An approach for determining an estimated pressure curve for the ventricle of the heart, the method comprising: using data from a motion sensor that has been implanted at the heart to determine the timing of heart cycle events; scaling a reference pressure-time curve including timing of reference heart cycle events in order to fit the reference pressure-time curve to the motion sensor data, the scaling comprising scaling the reference curve along the time axis to fit it to the measured timing of the heart cycle events; and thereby obtaining an estimated pressure-time curve in the form of the scaled reference pressure-time curve.

A system for evaluating a cardiac pumping function includes an oximeter which is attached to a patient for the purpose of recording a pulse oximeter waveform. A computer is connected to the oximeter to receive metric information from the waveform. With this information, the computer determines the value and location of a second derivative acceleration, d.sup.2A/dt.sup.2 in the waveform, which indicates the rate of rise/fall of the waveform. A comparator in the computer then compares this with the value and location of maximum second derivative acceleration, d.sup.2A/dt.sup.2, in earlier waveforms. With this comparison, the computer identifies a trend which can be clinically used to evaluate the efficacy of a cardiac pumping function.

An apparatus for measuring bio-information may include: a pulse wave sensor comprising at least one pair of light emitters which are disposed apart from each other and a light receiver disposed between the at least one pair of light emitters, and configured to measure a plurality of pulse wave signals from an object by using the light receiver and the at least one pair of light emitters; a force sensor configured to measure a contact force that is applied to the pulse wave sensor by the object; and a processor configured to generate an integrated pulse wave signal by integrating the plurality of pulse wave signals based on the contact force and an area of a contact surface of the pulse wave sensor, and estimate bio-information of the object based on the integrated pulse wave signal.

The invention relates to a control device for controlling a measurement system for measuring blood pressure and optionally hemodynamic parameters of a subject. In a first measurement time period T.sub.1, for measured pressure pulses, features are determined, which characterize the respective pressure pulse. Based on the features, start values are determined and, based on the start values, a start curve TPW_F-curve is formed. The measurement system is controlled such that, after the start curve has reached a first maximum, a second measurement time period T.sub.2 succeeds, wherein a blood pressure value is determined based on the pressure measured in the second measurement time period. It has been found that by using the maximum in the first measurement time period for defining a start point for the actual blood pressure measurement, a blood pressure value and optionally also hemodynamic parameters of a subject can be determined very accurately and fast.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Methods for calculating a MetaKG signal are provided. The method including illuminating a region of interest in a sample with a near-infrared (NIR) light source and/or a visible light source. The region of interest includes a sample portion and background portion, each having a different set of optical characteristics. Images of the region of interest are acquired and processed to obtain metadata associated with the acquired images. MetaKG signals are calculated for the region of interest and for the background. The MetaKG signal for the background is used to adjust the MetaKG signal for the region of interest to provide a final adjusted MetaKG signal for the region of interest.

An apparatus and method for estimating one or more hemodynamic parameters such as cardiac output or stroke volume. Embodiments are based on the concept of incorporating information about vascular tone into hemodynamic parameter estimation to improve accuracy. More particularly, embodiments use a measurement of a time duration for a blood pulse to travel from the heart along a certain length of an arterial path as a proxy measure for vascular tone, and incorporate this into hemodynamic parameter estimation. Embodiments are also based on incorporating vascular tone proxy measurements for multiple different arterial paths to take account of vascular tone variations between different portions of the circulatory system.

A method for a wearable device to determine a biological parameter of a tissue of a person. To apply an emitting of a first and a second wavelength of light towards the tissue. To collect and sense a first and a second set of frequency bands from the signals received back from the first and the second wavelengths respectively. The first set of frequency bands represents a first signal which corresponds to a combination of the biological parameter and an extraneous noise. The second set of frequency bands represents a second signal mainly comprising the extraneous noise. To subtract the first set of frequency bands from the second set of frequency bands in the frequency domain to obtain a third set of frequency bands. The third set of frequency bands represents a third signal corresponding to the biological parameter.

A neural network is trained for estimating patient hemodynamic data using a plurality of extravascular imaging data sets and a plurality of intravascular imaging data sets that are each co-registered to a corresponding extravascular imaging data set. A plurality of hemodynamic data sets are provided, each hemodynamic data set co-registered with the corresponding extravascular imaging data set. The neural network learns what hemodynamic data to expect for a given intravascular imaging data set. An intravascular imaging event is subsequently performed in which an intravascular imaging element is translated within a blood vessel of the patient to produce one or more intravascular images. The neural network uses its training to predict hemodynamic values corresponding to the one or more intravascular images from the intravascular imaging event, and the one or more intravascular images are outputted in combination with the predicted hemodynamic values.

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.