Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and processing system adapted to monitor respiratory instability by directly calculating a measure of periodicity and amplitude regularity, which is effectively a measure of disorganization, of a respiratory waveform. Normal respiration is a somewhat periodic signal whereas complete cessation of breathing would result in the respiratory signal reflecting measurement noise (i.e. aperiodic with minimal amplitude regularity). Thus, the measure is responsive to changes in the subject's breathing, and can distinguish between normal and abnormal breathing patterns.

Abstract: A system is provided for detecting fluid accumulation in a subject. A movement induced by the beating of the heart is analyzed over time, by monitoring a movement, pressure or force. Changes in density caused by fluid accumulation result in changes in the sensor arrangement signals. Changes are used to indicate that fluid accumulation such as internal bleeding is likely to have taken place.

Abstract: 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.

Abstract: Methods and systems for reducing redundant alarms. The system may receive two or more different alarm sequences to infer relationships therebetween. Upon inferring that the datasets are coupled such that an alarm associated with one of the datasets follows an alarm associated with the other dataset, the system may suppress one of the alarms.

Abstract: 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.

Abstract: 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.