Methods and apparatus for determining a central aortic pressure waveform from a peripheral artery pressure waveform

• US 10,251,566 B2
• Filed: 05/27/2009
• Issued: 04/09/2019
• Est. Priority Date: 05/27/2008
• Status: Active Grant

• Current Assignee: The Board of Trustees of Michigan State University (Michigan State University)
• Original Assignee: The Board of Trustees of Michigan State University (Michigan State University)
• Inventors: Mukkamala, Ramakrishna, Swamy, Gokul, Olivier, Nicholas Bari
• Primary Examiner(s): Weston, Tiffany
• Application Number:
  US12/993,544
• Publication Number:
  US 20110263989A1
• Time in Patent Office: 3,604 Days
• Field of Search: 600485
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms

Abstract

A method is provided for determining a central aortic pressure (AP) wave-form for a subject. The method includes measuring a peripheral artery pressure (PAP) waveform from the subject, employing a distributed model to define a pressure-to-pressure transfer function relating PAP to AP and a pressure-to-flow transfer function relating PAP to a central arterial flow in terms of the same unknown parameters, estimating the unknown parameters by finding the pressure-to-flow transfer function, which when applied to the measured PAP waveform, minimizes the magnitude of the central arterial flow waveform during diastole, and applying the pressure-to-pressure transfer function with the estimated parameters to determine an AP waveform for the subject.

Claims

1. A method for determining a central aortic pressure (AP) waveform for a subject, comprising:

measuring, by a sensor, a peripheral artery pressure (PAP) waveform from the subject;

deriving a mathematical transformation relating PAP to AP using a distributed model, the transformation is defined in terms of unknown parameters;

estimating, by a computing device, the unknown parameters by minimizing a variable representing a magnitude of central arterial flow during diastole when applied to the PAP waveform, wherein estimating the unknown parameters uses only one PAP waveform measured from the subject; and

determining, by the computing device, the AP waveform for the subject by applying the derived mathematical transformation to the measured AP, where the steps of estimating and determining are implement by computer-executable instructions executed by a computer processor of the computing device.

2. The method of claim 1, wherein the distributed model includes one or more parallel tubes representing paths from a central aorta to a peripheral artery and corresponding lumped parameter terminal loads representing the arterial bed distal to the peripheral artery.

3. The method of claim 1 wherein deriving a mathematical transformation further comprises

employing the distributed model to define a pressure-to-pressure transfer function relating PAP to AP in terms of unknown parameters and a pressure-to-flow transfer function relating PAP to a central arterial flow in terms of the same unknown parameters;

estimating the unknown parameters by finding the pressure-to-flow transfer function, which when applied to the measured PAP waveform, minimizes the magnitude of the central arterial flow waveform during diastole.

4. The method of claim 3 wherein the unknown parameters are estimated to provide a minimum variance of the central arterial flow waveform during diastole.

5. The method of claim 3 wherein the unknown parameters are estimated to provide a minimum absolute value of the central arterial flow waveform during diastole.

6. The method of claim 3 wherein the unknown parameters are estimated with a numerical search over a physiologic range of parameter values.

7. The method of claim 3 wherein the unknown parameters are estimated with a local search method.

8. The method of claim 3 further comprises measuring another peripheral artery pressure (PAP) waveform from the subject and updating the unknown parameters using the another PAP waveform.

9. The method of claim 3 further comprises measuring another peripheral artery pressure (PAP) waveform from the subject and applying the derived mathematical transformation to the another PAP waveform.

10. The method of claim 1 further comprises measuring the PAP waveform invasively or non-invasively.

11. A method for determining a central aortic pressure (AP) waveform for a subject, comprising:

measuring, by a sensor, a peripheral artery pressure (PAP) waveform from the subject;

employing a distributed model to define a pressure-to-pressure transfer function relating PAP to AP and a pressure-to-flow transfer function relating PAP to a central arterial flow in terms of the same unknown parameters;

estimating, by a computing device, the unknown parameters by finding the pressure-to-flow transfer function, which when applied to the measured PAP waveform, minimizes a variable representing a magnitude of the central arterial flow waveform during diastole, wherein estimating the unknown parameters uses only one PAP waveform from the subject; and

applying, by the computing device, the pressure-to-pressure transfer function with the estimated parameters to determine an AP waveform for the subject, where the steps of estimating and determining are implemented by computer-executable instructions executed by a computer processor of the computing devices.

12. The method of claim 11, further comprising periodically repeating the steps of determining the AP waveform for the subject.

13. The method of claim 11, further comprising measuring the PAP waveform invasively or non-invasively.

14. The method of claim 13, further comprising measuring the PAP waveform with finger-cuff photoplethysmography or applanation tonometry.

15. The method of claim 11, wherein the distributed model includes one or more parallel tubes representing paths from the central aorta to a peripheral artery and corresponding lumped parameter terminal loads representing the arterial bed distal to the peripheral artery.

16. The method of claim 15, wherein the one or more parallel tubes are at least one of uniform, tapered, and frictionless.

17. The method of claim 15, wherein the corresponding lumped parameter terminal loads are characterized by a gain factor, a single pole, and a single zero.

