Non-invasive monitoring of hemodynamic parameters using impedance cardiography

• US 6,161,038 A
• Filed: 10/08/1998
• Issued: 12/12/2000
• Est. Priority Date: 04/08/1996
• Status: Expired due to Fees

• Current Assignee: Rheo-Graphic Pte Limited
• Original Assignee: Rheo-Graphic Pte Limited
• Inventors: Morozov, Aleksandr A., Schookin, Sergei I., Beliaev, Konstantin R., Zubenko, Viatcheslav G., Yong, Wen H.
• Primary Examiner(s): Layno, Carl H.
• Application Number:
  US09/171,138
• Time in Patent Office: 796 Days
• Field of Search: 600/508, 600/513, 600/519
• US Class Current: 600/519
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/0535 Impedance plethysmography for measuring blood flow A61B5/0295
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

A method and apparatus for determination of heart rate, heart stroke volume, heart stroke volume, and cardiac output from thoracic bioimpedance signals and electrocardiograms. A unique bioimpedance electrode arrangement is employed, and the bioimpedance signals are corrected for gain-phase-frequency distortion through the use of sinusoidal test signals through the measuring or detection electrodes to identify distortions and correct for same during actual measurements. Time-derivative bioimpedance signals are employed, the power spectrum calculated, and a novel autoconvolution procedure used to emphasize the heart rate harmonic. Breath waves and other signals not indicative of the patient'"'"'s cardiocycles are removed. Left ventricular ejection time is derived from the bioimpedance signals, and an improved version of Kubicek'"'"'s equation is employed to derive heart stroke volume and thus cardiac output.

Claims

1. A method for processing a bioimpedance signal and electrocardiogram for deriving heart rate, heart stroke volume, and cardiac output comprising:

registering gain-phase-frequency (GPF) characteristics of input analog devices for measuring bioimpedance;

registering gain-phase-frequency (GPF) characteristics of input analog devices for measuring electrocardiogram;

measuring bioimpedance as a function of time over a given time period with said input analog devices for measuring bioimpedance and generating a bioimpedance signal;

measuring electrocardiogram as a function of time over said given time period with said input analog devices for measuring electrocardiogram and generating an electrocardiogram (ECG) signal;

correcting the bioimpedance signal for distortions based on the GPF characteristics previously registered;

correcting the electrocardiogram signal for distortions based on the GPF characteristics previously registered;

determining valid QRS complexes associated with each cardiocycle of said electrocardiogram signal in the given time period;

locating check-points on said valid QRS complexes;

processing one of said ECG signal and said corrected bioimpedance signal to estimate heart rate;

time-differentiating the corrected bioimpedance signal;

determining check-points for each said cardiocycle of the time-differentiated bioimpedance signal in the given time period;

determining effective left ventricular ejection time (ELVET) using said time differentiated bioimpedance check-points in relation to said corresponding QRS check-points;

determining a novel correction factor Z_s-q using said time differentiated bioimpedance check-points in relation to said corresponding QRS check-points;

calculating stroke volume as a function of said ELVET, maximum time-differentiated bioimpedance (dZ/dt)_max, specific blood resistivity (P), distance (L) between two bioimpedance voltage sensing electrodes of the bioimpedance analog input device, baseline bioimpedance (Z₀), said correction factor Z_s-q, and a novel scale factor (K); and

calculating cardiac output by multiplying said stroke volume by said heart rate.

2. The method of claim 1, wherein said registering gain-phase-frequency (GPF) characteristics of said bioimpedance input analog devices comprises determining phase-frequency and gain-frequency characteristics of a transducer employed in detection of said bioimpedance prior to use thereof in said detection.

3. The method of claim 1, wherein said correcting of measured bioimpedance signal comprises digitally filtering and phase correcting said measured bioimpedance to remove distortion in the output of said transducer.

4. The method of claim 1, wherein said registering gain-phase-frequency (GPF) characteristics of said electrocardiogram input analog devices comprise determining phase-frequency and gain-frequency characteristics of a transducer employed in detection of said electrocardiogram prior to use thereof in said detection.

5. The method of claim 1, wherein said correcting of measured electrocardiogram signal comprises digitally filtering and phase correcting said measured electrocardiogram to remove distortion in the output of said transducer.

