ELIMINATION OF THE EFFECTS OF IRREGULAR CARDIAC CYCLES IN THE DETERMINATION OF CARDIOVASCULAR PARAMETERS

• US 20130053664A1
• Filed: 01/28/2011
• Published: 02/28/2013
• Est. Priority Date: 01/29/2010
• Status: Abandoned Application

• Current Assignee: Edwards Lifesciences Corporation
• Original Assignee: Edwards Lifesciences Corporation
• Inventors: Hatib, Feras, Jian, Zhongping
• Application Number:
  US13/575,586
• Publication Number:
  US 20130053664A1
• Time in Patent Office: Days
• US Class Current: 600/324
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

Methods for determining a cardiovascular parameter, e.g., a parameter reflecting fluid or volume responsiveness, using a modified waveform dataset are described. The waveform dataset corresponds to a signal, for example, from an arterial blood pressure, or any signal proportional to, or derived from the arterial pressure signal. These methods involve identifying individual cardiac cycles in the waveform dataset, measuring the waveform characteristics for the individual cycles, then determining if the individual cardiac cycles are regular cardiac cycles or irregular cardiac cycles. Once any irregular cardiac cycles are detected, a respiratory parameter is measured. Next, a modified waveform dataset containing the waveform characteristics of the regular cardiac cycles and the waveform characteristics of the irregular cardiac cycles is created wherein the waveform characteristics of the irregular cardiac cycles are replaced with estimated waveform characteristics. Finally, a cardiovascular parameter is determined using the modified waveform dataset.

Claims

1. A method of determining a cardiovascular parameter comprising:

receiving a waveform dataset corresponding to an arterial blood pressure, or a signal proportional to, or derived from, the arterial blood pressure signal;

identifying individual cardiac cycles in the waveform dataset;

to measuring waveform characteristics for the individual cardiac cycles;

determining if the individual cardiac cycles are regular cardiac cycles or irregular cardiac cycles;

measuring a respiratory parameter;

creating a modified waveform dataset containing the waveform characteristics of the regular cardiac cycles and the waveform characteristics of the irregular cardiac cycles wherein the waveform characteristics of the irregular cardiac cycles are replaced with estimated waveform characteristics, the estimated waveform characteristics being calculated based on the waveform characteristics of the regular cardiac cycles or a combination of the waveform characteristics of the regular cardiac cycles and the respiratory parameter; and

calculating a cardiovascular parameter using the modified waveform dataset.

2. The method of claim 1, wherein determining if the individual cardiac cycles are regular cardiac cycles or irregular cardiac cycles comprises:

comparing one or more waveform characteristics of the individual cardiac cycle to one or more waveform characteristics of a control cardiac cycle; and

identifying the individual cardiac cycle as an irregular cardiac cycle if the one or more waveform characteristics of the individual cardiac cycle differs from the one or more waveform characteristics of the control cardiac cycle by a predetermined threshold amount.

3. The method of claim 1, wherein replacing the waveform characteristics of the irregular cardiac cycles with estimated waveform characteristics comprises:

determining pseudo-starting points and/or pseudo-ending points for the irregular cardiac cycles; and

directly measuring or calculating waveform parameters/characteristics of the irregular cardiac cycle based on the pseudo-starting points and/or pseudo-ending points.

4. The method of claim 3, wherein determining the pseudo-ending point for an irregular cycle comprises:

determining a time factor for all the regular cardiac cycles;

determining a pressure factor based on the diastolic pressures of the cycles;

identifying a starting point of the irregular cycle and pressure data points that are within a pre-established range from the starting point;

if there is a pressure data point within the pre-established range and the relationship between the pressure data point and the pressure factor is within a predetermined range, that pressure data point is assigned to be the pseudo-ending point; and

if there is more than one pressure data point within the range and the relationship between each of the more than one pressure data points and the pressure factor is within a predetermined range, the pressure data point with a time value that is closest to the time factor is assigned to be the pseudo-ending point.

