A method and system for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, or parameters proportional thereto, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). This is used, preferably in combination with at least one further physiological parameter, for example heart rate, to derive one or more hemodynamic parameters. A transfer function or machine learning model is used to process the inputs to obtain the estimated hemodynamic parameters.

Systems and methods include differential diagnosis for acute heart failure to provide treatment to a patient including determining whether the patient has cardiac volume overload, determining whether the patient has decreased abdominal venous system volume, and providing the appropriate treatment in response to the determinations. A multi-sensor system may be used to determine cardiac volume and abdominal venous system volume. Fluid redistribution treatment may be provided when cardiac volume overload is accompanied by a decrease in abdominal venous system volume. Fluid accumulation treatment may be provided when cardiac volume overload is not accompanied by a decrease in abdominal venous system volume.

Systems and methods include differential diagnosis for acute heart failure to provide treatment to a patient including determining whether the patient has cardiac volume overload, determining whether the patient has decreased abdominal venous system volume, and providing the appropriate treatment in response to the determinations. A multi-sensor system may be used to determine cardiac volume and abdominal venous system volume. Fluid redistribution treatment may be provided when cardiac volume overload is accompanied by a decrease in abdominal venous system volume. Fluid accumulation treatment may be provided when cardiac volume overload is not accompanied by a decrease in abdominal venous system volume.

The present disclosure relates to an apparatus for predicting a hemodynamics parameter being conventionally obtained from an implanted sensor or catheter (invasive sensor), e.g., pulmonary artery pressure, based on noninvasive biosignals, such as electrocardiographic (ECG), impedance cardio graphic (ICG), phonocardiogram (PCG), pulse oximetry plethysmograph (PPG). The present disclosure also relates to a method of feeding multiple noninvasive biosignals and/or general inputs into an AI model or AI models to predict a hemodynamics parameter, such as pulmonary artery pressure, which is conventionally obtained from an implanted sensor or catheter.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct measurement.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct measurement.

Antibody formulations and methods useful for treatment of patients with AL amyloidosis.

Antibody formulations and methods useful for treatment of patients with AL amyloidosis.

The invention comprises a method for estimating state of a cardiovascular system, comprising the steps of: providing a cardiac analyzer, comprising: a blood pressure sensor, the blood pressure sensor generating a time-varying pressure state waveform output from a portion of a person; a system processor connected to the blood pressure sensor; and a dynamic state-space model of a cardiovascular system, the system processor receiving cardiovascular input data, from the blood pressure sensor, related to a transient pressure state of the cardiovascular system, where at least one probabilistic model, of the dynamic state-space model, operating on the time-varying pressure state waveform output generates a probability distribution function to a non-pressure state of the cardiovascular system. The probability distribution function is iteratively updated using synchronized updated time-varying pressure state waveform output from the blood pressure sensor and a non-pressure state output related to a cardiovascular system parameter is generated.

A bio-information measuring apparatus bio-information measuring method are provided. The bio-information measuring apparatus includes: a pulse wave obtainer configured to obtain a pulse wave signal, and a processor configured to correct a feature of the obtained pulse wave signal based on a variation in an amplitude of the obtained pulse wave signal, and to measure bio-information based on the corrected feature.