6. The method of claim 1, wherein said estimating heart rate comprises using a power spectrum of the bioimpedance signal, and an auto-convolution function of the said power spectrum.

7. The method of claim 1, wherein said estimating heart rate comprises processing the electrocardiogram signal.

8. The method of claim 3, wherein said correcting of measured bioimpedance signal further consists of suppressing breath waves to remove undesired power spectra components and generate a bioimpedance signal of restored shape.

9. The method of claim 1, wherein said determining valid QRS complexes comprises determining a distribution of all peaks measured in an electrocardiogram over a period of time (from the front (E₁) and back (E₂) amplitudes of the peaks, calculating the (E₁, E₂) amplitude envelope and rejecting all peaks outside the envelope.

10. The method of claim 4, wherein said determining transducer phase-frequency and gain-frequency characteristics comprises:

generating a high precision sinusoidal impedance signal with peak-to-peak impedance of approximately 0.2 Ohms and baseline impedance of approximately 100 Ohms to 200 Ohms;

connecting said sinusoidal impedance signal to said transducer;

measuring the output from said transducer; and

calculating a gain-phase-frequency characteristic, H(f), of the transducer in a predefined frequency range.

11. The method of claim 10, further comprising generating said high precision sinusoidal impedance signal using a voltage-to-impedance converter including a photoresistor, a photoemitter, a power supply and an analog-to-digital-to analog computer interface.

12. The method of claim 11, further comprising producing through said interface a set of test signals with predefined amplitude and frequency range, detecting an output signal from said transducer, and analyzing said output signal to determine said phase-frequency and said gain-frequency characteristics.

13. The method of claim 10, further comprising employing posterior signal processing to correct linear gain-phase-frequency distortions by converting real operating characteristics of the transducer to predefined characteristics, wherein phase shift is zeroed and gain is assumed to be constant in a predefined frequency range.

14. A method of heart rate estimation, comprising:

calculation of a power spectrum of a bioimpedance signal;

multiplication of said power spectrum by a selected amplitude-frequency function to differentiate the signal and suppress breath harmonics;

autoconvoluting the resulting power spectrum according to the formula

space="preserve" listing-type="equation">AS1(f)=PSa(f)·

PSa(2f)·

PSa(3f) . . . ;

and

determining a maximum amplitude value of autoconvolution in a predefined frequency range as an estimation of heart rate.

15. A method for determining cardiocycles, comprising:

filtering a bioimpedance signal to emphasize fronts of cardiocycles;

calculating a time-amplitude envelope of said cardiocycles by analyzing the first five harmonics of the power spectrum of said bioimpedance signal after said filtration;

selecting said cardiocycle fronts by comparison with said calculated time-amplitude envelope; and

rejecting erroneously-detected fronts.

16. A method of selecting valid cardiocycles from corrected bioimpedance signals to eliminate cardiocycles having interference artifacts, comprising:

detecting time and amplitude relations referencing check points within individuals of a plurality of cardiocycles;

comparing said time and amplitude relations between individuals of a said plurality of cardiocycles; and

further examining selected cardiocycles which exhibit the presence of artifacts according to a plurality of comparison criteria.

17. The method of claim 16, further comprising:

constructing a multi-dimensional vector for each selected cardiocycle;

comparing said multi-dimensional vector with such vectors for other cardiocycles and;

rejecting the cardiocycles with vectors having no neighboring vectors when compared to last 50 valid cardiocycles and other candidate cardiocycles.

18. A method of deriving effective left ventricular ejection time from measured bioimpedance signal and measured electrocardiogram signal, comprising:

filtering said measured bioimpedance signal and suppressing breath waves therein;

filtering said measured electrocardiogram signal;

detecting a valid cardiocycle;

calculating the time-derivative of said bioimpedance signal Y(x);

determining the maximum value of the time-derivative (dZ/dt)_max,

determining effective ejection start time (S-point);

determining effective ejection end time (T-point); and

calculating effective left ventricular ejection time (ELVET) as change in time between effective ejection start time and end time.

