System and Method for Noninvasively Measuring Ventricular Stroke Volume and Cardiac Output

1 . A computer implemented method for non-invasively measuring left ventricular stoke volume (SV) and/or cardiac output (CO) of a subject using a pulse oximeter, said method comprising:

receiving measured plethysmographic waveform data of a subject into a data processor comprising software, said software comprising a mathematical model of a cardiovascular system integrated in a dynamic state space model (DSSM);

calculating a measured value for CO and/or SV using the plethysmographic waveform data; and

reporting the measured SV and/or CO values.

2 . The computer implemented method of claim 1 , further comprising collecting plethysmographic waveform data from the subject.

3 . The computer implemented method of claim 2 , further comprising transmitting the plethysmographic waveform data of the subject to the data processor in real time.

4 . The computer implemented method of claim 1 , wherein said calculating and reporting are performed in real time.

5 . The computer implemented method of claim 1 , wherein the mathematical model of the cardiovascular system comprises aortic pressure, radial pressure, peripheral resistance, aortic impedance, heart rate, stroke volume, and blood density as state or model parameters.

6 . The computer implemented method of claim 1 , wherein the DSSM is integrated with a dual estimation processor engine or a joint estimation processor engine.

7 . The computer implemented method of claim 1 , wherein said reporting the measured SV and/or CO values to a user comprises producing an electronic visual display, a hard copy display, an audible sound, or a tactile output.

8 . The computer implemented method of claim 1 , wherein the mathematical model of the cardiovascular system comprises model and/or state parameters that correspond directly to one or more of: peripheral blood oxygen saturation, heart rate, respiratory rate, and blood pressure.

9 . The computer implemented method of claim 8 , further comprising receiving into the data processor, data of a direct measurement for one or more of SpO 2 , HR, RR, and BP.

10 . The computer implemented method of claim 1 , wherein said software comprises code directing the data processor to:

a) receive system and model parameters for a time t into the DSSM to produce a first probability distribution function (PDF) vector comprising state and model parameters for time t+n;

b) use the first PDF vector and timed data obtained for time t+n from the plethysmographic waveform data in a Bayesian statistical process to produce a second PDF vector for state and model parameters for time t+n;

c) calculate probabilistic expectation values for the state and model parameters for time t+n from the second probability distribution function; and

d) determine a measured value for SV and/or CO for time t+n from probabilistic expectation values for the state and/or model parameters for time t+n

wherein:

the state and model parameters for a time t received into the DSSM in step a) are in the form of a probability distribution function produced from a sampling of expectation values calculated in step c) for an immediately preceding time t−n′; and n and n′ are time intervals that may be the same of different;

the DSSM mathematically represents physiological processes responsible for the measured plethysmographic waveform data and stroke volume to produce a time dependent state representing a time dependent physiological state of the subject;

the DSSM comprises at least one model parameter and/or state parameter representing at least one of total blood volume (TBV), stroke volume (SV), vasomotor tone (VT), and autonomous nervous system (ANS) tone; and

the software comprises code that determines a measured value for at least one of SV, CO, TBV, VT, and ANS tone.

11 . The computer implemented method of claim 10 , wherein said measured value determined in step d) is equal to the value of a model parameter or a state parameter of said DSSM, or is calculated from the value of a model parameter and/or a state parameter of said DSSM.

12 . The computer implemented method of claim 10 , wherein the DSSM is integrated in a joint estimation processing engine.

13 . The computer implemented method of claim 10 , wherein the first PDF is produced using a Sequential Monte Carlo or Sigma Point Kalman Filter method.

14 . The computer implemented method of claim 13 , wherein the Sigma Point Kalman Filter method is an unscented Kalman Filter, a central difference Kalman Filter, a squareroot unscented Kalman Filter, a square-root central difference Kalman Filter, or a combination thereof; and the Sequential Monte Carlo method is an unscented Monte Carlo method, a central difference Monte Carlo method, a square-root unscented Monte Carlo method, a square-root central difference Monte Carlo method, Gaussian Sum Monte Carlo method, Bayes Monte Carlo method, a Gaussian Mixture Sigma Point Monte Carlo method, or any combination thereof.

15 . A non-transitory computer-readable storage medium storing a program that causes a computer to execute the method of claim 1 .