18. The method of claim 15, wherein the corresponding lumped parameter loads are characterized by a gain factor.

19. The method of claim 15, wherein the corresponding lumped parameter loads are characterized by a gain factor, multiple poles, and multiple zeros.

20. The method of claim 11 wherein the unknown parameters include a wave transmission delay time.

21. The method of claim 20, further comprising measuring the wave transmission delay time non-invasively.

22. The method of claim 21, further comprising measuring the wave transmission delay time one time during a monitoring period of the subject.

23. The method of claim 22, further comprising measuring the wave transmission delay time by applanating a carotid artery and determining a time between an onset of upstroke of a waveform it measures and an onset of upstroke of the measured PAP waveform.

24. The method of claim 21, further comprising continuously approximating the wave transmission delay time using at least one additional measurement.

25. The method of claim 24, wherein the at least one additional measurement includes at least one of surface electrocardiogram and phonocardiogram signals.

26. The method of claim 21 wherein the measured wave transmission delay time is used as an initial value during the step of estimating the unknown parameters.

27. The method of claim 26, wherein values for the wave transmission delay time are derived from the initial value during the step of estimating the unknown parameters.

28. The method of claim 11, further comprising removing a mean value of the PAP waveform before estimating the unknown parameters.

29. The method of claim 11 further comprising calculating the central arterial flow waveform to within a scale factor.

30. The method of claim 29, further comprising identifying diastole from the central arterial flow waveform based on features of the central arterial flow waveform.

31. The method of claim 29, further comprising identifying diastole from the central arterial flow waveform based on features of the central arterial flow waveform and an empirical formula relating cardiac cycle length to systolic ejection interval.

32. The method of claim 31, wherein the empirical formula is Malik'"'"'s formula or Bazett'"'"'s formula.

33. The method of claim 11, wherein the unknown parameters are estimated to provide a minimum variance of the central arterial flow waveform during diastole.

34. The method of claim 11, wherein the unknown parameters are estimated to provide a minimum absolute value of the central arterial flow waveform during diastole.

35. The method of claim 11, further comprising the requirement that the parameter estimates yield physiologic pressure and flow waveforms.

36. The method of claim 35, wherein the physiologic waveforms are defined as one of central arterial flow waveforms with an undershoot during diastole and AP waveforms without double peaks.

37. The method of claim 11, wherein the unknown parameters are estimated with a numerical search over a physiologic range of parameter values.

38. The method of claim 11, wherein the unknown parameters are estimated with a local search method.

39. The method of claim 38, wherein the local search method is the simplex method or Newton'"'"'s method.

40. The method of claim 11, further comprising determining an optimal number of parameters characterizing the distributed model.

41. The method of claim 40, further comprising estimating the unknown parameters for a set of candidate models characterized by a different number of parameters.

42. The method of claim 41, further comprising determining the model in the set of candidate models using criterion that penalize for inclusion of additional parameters.

43. The method of claim 11, further comprising estimating the unknown parameters by minimizing a fit between an average diastolic decay time constant of the AP waveform and a Windkessel time constant determined from the PAP waveform using another method.

44. The method of claim 11, further comprising low-pass filtering the determined AP waveform to reduce noise.

45. The method of claim 11, wherein a constant is added to the determined AP waveform.

46. The method of claim 45, wherein the constant is in a range from 1 to 3 mmHg.

47. The method of claim 11, further comprising monitoring relative change in central arterial flow.

48. The method of claim 11, further comprising determining at least one parameter of the AP waveform.

49. The method of claim 48, wherein the at least one parameter includes systolic pressure, diastolic pressure, pulse pressure, and systolic ejection interval.

50. The method of claim 11, further comprising estimating a cardiovascular variable from the derived AP waveform.

51. The method of claim 50, further comprising estimating the cardiovascular variable using a lumped parameter model.

52. The method of claim 50, wherein the cardiovascular variable is further defined as one of proportional cardiac output, proportional stroke volume, proportional total peripheral resistance, proportional maximum left ventricular elastance, and absolute left ventricular ejection fraction.

53. The method of claim 52, further comprising calibrating the proportional cardiovascular variable to an absolute value using one of a nomogram, a single absolute measurement of cardiac output, and a single absolute measurement of ventricular volume.

54. A system for determining a central aortic pressure (AP) waveform for a subject, comprising:

a sensor configured to obtain a peripheral artery pressure (PAP) waveform from the subject;

a parameter estimation module that estimates unknown parameters of a pressure-to-flow transfer function, from only one PAP wavefrom, by minimizing a variable representing central arterial flow during diastole when the pressure-to-flow transfer function is applied to the PAP waveform, where the pressure-to-flow transfer function relates PAP to a central arterial flow in terms of the unknown parameters; and

a waveform estimation module that applies a pressure-to-pressure transfer function with the estimated parameters to the PAP waveform to determine an AP waveform for the subject, where the pressure-to-pressure transfer function relates PAP to AP in terms of the same unknown parameters, where the parameter estimation module and the waveform estimation module are implemented by computer-executable instructions executed by a computer processor of a computing device.