19. The method of claim 18, wherein determining effective ejection start time comprises:

determining the global maximum for a given valid cardiocycle of a time-differentiated bioimpedance signal and designating said maximum as point A;

tracing back in time from corresponding point A on electrocardiogram to point S₁ ;

looking for abnormalities in the bioimpedance signal between points A and S_a ;

if there are no said abnormalities, then the cardiocycle is rejected as noisy;

if there are any said abnormalities the one closest to point S_b approached from the right is selected as the ejection start time S;

otherwise the abnormality nearest S_b approached from the left is selected as the ejection start time S.

20. The method of claim 19, wherein determining effective ejection end time comprises:

determining the first (T₁) and second (T₂) local minimums at the time-differentiated bioimpedance signal after point A;

analyzing the depth of the signal curve at each of the first (T₁) and second (T₂) local minimums;

if the depth of the second (T₂) minimum is greater than a predetermined fractional value of the depth of the first (T₁) minimum, selecting the second minimum (T₂) as T₀ ;

otherwise, selecting T₁ as T₀ ; and

identifying the T-point as the nearest local minimum before point T₀ on the graph of the curve generated by the second derivative of Y(x).

21. The method of claim 20, further comprising, after identifying T₁ and T₂ and before identifying the T-point and regardless of relative amplitudes of T₁ and T₂ ;

detecting a back edge of a T-wave in the electron cardiogram signal;

determining if T₁ or T₂ is out of bounds of the back of the T-wave; and

if so, selecting the one of T₁ or T₂ which is not out of bounds as T₀.

22. In the method of claim 19, abnormalities are selected from the group comprising:

dZ/dt zero crossing (Q-point), local minimum in dZ/dt, and local maximum in the third time-derivative of the bioimpedance signal, d³ Z/dt³.

23. A method of determining stroke volume for a patient, comprising:

determining specific blood resistivity P;

measuring a distance L between two bioimpedance electrodes applied to the patient;

determining the base thoracic impedance Z₀ ;

determining ELVET;

determining Δ

Z, impedance changes due to blood influx;

and calculating stroke volume SV according to the equation

space="preserve" listing-type="equation">SV=K·

P·

(L/Z.sub.η

).sup.2 ·

Δ

Z

where K is a novel scale factor related to body composition of the patient.

24. The method of claim 23, further comprising calculating K as

25. The method of claim 24, wherein K₀, K₁, K₂, K₃ are gender and age dependent and lie in ranges of

26. A method of claim 23, wherein determining Δ

Z comprises;

locating the start of QRS complex of an ECG signal and labelling it point Q;

determining the impedance at point S, Z_s ;

determining the impedance at point Q, Z_q ;

calculating the impedance difference Z_s-q between points S and Q;

estimating Δ

Z according to the formula

space="preserve" listing-type="equation">Δ

Z=(dZ/dt).sub.τ

τ

·

ELVET+Z.

27. A method of breath wave suppression for a bioimpedance signal, comprising:

calculating the Fourier transform of the signal;

locating the first and second frequency harmonics of cardiocycles in the calculated spectrum of the signal;

estimating the width of each of the harmonics;

suppressing frequency harmonics below the lower bound of the second harmonic except for harmonics within the bounds of the first frequency harmonic; and

calculating the inverted Fourier transform of the signal.

28. A system for non-invasive monitoring of hemodynamic parameters using detected thoracic bioimpedance and electrocardiogram indications, comprising:

an electrode arrangement adapted for detecting thoracic bioimpedance and electrocardiogram indications of a patient to provide analog signals representative of said indications;

means for correcting errors associated with said analog signals;

means for converting said corrected analog signals to digital signals;

means for processing said digital signals to effectuate at least one of;

estimation of heart rate;

suppression of breath artifact in said bioimpedance signal;

cardiocycle recognition;

arrangement of check points; and

selection of cardiocycles devoid of artifact.

29. The system of claim 28, wherein said means for correcting errors associated with said analog signals comprises:

means for registering gain-phase-frequency (GPF) characteristics of input analog devices for measuring bioimpedance;

means for registering gain-phase-frequency (GPF) characteristics of input analog devices for measuring electrocardiogram;

means for measuring bioimpedance as a function of time over a given time period with said bioimpedance input analog devices and generating a bioimpedance signal;

means for measuring electrocardiogram as a function of time over said given time period with said electrocardiogram input analog devices and generating an electrocardiogram (ECG) signal;

means for correcting the bioimpedance signal for distortions based on the GPF characteristics previously registered; and

means for correcting the electrocardiogram signal for distortions based on the GPF characteristics previously registered.

