A method for non-invasive assessment of intracranial pressure includes providing an image recording device, recording at least one image of a retina part of an eye of a person using said image recording device, identifying, in said at least one image, at least one artery and at least one vein associated with said artery, determining, in said image, a first characteristic diameter value for said identified artery, determining, in said image, a second characteristic diameter value for said identified vein, calculating an arteriovenous ratio, A/V ratio, based on said first and second characteristic diameter values, and comparing said arteriovenous ratio with a threshold value to estimate intracranial pressure.

The invention provides a system and method for measuring vital signs and motion from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

The present disclosure relates to cardiac health assessment system for use with a handheld electronic device for assessing cardiac health of a user and a method for assessing cardiac health of a user. The disclosure further relates to systems and methods for training a machine learning model to estimate intracardiac pressure data.

A system (100) for assessing fluid responsiveness includes an infusion pump (24) in communication with at least one processor (32), and a plurality of physiological monitors (40,42,44,46) operable to receive physiological signals from an associated patient. Physiological signals (48,50) acquired from the associated patient (10) during a fluid challenge are synchronized with a timing signal (54) of the infusion pump (24) administering the fluid challenge. One or more dynamic indices and/or features (58) is calculated from the synchronized physiological signals (50), and one or more dynamic indices and/or features (50) is calculated from baseline physiological signals (48) acquired from the associated patient (10) prior to the fluid challenge. A fluid responsiveness probability value (64) of the patient (10) is determined based on dynamic indices and/or features (58) from the synchronized physiological signals (50) and dynamic indices and/or features (50) from the baseline physiological signals (48).

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A method for non-invasive assessment of peripheral artery disease (PAD) in the peripheral artery/arteries of a patient can include first constructing from medical image data a source model that includes a patient-specific model of the artery/arteries. The method can further include creating a corresponding benchmark model by replacing stenotic segments with idealized segments in the source model and simulating blood flow and blood pressure in the benchmark model to compute reference hemodynamics information. The method can further include generating an assay model by replacing an idealized artery/arteries of interest in the benchmark model with the actual stenotic geometry of the artery/arteries from the source model.

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes a first sensor circuit to sense a first signal indicative of a functional state of the subject, a second sensor circuit to sense a second signal different from the first signal, and a controller circuit. The controller circuit may determine an operating mode of the second sensor circuit according to the sensed first signal, trigger the second senor circuit to sense the second signal under the determined operating mode, and generate a pain score using at least the second signal sensed under the determined operating mode. The pain score may be output to a patient or used for closed-loop control of a pain therapy.

An apparatus for estimating blood pressure may include: a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: extract a cardiac output (CO) feature, a first candidate total peripheral resistance (TPR) feature, and a second candidate TPR feature from a bio-signal; determine one of the first candidate TPR feature and the second candidate TPR feature as a TPR feature based on a direction of change in the CO feature and a direction of change in the first candidate TPR feature between a blood pressure measurement time and a calibration time; and estimate the blood pressure based on the TPR feature and the CO feature.

Machine-learned computational models can be trained to estimate echocardiogram parameters (as conventionally measured by echocardiography) from electrocardiograms and/or electrocardiogram-derived time-domain and/or time-frequency features. In some embodiments, a multi-level model architecture includes a level to derive the echocardiogram parameter estimate(s), with input features to that level being computed in a preceding level, and/or with one or more echocardiogram parameter estimate(s) flowing into a subsequent layer to compute downstream qualitative or quantitative indicators of heart function.

Systems and methods for monitoring patients with respiratory diseases are described. A system may include a sensor circuit to sense a respiration signal and at least one hemodynamic signal. The system may detect a specified respiratory phase from the respiration signal, and generate from the hemodynamic signal one or more signal metrics that are correlative to at least one of a systolic blood pressure, a blood volume, or a cardiac dimension. The system may detect a restrictive or obstructive respiratory condition when the hemodynamic signal metric indicates hemodynamic deterioration during a specified respiratory phase. The system may additionally classify the detected restrictive or obstructive respiratory condition into one of two or more categories, and deliver a therapy based on the detection or the classification.