriate physical models, which optionally include chemical, electrical, optical, mechanical, or fluid based models. Examples to valve bearings and pipe systems are provided.

Mechanical health monitor apparatus and method of operation therefor

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.

Apparatus for processing physiological sensor data using a physiological model and method of operation therefor

A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and/or enhanced parameter information.

Sensor fusion and probabilistic parameter estimation method and apparatus

A one-step process for the lysis of microalgae cell walls and separation of the cellular lipids for use in biofuel production by utilizing a hydrophilic ionic liquid, 1-butyl-3-methylimidazolium. The hydrophilic ionic liquid both lyses the microalgae cell walls and forms two immiscible layers, one of which consists of the lipid contents of the lysed cells. After mixture of the hydrophilic ionic liquid with a suspension of microalgae cells, gravity causes a hydrophobic lipid phase to move to a top phase where it is removed from the mixture and purified. The hydrophilic ionic liquid is recycled to lyse new microalgae suspensions.

Lipid extraction from microalgae using a single ionic liquid

The invention relates to use of an active ionic liquid to dissolve algae cell walls. The ionic liquid is used to, in an energy efficient manner, dissolve and/or lyse an algae cell walls, which releases algae constituents used in the creation of energy, fuel, and/or cosmetic components. The ionic liquids include ionic salts having multiple charge centers, low, very low, and ultra low melting point ionic liquids, and combinations of ionic liquids. An algae treatment system is described, which processes wet algae in a lysing reactor, separates out algae constituent products, and optionally recovers the ionic liquid in an energy efficient manner.

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Madison, AL, United States of America

Rodrigo E Teixeira