30. The system of claim 29, wherein said means for processing comprises:

means for determining valid QRS complexes associated with each cardiocycle of said electrocardiogram signal in the given time period;

means for locating check-points on said valid QRS complexes;

means for processing one of said ECG signal and said corrected bioimpedance signal to estimate heart rate;

means for time-differentiating the corrected bioimpedance signal;

means for determining check-points for each said cardiocycle of the time-differentiated bioimpedance signal in the given time period;

determining effective left ventricular ejection time (ELVET) using said time differentiated bioimpedance check-points in relation to said corresponding QRS check-points;

determining a novel correction factor Z_s-q using said time differentiated bioimpedance check-points in relation to said corresponding QRS check-points.

31. The system of claim 29, further comprising means for calculation of stroke volume adaptive to the patient'"'"'s constitution, sex and exhibited features of cardiac stroke waveform.

32. The system of claim 31, wherein said means for calculation of stroke volume comprise:

means for calculating stroke volume as a function of said ELVET, maximum time-differentiated bioimpedance (dZ/dt)_max, specific blood resistivity (P), distance (L) between two bioimpedance voltage sensing electrodes of the bioimpedance analog input device, baseline bioimpedance (Z₀), said correction factor Z_s-q, and a novel scale factor (K).

33. The system of claim 32, further comprising means for calculation of cardiac output from said calculated stroke volume by said estimated heart rate.

34. The system of claim 29, wherein said means for registering gain-phase-frequency (GPF) characteristics of said bioimpedance input analog devices comprises means for determining phase-frequency and gain-frequency characteristics of a transducer employed in detection of said bioimpedance prior to use thereof in said detection.

35. The system of claim 29, wherein said means for correcting of measured bioimpedance signal comprises means for digitally filtering and phase correcting said measured bioimpedance to remove distortion in the output of said transducer.

36. The system of claim 29, wherein said means for registering gain-phase-frequency (GPF) characteristics of said electrocardiogram input analog devices comprises means for determining phase-frequency and gain-frequency characteristics of a transducer employed in detection of said electrocardiogram prior to use thereof in said detection.

37. The system of claim 29, wherein said means for correcting measured electrocardiogram signal comprises means for digitally filtering and phase correcting said measured electrocardiogram to remove distortion in the output of said transducer.

38. The system of claim 28, wherein said processing means is adapted to estimate heart rate using a power spectrum of the bioimpedance signal, and an auto-convolution function of the said power spectrum.

39. The system of claim 28, wherein said estimating heart rate comprises processing the electrocardiogram signal.

40. The system of claim 29, wherein said means for correcting measured bioimpedance signal is adapted to suppress breath waves to remove undesired power spectra components and generate a bioimpedance signal of restored shape.

41. The system of claim 30, wherein said means for determining valid QRS complexes is adapted to determine a distribution of all peaks measured in an electrocardiogram over a period of time (from the front (E₁) and back (E₂) amplitudes of the peaks, calculating the (E₁, E₂) amplitude envelope and rejecting all peaks outside the envelope.

42. The system of claim 34, wherein said means for determining transducer phase-frequency and gain-frequency characteristics is adapted to:

generate a high precision sinusoidal impedance signal with peak-to-peak impedance of approximately 0.2 Ohms and baseline impedance of approximately 100 Ohms to 200 Ohms;

connect said sinusoidal impedance signal to said transducer;

measure the output from said transducer; and

calculate a gain-phase-frequency characteristic, H(f), of the transducer in a predefined frequency range.

43. The system of claim 42, further comprising means for generating said high precision sinusoidal impedance signal using a voltage-to-impedance converter including a photoresistor;

a photoemitter, a power supply and an analog-to-digital-to analog computer interface.

44. The system of claim 43, further comprising means for producing through said interface a set of test signals with predefined amplitude and frequency range, detecting an output signal from said transducer, and analyzing said output signal to determine said phase-frequency and said gain-frequency characteristics.