5. The method of claim 4, wherein the pre-established range from the starting point is the starting point plus (a range start factor*the median time duration) to the starting point plus (a range stop factor*the median time duration).

6. The method of claim 5, wherein the range start factor is 0.9 and the range stop factor is 1.1.

7. The method of claim 3, wherein determining the pseudo-starting point for an irregular cycle comprises:

determining a time factor for all the regular cardiac cycles;

determining a pressure factor based on the diastolic pressures of the cycles;

identifying an ending point of the irregular cycle and pressure data points for the waveform that are within a pre-established range from the ending point;

if there is a pressure data point within the pre-established range and the relationship between the pressure data point and the pressure factor is within a predetermined range, that pressure data point is assigned to be the pseudo-starting point; and

if there is more than one pressure data point within the range and the to relationship between each of the more than one pressure data points and the pressure factor is within a predetermined range, the pressure data point with a time value that is closest to the time factor is assigned to be the pseudo-ending point.

8. The method of claim 7, wherein the pre-established range from the starting point is the starting point plus (a range begin factor*the median time duration) to the starting point plus (a range end factor*the median time duration).

9. The method of claim 8, wherein the range begin factor is 1.1 and the range end factor is 0.9.

10. The method of claim 1, wherein the waveform characteristic is the standard deviation of an irregular cycle.

11. The method of claim 10, wherein the standard deviation of an irregular cycle is calculated by:

determining the systolic pressure of the nearest regular cycle;

determining the standard deviation of the nearest regular cycle;

determining the systolic pressure of the irregular cycle from the respiratory parameter; and

solving Equation 11.

12. The method of claim 2, wherein the predetermined threshold amount is 30% or more.

13. The method of claim 2, wherein the predetermined threshold amount is 25% or more.

14. The method of claim 2, wherein the predetermined threshold amount is 20% or more.

15. The method of claim 2, wherein the predetermined threshold amount is 15% or more.

16. The method of claim 2, wherein the predetermined threshold amount is 10% or more.

17. The method of claim 2, wherein the predetermined threshold amount is 5% or more.

18. The method of claim 2, wherein the predetermined threshold amount is 1% or more.

19. The method of claim 1, wherein the irregular cardiac cycle is a premature ventricular contraction, a premature atrial contraction, a cardiac cycle caused by arrhythmia, a cardiac cycle caused by atrial fibrillation, a patient artifact or a missing cardiac cycle.

20. The method of claim 19, wherein the patient artifact is related to patient movement, electrical interference, or signal noise.

21. The method of claim 2, wherein the control cardiac cycle is a regular cardiac cycle immediately preceding the individual cardiac cycle.

22. The method of claim 21, further comprising comparing the individual cardiac cycle to a regular cardiac cycle immediately following the individual cardiac cycle.

23. The method of claim 2, wherein the control cardiac cycle is a regular cardiac cycle immediately following the individual cardiac cycle.

24. The method of claim 2, wherein the control cardiac cycle is a median cardiac cycle from a sequence containing at least three cardiac cycles.

25. The method of claim 2, wherein the control cardiac cycle is a mean cardiac cycle from a sequence containing at least three cardiac cycles.

26. The method of claim 2, wherein the one or more waveform characteristics is a statistical measurement of a phase of a cardiac cycle.

27. The method of claim 26, wherein the statistical measurement is one of to average, variance, skewness, or kurtosis.

28. The method of claim 26, wherein the phase of a cardiac cycle is one of the entire cardiac cycle, systole, diastole, systolic rise, systolic decay, or overall decay.

29. The method of claim 28, wherein the one or more waveform characteristics is a time interval of the phase of a cardiac cycle.

30. The method of claim 29, wherein the time interval is measured from the time corresponding to the end of the diastolic phase of the previous cardiac cycle.

31. The method of claim 2, wherein the one or more waveform characteristics is the power of a phase of a cardiac cycle.

32. The method of claim 31, wherein the phase of a cardiac cycle is selected from the group consisting of the entire cardiac cycle, systole, diastole, systolic rise, systolic decay, and overall decay.

