METHOD AND APPARATUS FOR QUANTITATIVE ASSESSMENT OF CARDIAC ELECTRICAL EVENTS

• US 20090088655A1
• Filed: 07/31/2008
• Published: 04/02/2009
• Est. Priority Date: 08/01/2007
• Status: Active Grant

• Current Assignee: eResearchTechnology, Inc. (Explorer Holdings, Inc.)
• Original Assignee: Newcardio, Inc.
• Inventors: LUNGINOVIC, Alexander D., HADZIEVSKI, Ljupco, VAJDIC, Branislav, BOJOVIC, Bosko
• Granted Patent: US 8,209,002 B2
• Time in Patent Office: Days
• US Class Current: 600/523
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle

Abstract

Method and apparatus for computer enabled analysis of ECG (electro cardiographic) data by exploiting computerized three-dimensional spatial presentation of the measured data using vectors. A three-dimensional presentation of the human heart may be correlated with waveforms specific for standard ECG or derived ECG signals based on the dipole approximation of the heart electrical activity. Additional tools for analyzing ECG data are also provided which may be used to determine the time of cardiac electrical events, to select specific beats for automated cardiac interval determination, and to flag ECGs that have been evaluated by automated means but may benefit by human reading.

Claims

1. A computer enabled method of providing a visualization of cardiac activity, comprising the acts of:

providing an indication of heart electrical activity;

determining a vector interpretation of the indication of heart electrical activity; and

providing a display of the vector interpretation in 3 dimensions.

2. The method of claim 1, wherein the vector interpretation uses a dipole approximation.

3. The method of claim 1, wherein the display is of a particular region of the heart.

4. The method of claim 1 wherein the 3 dimensions define 3 orthogonal axes.

5. The method of claim 1, wherein the indication is for a predetermined time interval;

and

the display is of a cardiac electrical event within the time interval.

6. The method of claim 5, further comprising the acts of:

determining the vector interpretation at a plurality of times within the time interval; and

determining an angular change in the vector interpretation between two of the times.

7. The method of claim 6, further comprising the act of finding an angle between two tangents of the vector interpretation.

8. The method of claim 5, further comprising the acts of:

determining the vector interpretation at a plurality of times within the time interval;

calculating a value for each vector interpretation; and

determining a difference in the value between two of the times.

9. The method of claim 5, wherein providing a display includes associating a function with data points of the vector interpretation within the time interval;

and

identifying the cardiac event as corresponding to a maximum or minimum of the function.

10. The method of claim 9, wherein the function is a polynomial or fitted.

11. The method of claim 10, further comprising the act of determining a weighted integral of a difference between the function and the vector interpretation.

12. The method of claim 5, wherein the time interval begins at a time related to a first peak and ends at a time related to a second peak.

13. The method of claim 5, wherein the event is one of a beginning, end, or peak of one of a P-wave, QRS complex, T-wave, and U-wave.

14. The method of claim 5, wherein the cardiac event is measured by one of a first derivative of the vector interpretation, an attenuation function of the first derivative of the vector interpretation, or a second derivative of the vector interpretation.

15. The method of claim 1, wherein the indication of heart electrical activity is real time data from a patient or stored data.

16. A computing device programmed to carry out the method of claim 1.

17. A computer readable storage medium carrying computer code to carry out the method of claim 1.

18. A computer readable storage medium carrying a file produced by the method of claim 1.

19. A computer enabled apparatus comprising:

an input port for receiving an indication of heart electrical activity;

a storage adapted to store the indication;

a processing portion coupled to the storage to determine a vector interpretation of the electrical activity and provide a display of the vector interpretation in 3 dimensions.

20. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting from the ECG data a time interval that includes a cardiac electrical event;

determining a time-variable heart vector of the data at a plurality of time points within the time interval; and

determining an angular change in the time-variable heart vector between any two time points within the time interval;

wherein the angular change between the time points is greater than a specified minimum.

21. The method of claim 20, wherein the time points are time points within the time interval between which the angular change is equal to or greater than the angular change determined for any other pair of time points within the time interval.

22. The method of claim 20, wherein the ECG data is virtual ECG data.

23. The method of claim 22, wherein the virtual ECG data is vector magnitude data.

24. The method of claim 22, wherein the virtual ECG data is V₁₃ data.

25. The method of claim 21, further comprising determining the angular change using a spatial angle difference.

26. The method of claim 20, wherein the time-variable heart vector is normalized.

27. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting from the ECG data a time interval that includes a cardiac electrical event;

determining a time-variable heart vector {right arrow over (H)}_t from the data at a plurality of time points within the time interval;

determining an angle γ

of the heart vector between any two time points t1, t2 within the time interval;

wherein

$\cos <br><br>\left(\mathrm{\gamma <br><br>}\right)=\frac{{\stackrel{\to <br><br>}{H}}_t<br><br>\left(t<br><br><br><br>1\right)·<br><br>{\stackrel{\to <br><br>}{H}}_t<br><br>\left(t<br><br><br><br>2\right)}{{\stackrel{\to <br><br>}{H}}_t<br><br>\left(t<br><br><br><br>1\right)·<br><br>{\stackrel{\to <br><br>}{H}}_t<br><br>\left(t<br><br><br><br>2\right)}$

and wherein the angle γ

between the two time points is greater than a specified minimum.