45. The system of claim 42, further comprising means for employing posterior signal processing to correct linear gain-phase-frequency distortions by converting real operating characteristics of the transducer to predefined characteristics, wherein phase shift is zeroed and gain is assumed to be constant in a predefined frequency range.

46. The system of claim 28, wherein the processing means is adapted to estimate heart rate by:

calculation of a power spectrum of a bioimpedance signal;

multiplication of said power spectrum by a selected amplitude-frequency function to differentiate the signal and suppress breath harmonics;

autoconvoluting the resulting power spectrum according to the formula

space="preserve" listing-type="equation">AS1(f)=PSa(f)·

PSa(2f)·

PSa(3f) . . .

and

determining a maximum amplitude value of autoconvolution in a predefined frequency range as an estimation of heart rate.

47. The system of claim 28, wherein the processing means is adapted to recognize cardiocycles by:

filtering a bioimpedance signal to emphasize fronts of cardiocycles;

calculating a time-amplitude envelope of said cardiocycles by analyzing the first five harmonics of the power spectrum of said bioimpedance signal after said filtration;

selecting said cardiocycle fronts by comparison with said calculated time-amplitude envelope; and

rejecting erroneously detected fronts.

48. The system of claim 28, wherein said processing means is adapted to select valid cardiocycles from corrected bioimpedance signals to eliminate cardiocycles having interference artifacts by:

detecting time and amplitude relations referencing check points within individuals of a plurality of cardiocycles;

comparing said time and amplitude relations between individuals of a said plurality of cardiocycles; and

further examining selected cardiocycles which exhibit the presence of artifacts according to a plurality of comparison criteria.

49. The system of claim 48, wherein said processing means is further adapted to:

construct a multi-dimensional vector for each selected cardiocycle;

compare said multi-dimensional vector with such vectors for other cardiocycles and;

reject the cardiocycles with vectors having no neighboring vectors when compared to last 50 valid cardiocycles and other candidate cardiocycles.

50. The system of claim 30, wherein said processing means is adapted to derive effective left ventricular ejection time from said bioimpedance signal and said electrocardiogram signal by:

filtering said measured bioimpedance signal and suppressing breath waves therein;

filtering said measured electrocardiogram signal;

detecting a valid cardiocycle;

calculating the time-derivative of said bioimpedance signal Y(x);

determining the maximum value of the time-derivative (dZ/dt)_max ;

determining effective ejection start time (S-point);

determining effective ejection end time (T-point); and

calculating effective left ventricular ejection time (ELVET) as change in time between effective ejection start time and end time.

51. The system of claim 50, wherein determining effective ejection start time is effected by:

determining the global maximum for a given valid cardiocycle of a time-differentiated bioimpedance signal and designating said maximum as point A;

tracing back in time from corresponding point A on electrocardiogram to point S_a ;

looking for abnormalities in the bioimpedance signal between points A and S_a ;

if there are no said abnormalities, then the cardiocycle is rejected as noisy;

if there are any said abnormalities the one closest to point S_b approached from the right is selected as the ejection start time S;

otherwise the abnormality nearest S_b approached from the left is selected as the ejection start time S.

52. The system of claim 51, wherein determining effective ejection end time is effected by:

determining the first (T₁) and second (T₂) local minimums at the time-differentiated bioimpedance signal after point A;

analyzing the depth of the signal curve at each of the first (T₁) and second (T₂) local minimums;

if the depth of the second (T₂) minimum is greater than a predetermined fractional value of the depth of the first (T₁) minimum, selecting the second minimum (T₂) as T₀ ;

otherwise, selecting T₁ as T₀ ; and

identifying the T-point as the nearest local minimum before point T₀ on the graph of the curve generated by the second derivative of Y(x).

53. The system of claim 52, wherein said processing means is further adapted to, after identifying T₁ and T₂ and before identifying the T-point and regardless of relative amplitudes of T₁ and T₂ ;

detect a back edge of a T-wave in the electron cardiogram signal;

determine if T₁ or T₂ is out of bounds of the back of the T-wave; and

if so, select the one of T₁ or T₂ which is not out of bounds as T₀.

