A method for providing personalized health assessment of a subject includes: receiving a raw electrocardiography (ECG) signal of a subject from an ECG device and a raw blood pressure (BP) signal of the subject from a BP waveform detector; calculating a beat-to-beat ECG signal from the raw ECG signal; calculating a beat-to-beat BP signal from the raw BP signal; calculating a beat-to-beat pulse arrival time (PAT) signal that is measured as a time delay between the beat-to-beat ECG signal and the beat-to-beat BP signal; calculating an interpolated beat-to-beat PAT signal and an interpolated beat-to-beat BP signal by interpolating the beat-to-beat PAT signal and the beat-to-beat BP signal, respectively; assessing a subject-specific relationship between the interpolated beat-to-beat PAT signal and the interpolated beat-to-beat BP signal; and estimating a real-time blood pressure of the subject based on the subject-specific relationship.

A control unit (12) and method for deriving a measure of arterial compliance based on an acquired arterial volume variation signal and measured diastolic and systolic blood pressure measurements. An oscillometric blood pressure measurement device is used to obtain a first signal representative of arterial volume variations and to obtain blood pressure measurements. Both are measured as an applied pressure to an artery is varied by the oscillometric blood pressure measurement device. The first signal is processed to compile a dataset of values, ΔV, representative of the change in the arterial volume for set step changes, ΔP, in applied pressure, at different transmural pressure values. This set of values is numerically integrated to derive a function of arterial volume with transmural pressure. This function is differentiated to thereby derive a function of arterial compliance with transmural pressure.

A system is provided for deployment of a sensor in a blood vessel comprising: an expandable sensor; at least one anchor element attached to the sensor; an expandable element configured within the sensor such that, in use, expansion of the expandable element causes the sensor to radially expand to fix the at least one anchor element in a wall of the blood vessel.

A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.

In brain analysis, anatomical standardization is performed when analyzing a region of interest (ROI). There are individual differences in the shape and size of the brain and by converting the brain into a standard brain, these differences can be compared with each other and subjected to statistical analysis. When generating a standard brain analysis, a large number of pieces of image data are classified into a plurality of groups based on their anatomical features. An intermediate template that is an intermediate conversion image and a conversion map is calculated for each group, and the calculation of the intermediate template and the generation of the intermediate conversion image are repeated while gradually reducing the number of classifications, so that a final standard image is generated. Using the standard image and the intermediate template calculated during the generation of the standard image, spatial standardization of the measured image is performed.

An image processing system includes at least one processor including hardware. The processor performs a process that acquires, as a processing target image sequence, images captured in a time series manner with an inside of a living body by an endoscope imaging device, a process based on a database generated by using a plurality of in-vivo images captured at earlier timings than timings at which the processing target image sequence is captured, and a process that determines, when a bleeding has been occurring inside the living body, a kind of a desirable bleeding stopping treatment for a blood vessel on which the bleeding has been occurring based on the processing target image sequence and the database to present, to a user, the determined type of the bleeding stopping treatment.

Embodiments are disclosed for a machine learning (ML) gesture recognition with a framework for adding user-customized gestures. In an embodiment, a method comprises: receiving sensor data indicative of a gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn on a limb of the user; generating a current encoding of features extracted from the sensor data using a machine learning model with the features as input; generating similarity metrics between the current encoding and each encoding in a set of previously generated encodings for gestures; generating similarity scores based on the similarity metrics; predicting the gesture made by the user based on the similarity scores; and performing an action on the wearable device or other device based on the predicted gesture.

An apparatus and a method of measuring bioinformation are provided. The apparatus for measuring bioinformation includes a first sensor configured to measure a first biosignal including arterial pulse wave information, a second sensor configured to measure a second biosignal including venous or capillary pulse wave information, and a bioinformation estimator configured to estimate bioinformation of a user based on a time delay between the first biosignal and the second biosignal.

Pressure sensing guidewire assemblies are described herein where the guidewire assembly may be comprised of an elongate guidewire body and multiple pressure sensors secured near or at a distal end of the guidewire body. The signals obtained from the guidewire connectors and aortic sensor modules may be synchronized to minimize signal acquisition delays. The signals may be further processed to equalize the pressure waveforms by shifting the connector waveform to align correctly with the aortic module waveform and improve output signals.

A method of monitoring cardiac dysfunction, such as pericardial effusion, is disclosed. The method uses an indwelling probe inserted within a coronary sinus or a chamber or vessel of the heart, the probe having motion sensing means configured to sense motion of the probe based on movement of the wall of the coronary sinus or other chamber or vessel. Data is obtained from the motion sensing means and processed to monitor for cardiac dysfunction. The monitoring can be in real-time and can utilise one or more three-axis accelerometers. In some embodiments, two or more three-axis accelerometers are spaced longitudinally along an elongate body of the probe, which can increase accuracy and reliability of monitoring.