33. The method of claim 2, wherein the one or more waveform characteristics is one or more frequency characteristics of a phase of a cardiac cycle.

34. The method of claim 33, wherein the phase of a cardiac cycle is selected from the group consisting of the entire cardiac cycle, systole, diastole, systolic rise, systolic decay, and overall decay.

35. The method of claim 2, wherein the one or more waveform characteristics is one or more time-frequency characteristics of a phase of a cardiac cycle.

36. The method of claim 35, wherein the phase of a cardiac cycle is selected from the group consisting of the entire cardiac cycle, systole, diastole, systolic rise, systolic decay, and overall decay.

37. The method of claim 2, wherein the one or more waveform characteristics is a value corresponding to the maximum pressure of a cardiac cycle.

38. The method of claim 2, wherein the one or more waveform characteristics include characteristics corresponding to the maximum pressure of a cardiac cycle and a time interval of the phase of a cardiac cycle.

39. The method of claim 38, wherein the phase of a cardiac cycle is selected from the group consisting of the entire cardiac cycle, systole, diastole, systolic rise, systolic decay, and overall decay.

40. The method of claim 2, wherein the one or more waveform characteristics is the difference between a value corresponding to the diastolic pressure of the beginning of a cardiac cycle and a value corresponding to a maximum pressure of the cardiac cycle.

41. The method of claim 2, wherein the one or more waveform characteristics is the difference between a value corresponding to the maximum pressure of a cardiac cycle and a value corresponding to the diastolic pressure at the end of a cardiac cycle.

42. The method of claim 1, wherein the cardiovascular parameter is stroke volume variation, pulse pressure variation, or systolic pressure variation.

43. The method of claim 1, wherein the cardiovascular parameter is stroke volume.

44. The method of claim 1, wherein the cardiovascular parameter is cardiac output.

45. The method of claim 1, wherein the cardiovascular parameter is systemic vascular compliance.

46. The method of claim 1, wherein the cardiovascular parameter is cardiac flow.

47. The method of claim 1, wherein the cardiovascular parameter is cardiac to flow velocity.

48. The method of claim 1, wherein the cardiovascular parameter is vascular compliance.

49. The method of claim 1, wherein the cardiovascular parameter is vascular elastance.

50. The method of claim 1, further comprising filtering the waveform dataset with a low-pass filter.

51. The method of claim 1, further comprising indicating the position of the estimated cardiac cycles on a graphical user interface.

52. The method of claim 1, further comprising when an irregular cardiac cycle is detected indicating that the waveform contains estimated cardiac cycles on a graphical user interface.

53. The method of claim 1, wherein the waveform dataset is from a sampling period of a set duration.

54. The method of claim 53, wherein when an irregular cardiac cycle is detected, the duration of the sampling period is increased.

55. The method of claim 1, wherein the signal proportional to, or derived from, the arterial blood pressure signal is a pulseox, Doppler ultrasound, or bioimpedance signal.

56. The method of claim 1, wherein the respiratory parameter is the respiratory-induced variation in the waveform dataset caused by mechanical ventilation

57. The method of claim 47, wherein the mechanical ventilation is pressure controlled ventilation, volume controlled ventilation, synchronized intermittent mandatory ventilation, pressure support ventilation, volume support ventilation, high to frequency ventilation, synchronized intermittent positive pressure ventilation, continuous positive pressure ventilation, or non-invasive ventilation.

58. The method of claim 1, wherein the respiratory parameter is the respiratory-induced variation in the waveform dataset caused by spontaneous breathing.

59. The method of claim 1, wherein the estimated waveform characteristics are calculated using mathematical interpolation.

60. The method of claim 50, wherein mathematical interpolation is spline interpolation, polynomial interpolation, exponential interpolation, linear interpolation, nonlinear interpolation, piecewise interpolation, or multivariate interpolation.

61. The method of claim 1, wherein the estimated waveform characteristics are calculated using mathematical curve fitting methods

62. The method of claim 1, wherein the estimated waveform characteristics are calculated using approximating functions