Abstract
A cardiovascular parameter such as cardiac output is estimated from a current pressure waveform data set without needing to directly measure blood flow or arterial compliance. The general shape of an input flow waveform over one beattobeat cycle is assumed (or computed), and then the parameters of a flowtopressure model, if not predetermined, are determined using system identification techniques. In one embodiment, the parameters thus determined are used to estimate a current peripheral resistance, which is used not only to compute an estimate of the cardiovascular parameter, but also to adjust the shape of the input flow waveform assumed during at least one subsequent beattobeat cycle. Another embodiment does not require computation of the peripheral resistance and still another embodiment computes a flow estimate from an optimized identification of the parameters defining the assumed input flow waveform.
Claims

A method for determining a cardiovascular parameter equal to or derivable from cardiac output (CO) comprising:
inputting a current pressure waveform data set corresponding to arterial blood pressure over a current pressure cycle;
determining defining parameters of an assumed input flow waveform as a function of a peripheral resistance value determined for at least one previous pressure cycle;
determining model parameters of a model of a relationship between the assumed input flow waveform and the current pressure waveform data set;
computing a current peripheral resistance value as a function of the model parameters; and
computing an estimate of the cardiovascular parameter as a function of the current peripheral resistance value and the current pressure waveform data set.
 A method as in claim 1, further comprising determining the defining parameters of the assumed input flow waveform also as a function of shape characteristics of the current pressure waveform data set.
 A method as in claim 2, in which the assumed input flow waveform is a series of component waveforms, with one component waveform per pressure cycle.

A method as in claim 3, in which:
the defining parameters include duration and amplitude; and
the duration of the component waveform for the current pressure cycle is set at least approximately equal to a time interval between systole onset and systole in the current pressure waveform data set.

A method as in claim 4, further comprising:
estimating a diastolic time constant as a product of a sampling rate at which the pressure waveform data set is derived and a function of a model feedback parameter;
estimating an arterial compliance value as a ratio of the diastolic time constant and the peripheral resistance value;
estimating a systolic time constant from chosen points in the current pressure waveform data set;
computing an aortic characteristic resistance value as a ratio of the systolic time constant and the arterial compliance value;
setting the amplitude of the component waveform for the current pressure cycle to be inversely proportional to the square of a function of at least one aortic characteristic resistance value.

A method as in claim 5, further comprising:
computing the mean of a plurality of aortic characteristic resistance values, which will include at least one aortic characteristic resistance value estimated for a previous cycle;
setting the amplitude of the component waveform for the current pressure cycle to be inversely proportional to the square of the product of the mean and a calibration constant.
 A method as in claim 6, further comprising setting the amplitude of the component waveform for the current pressure cycle to be inversely proportional to the square of the product of the mean, the calibration constant, and the arterial compliance value.
 A method as in claim 3, in which the assumed input flow waveform comprises a train of squarewave signals, each forming a respective one of the component waveforms.

A method as in claim 3, further comprising:
setting the amplitude of the component waveform for the current pressure cycle to be proportional to a peaktopeak value of the current pressure waveform data set and inversely proportional to a function of the current peripheral resistance value.

A method as in claim 9, further comprising:
determining a mean value of a plurality of previously estimated peripheral resistance values; and
setting the amplitude of the component waveform for the current pressure cycle to be proportional to the peaktopeak value and inversely proportional to the mean value.

A method as in claim 10, further comprising:
determining a calibration constant; and
setting the amplitude of the component waveform for the current pressure cycle to be proportional to the peaktopeak value and inversely proportional to the mean value scaled by the calibration constant.

A method as in claim 1, in which:
the model is a discrete, autoregressive representation of a multielement Windkessel model of the aorta; and
the model parameters are coefficients of the discrete, autoregressive representation.

A system for determining a cardiovascular value equal to or derivable from cardiac output (CO) comprising:
an arrangement generating a current pressure waveform data set corresponding to arterial blood pressure over a current pressure cycle;
a processing system including:
an input flow waveform generation module comprising computerexecutable code for determining defining parameters of an assumed input flow waveform as a function of a peripheral resistance value determined for at least one previous pressure cycle;
a system parameter identification module comprising computerexecutable code for determining model parameters of a model of a relationship between the assumed input flow waveform and the current pressure waveform data set;
a model parameter computation module comprising computerexecutable code for computing a current peripheral resistance value as a function of the model parameters; and
a cardiovascular value computation module comprising computerexecutable code for computing an estimate of the cardiovascular parameter as a function of the current peripheral resistance value and the current pressure waveform data set.
 A system as in claim 13, in which the system parameter identification module is further provided with computerexecutable code for determining the defining parameters of the assumed input flow waveform also as a function of shape characteristics of the current pressure waveform data set.
 A system as in claim 14, in which the assumed input flow waveform is a series of component waveforms, with one component waveform per pressure cycle.

A system as in claim 15, in which:
the defining parameters include duration and amplitude; and
the duration of the component waveform for the current pressure cycle is set at least approximately equal to a time interval between systole onset and systole in the current pressure waveform data set.
 A system as in claim 15, in which the input flow waveform generation module is further provided for setting the amplitude of the component waveform for the current pressure cycle to be proportional to a peaktopeak value of the current pressure waveform data set and inversely proportional to a function of the current peripheral resistance value.

A system as in claim 17, further comprising:
an averaging module comprising computerexecutable code for determining a mean value of a plurality of previously estimated peripheral resistance values;
in which input flow waveform generation module is further provided for setting the amplitude of the component waveform for the current pressure cycle to be proportional to the peaktopeak value and inversely proportional to the mean value.

A system as in claim 18, further comprising:
a calibration module determining a calibration constant;
in which the input flow waveform generation module is further provided for setting the amplitude of the component waveform for the current pressure cycle to be proportional to the peaktopeak value and inversely proportional to the mean value scaled by the calibration constant.
 A system as in claim 16, in which the assumed input flow waveform is a train of squarewave signals, each forming a respective one of the component waveforms.

A system as in claim 13, in which:
the model is a discrete, autoregressive representation of a multielement Windkessel model of the aorta; and
the model parameters are coefficients of the discrete, autoregressive representation.
Owners (US)

Edwards Lifesciences Corporation
(Jul 11 2005)
Explore more patents:
Applicants

Edwards Lifesciences Corp
Explore more patents:
Inventors

Hatib Feras
Explore more patents:

Roteliuk Luchy
Explore more patents:

Pearce Jeffrey
Explore more patents:
CPC Classifications

A61B5/02007
Explore more patents:

A61B5/02028
Explore more patents:

A61B5/02108
Explore more patents:

A61B5/0215
Explore more patents:

A61B5/029
Explore more patents:
IPC Classifications

A61B5/02
Explore more patents:
Document Preview
 Publication: Jan 26, 2010

Application:
Jul 11, 2005
US 17899905 A

Priority:
Jul 11, 2005
US 17899905 A

Priority:
Apr 13, 2005
US 67076705 P