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).

The present application discloses an apparatus for determining a blood pressure of a subject, the apparatus includes a sensor assembly configured to measure a pulse wave signal of the subject; and a signal processor configured to generate a metric of the pulse wave signal based on the pulse wave signal, to select a blood pressure calculation algorithm base on the metric of the pulse wave signal, and to determine the blood pressure of the subject using the blood pressure calculation algorithm.

A system is provided for displaying heart graphic information relating to sources and source locations of a heart disorder to assist in evaluation of the heart disorder. A heart graphic display system provides an intra-cardiogram similarity (ICS) graphic and a source location (SL) graphic. The ICS graphic includes a grid with the x-axis and y-axis representing patient cycles of a patient cardiogram with the intersections of the patient cycle identifiers indicating similarity between the patient cycles. The SL graphic provides a representation of a heart with source locations indicated. The source locations are identified based on similarity of a patient cycle to library cycles of a library cardiogram of a library of cardiograms.

The invention provides methods and systems for continuous noninvasive measurement of vital signs such as blood pressure (cNIBP) based on pulse arrival time (PAT). The invention uses a body-worn monitor that recursively determines an estimated PEP for use in correcting PAT measurements by detecting low frequency vibrations created during a cardiac cycle, and a state estimator algorithm to identify signals indicative of aortic valve opening in those measured vibrations.

A system for processing cardiac activation information associated with a complex rhythm disorder identifies a location of the heart rhythm disorder by determining activations within cardiac signals obtained at neighboring locations of the heart and arranging the activations to identify an activation trail. The activation trail may define a rotational pattern or radially emanating pattern corresponding to an approximate core of the heart rhythm disorder.

Method and system for monitoring the autonomic nervous system (ANS) of a subject, comprising acquiring at least one physiological signal having W heartbeats, generating data which is a function of the variability of the heart rate on the W heartbeats, with the HRV having W1 RR intervals separating two consecutive heartbeats detected respectively at the moments tk1 and tk, each RR interval having a duration of value a=tt1 with k=(XW+2) . . . X; calculating at least one parameter taken from the parameter TAS(tx) representing the level of activity of the sympathetic system and/or the parameter TAP(tx) representing the level of activity of the parasympathetic system; and/or other parameters such as the parameter SL(tx) representing stress level of the subject at the moment tx and calculated using the equation such that SL(tx)=100+TAS(tx)TAP(tx); and supplying data representative of at least one parameter.

Methods and systems are provided for detecting arrhythmias in cardiac activity. The methods and systems declare a current beat, from the CA signals, to be a candidate beat or an ineligible beat based on whether the current beat satisfies the rate based selection criteria. The determining and declaring operations are repeated for multiple beats to form an ensemble of candidate beats. The method and system calculate a P-wave segment ensemble from the ensemble of candidate beats, perform a morphology-based comparison between the P-wave segment ensemble and at least one of a monophasic or biphasic template, declare a valid P-wave to be present within the CA signals based on the morphology-based comparison, and utilize the valid P-wave in an arrhythmia detection process to determine at least one of an arrhythmia entry, arrhythmia presence or arrhythmia exit.

Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

A heartbeat analyzing method and a heartbeat analyzing system are provided. The heartbeat analyzing method includes: sensing a user using a wearable device and acquiring a physiological signal record; performing a dispersion calculation to the physiological signal record using the wearable device and generating a Poincar plot of the physiological signal record; and inputting the Poincar plot into a heart rhythm classifier model and determining a heartbeat classification of the user based on personal health data of the user.

Systems and methods for classifying a cardiac arrhythmia are discussed. An exemplary system includes a correlator circuit to generate autocorrelation sequences using information of cardiac activity of a subject, including signal segments taken from a cardiac signal at respective elapsed time with respect to reference time. The correlator circuit can generate a correlation image using the autocorrelation sequences. The correlation image may be constructed by stacking the autocorrelation sequences according to the elapsed time of signal segments. An arrhythmia classifier circuit can classify the cardiac activity of the subject as one of arrhythmia types using the correlation image.