Koninklijke Philips Corporation N.v.

Rohan Joshi

Murtaza Bulut

Kiran Hamilton J Dellimore

Frank Verbakel

Ron Martinus Laurentius Van Lieshout

Krishnamoorthy Palanisamy

Johannes Antonius Van Rooij

Ralph Wilhelm Christianus Gemma Rosa Wijshoff

Illapha Gustav Lars Cuba Gyllensten

Herman Jan Ter Horst

Sergey Yuryevich Shulepov

A method for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). Additionally, a radial blood velocity profile is computed indicative of blood velocity as a function of radial position across a plane cut perpendicularly across the vessel lumen. This gives an indication of how blood velocity varies for the individual patient across the vessel diameter. This information supplements the standard blood-velocity and arterial diameter measures as inputs to a transfer function which maps the inputs to hemodynamic parameters.

Hemodynamic parameter estimation

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.

Embodiments propose methods and system for predicting the occurrence of critical alarms in response to the occurrence of less severe, non-critical alarms. It is proposed to use a machine-learning model trained to discern whether a non-critical alarm will be followed by a critical alarm within a particular time period, e.g. whether the non-critical alarm will develop into a critical alarm. Unlike existing alarm systems which are merely threshold based, this approach uses physiological data from a window of data. This window of data can be expected to carry more information than a simple breach of the threshold.

Predicting critical alarms

The present disclosure pertains to a system configured to predict decompensation in a subject with heart failure. The system comprises one or more hardware processors configured by machine-readable instructions to receive weight information, blood pressure information, and heart rate information about the subject; determine one or more weight parameters, one or more blood pressure parameters, and one or more heart rate parameters based on the received information; and predict decompensation in the subject based on the one or more weight parameters, the one or more blood pressure parameters, and the one or more heart rate parameters. Prior art systems use weight parameters alone for such prediction. However, weight parameters alone are often not predictive of decompensation.

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Eindhoven, Netherlands

Rohan Joshi