28. The method of claim 27, wherein time points t1 and t2 are within the time interval between which the angle γ

is equal to or greater than the angle γ

determined for any other pair of time points within the time interval.

29. The method of claim 27, wherein the time-variable heart vector is normalized.

30. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting from the ECG data a time interval that includes a cardiac electrical event;

determining a time-variable heart vector expressed as a function of orthogonal coordinates X, Y, Z from the data at a time point t within the time interval;

determining a value of K(t) at time point t, wherein K(t) equals

$\frac{\left(X_t^2+Y_t^2+Z_t^2\right)<br><br>\left(X_{\mathrm{tt}}^2+Y_{\mathrm{tt}}^2+Z_{\mathrm{tt}}^2\right)-{\left(X_t<br><br>X_{\mathrm{tt}}+Y_t<br><br>Y_{\mathrm{tt}}+Z_t<br><br>Z_{\mathrm{tt}}\right)}^2}{{\left(X_t^2+Y_t^2+Z_t^2\right)}^3};<br><br>$

wherein the difference in K(t) between time points t1 and t2 is equal to or greater than a specified minimum.

31. The method of claim 30, wherein the difference in K(t) between time points t1 and t2 is equal to or greater than the difference in K(t) determined for any other pair of time points within the time interval.

32. The method of claim 30, wherein the time-variable heart vector is normalized.

33. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting from the ECG data a time interval that includes a cardiac electrical event;

fitting a function to the ECG data with one or more maxima and one or more minima to the ECG data within the time interval; and

identifying the time of the cardiac electrical event as a time within the time interval which corresponds to a maximum or minimum of the function.

34. The method of claim 33, wherein the function is a third-order polynomial.

35. The method of claim 33, further comprising fitting the function using least-square fitting.

36. The method of claim 33, further comprising:

determining a reference level for the ECG data by defining a distribution frequency function of voltages within the time interval; and

setting the reference level equal to the approximate maximum of the distribution frequency function within the time interval.

37. The method of claim 36, wherein the distribution frequency function is a continuous single peak function.

38. The method of claim 37, wherein the continuous single peak function is a Gaussian distribution.

39. The method of claim 33, wherein the ECG data is virtual ECG data.

40. The method of claim 39, wherein the virtual ECG data is V₁₃ data.

41. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting in the ECG data a time interval that begins at approximately a function of a QRS_peak and ends at approximately a QRS_peak;

identifying within the time interval a subset of time points at which the value of a heart vector velocity magnitude of the ECG data is less than approximately 0.01 mV/sec; and

selecting as QRS_on the time point within the subset closest in time to the QRS_peak.

42. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting in the ECG data a time interval that begins at approximately a function of a QRS_peak and ends at approximately a QRS_peak;

identifying within the time interval a subset of time points at which the value of a velocity attenuation of the ECG data is less than approximately 0.01 sec²/mV²; and

selecting as QRS_on the time point within the subset that is closest in time to the QRS_peak.

43. A method for determining the time of a cardiac event Tend, comprising:

providing ECG data;

selecting in the ECG data a time interval that begins at approximately the time point of a T_peak of the ECG data and ends at a first subsequent time point where the magnitude of the ECG data falls to half or less of the magnitude at T_peak;

fitting a polynomial function with one or more maxima and one or more minima to data points of the ECG data within the time interval beginning at the time approximately corresponding to Tpeak and ending at the time approximately corresponding to the time of Tpeak+2.5*(the length of the time interval); and

identifying Tend as the time within the time interval at which there is a local minimum of the polynomial function.