54. The system of claim 51, wherein abnormalities are selected from the group comprising:

dZ/dt zero crossing (Q-point), local minimum in dZ/dt, and local maximum in the third time-derivative of the bioimpedance signal, d³ Z/dt³.

55. The system of claim 31, wherein said processing means is adapted to determine stroke volume for said patient by:

determining specific blood resistivity P;

measuring a distance L between two bioimpedance electrodes applied to said patient;

determining the base thoracic impedance Z₀ ;

determining ELVET;

determining Δ

Z, impedance changes due to blood influx;

and calculating stroke volume SV according to the equation

space="preserve" listing-type="equation">SV=K·

P·

(L/Z.sub.η

).sup.2 ·

Δ

Z

where K is a novel scale factor related to body composition of said patient.

56. The system of claim 55, wherein:

space="preserve" listing-type="equation">K=K.sub.η

-K.sub.θ

·

(SCHEST/(H.sup.τ

2·

W.sup.τ

     3)),

where

space="preserve" listing-type="equation">SCHEST=(PSCHEST.sup.2 +(PNECK·

PCHEST)+PNECK.sup.2)/12π

.

57. The system of claim 56, wherein K₀, K₁, K₂, K₃ are gender and age dependent and lie in ranges of

58. A system of claim 55, wherein Δ

Z is determined by;

locating the start of QRS complex of an ECG signal and labeling it point Q;

determining the impedance at point S, Z_s ;

determining the impedance at point Q, Z_q ;

calculating the impedance difference Z_s-q between points S and Q;

estimating Δ

Z according to the formula

space="preserve" listing-type="equation">Δ

Z=(dZ/dt).sub.τ

τ

·

ELVET+Z.

59. The system of claim 28, wherein said processing means is adapted to suppress breath artifact by:

calculating the Fourier transform of the signal;

locating the first and second frequency harmonics of cardiocycles in the calculated spectrum of the signal;

estimating the width of each of the harmonics;

suppressing frequency harmonics below the lower bound of the second harmonic except for harmonics within the bounds of the first frequency harmonic; and

calculating the inverted Fourier transform of the signal.

60. The system of claim 59, wherein said electrode arrangement comprises:

an upper influencing electrode placed on the subject'"'"'s head;

a lower influencing electrode placed on the left lower extremity of the subject;

an upper pair of detecting electrodes placed on the subject'"'"'s neck; and

a lower pair of detecting electrodes placed on the trunk of the subject.

61. The system of claim 60, wherein the electrode arrangement placement geometry further comprises:

an upper influencing electrode placed on the subject'"'"'s forehead;

a lower influencing electrode placed in the general area of the subject'"'"'s left knee;

a pair of upper detecting electrodes placed on the subject'"'"'s neck; and

a pair of lower detecting electrodes placed laterally on opposite sides of the subject'"'"'s chest.

62. The system of claim 61, wherein said upper influencing electrode comprises a spot electrode for orientation on vertical and horizontal center lines of said subject'"'"'s forehead.

63. The system of claim 60, wherein said lower influencing electrode comprises a spot electrode, the placement of which satisfies the relationship L<

5R, where L is the vertical distance between said upper and said lower influencing electrodes and R is the radius of said subject'"'"'s chest.

64. The system of claim 60, wherein said upper detecting electrodes comprises a pair of spot electrodes oriented symmetrically on opposite sides of said subject'"'"'s neck along a horizontal line approximately 4 centimeters above the base of said subject'"'"'s neck.

65. The system of claim 61, wherein said lower detecting electrodes further comprise a pair of electrode assemblies, each assembly providing contact surface area between about 12 square centimeters and about 30 square centimeters oriented laterally on opposite sides of said subject'"'"'s chest at approximately xiphoid process level.

66. The system of claim 65, wherein each assembly further comprises four spot electrodes, wherein each spot electrode comprises approximately 4 square centimeters contact surface area with each said spot electrode centered at corners of a square with sides measuring 5 centimeters, and all 4 spot electrodes of said assembly electrically connected to each other.

67. The system of claim 66, wherein the top spot electrodes of each assembly lie on the xiphoid process level of the subject.