44. A method for determining the time of a cardiac event, comprising:

providing ECG data;

selecting from the ECG data a time interval that includes a cardiac electrical event;

fitting a polynomial function with one or more maxima and one or more minima to data points of the ECG data within the time interval;

identifying the time of a cardiac electrical event a time within the time interval which corresponds to a maximum or minimum of a third-order polynomial function; and

determining a weighted integral of an absolute difference between the polynomial and the ECG data within the time interval, wherein the weighted integral is proportional to;

$\frac{\begin{array}{c}\underset{i=2}{\overset{N-1}{\sum <br><br>}}<br><br><br><br>\mathrm{VM}<br><br>\left(t_i\right)-\mathrm{VMp}<br><br>\left(t_i\right)<br><br>+\\ 1/2<br><br>\left[\begin{array}{c}<br><br>(\mathrm{VM}<br><br>\left(t_1\right)-\mathrm{VMp}<br><br>\left(t_1\right)<br><br>+\\ <br><br>\mathrm{VM}<br><br>\left(t_N\right)-\mathrm{VMp}<br><br>\left(t_N\right)<br><br>\end{array}\right]\end{array}}{\left(N-1\right)<br><br><br><br>\mathrm{VM}<br><br>\left(t_1\right)-\mathrm{VM}<br><br>\left(t_N\right)<br><br>},$

where VM is a vector magnitude of the ECG data, N is an integer and VMp is the value of the third-order polynomial at time t within the time interval;

identifying a subset of the time interval wherein the weighted integral is less than a predetermined value; and

identifying the time of the cardiac electrical event as a time within the subset which corresponds to a maximum or minimum of the polynomial function.

45. A method for selecting ECG complexes upon which to evaluate cardiac events, comprising:

providing ECG data having a plurality of ECG complexes; and

identifying within the plurality an optimal subset of ECG complexes that, relative to any other subset of ECG complexes within the plurality, demonstrates the highest degree of similarity to other ECG complexes in the subset.

46. The method of claim 45, further comprising applying an autocorrelation function to identify the optimal subset.

47. The method of claim 46, wherein the autocorrelation function is

${\int }_{}^{}$

- ∞

∞



f 

( t + τ

) 



f _ 

( t ) 



t 



or 



∫

- ∞

∞



f 

( t ) 



f _ 

( t - τ

) 



t

where τ

is an increment of time t, ƒ

is a function and ƒ

is the complex conjugate of function ƒ

.

48. The method of claim 45, wherein the optimal subset of ECG complexes is the subset of ECG complexes within the plurality of ECG complexes having the lowest standard deviation in an ECG electrical event being evaluated.

49. The method of claim 48, wherein the ECG electrical event is selected from the group consisting of:

PR interval, RR interval, QT interval, rate-corrected QT interval, peak voltage for T wave, peak voltage for R wave, peak voltage for P wave, time interval between Tpeak and Tend, time interval between QRS onset and J point, time interval between P onset and Pend, and time interval between peak voltage for R wave and peak voltage for T wave.

50. The method of claim 45, wherein the optimal subset is identified from virtual ECG data.

51. The method of claim 50, wherein the virtual ECG data is vector magnitude data or normalized vector magnitude data.

52. The method of claim 45, further comprising:

determining a first value of a complex dissimilarity factor for the optimal subset;

selecting from the plurality of ECG complexes a new subset having the same number of ECG complexes as the optimal subset, and with at least one ECG complex different from those included in the optimal subset;

determining a second value of a complex dissimilarity factor for the new subset;

determining if the second value is less than the first value; and

selecting the new subset as a new optimal subset if the second value is less than the first value.

53. A computer-assisted method for assessing quality of an ECG analysis, comprising:

determining a first factor that correlates inversely with noise content of ECG data undergoing analysis throughout the entire range of the ECG data;

determining a second factor that correlates directly with a similarity of a subset of ECG complexes from the ECG data;

assigning a first weight to the first factor and a second weight to the second factor;

determining a weighted average of the first factor and the second factor, the weighted average being determined by calculating a first product of the first weight times the first factor, and a second product of the second weight times the second factor, and calculating an average of the first product and the second product;

selecting a minimum for said weighted average;

comparing the weighted average from the ECG data which has undergone analysis to the minimum; and

determining whether the weighted average is less than, equal to, or greater than, the minimum.

54. The method of claim 53, further comprising:

determining a functionality factor;

determining the value of a third product, the third product being a product of the functionality factor and the weighted average;

comparing the third product to the minimum value; and

determining whether the third product is less than, equal to, or greater than, the minimum value.

55. The method of claim 53, further comprising suggesting for additional analysis ECG data that has a weighted average less than the minimum.

56. The method of claim 53, further comprising suggesting for additional analysis ECG data that has a third product less than the minimum.

57. The method of claim 55, wherein the additional analysis includes a visual inspection.

58. The method of claim 53, wherein the first factor comprises a first quantitative assessment of baseline wander and a second quantitative assessment of high-frequency noise of the ECG data.

59. The method of claim 58, wherein the first quantitative assessment is of the ECG data or of vector magnitude data of the ECG data.

60. The method of claim 53, wherein the second factor comprises a third quantitative assessment of deformities in the time period approximately between Tpeak and Tend in the ECG data, and a fourth quantitative assessment of variability in a cardiac electrical event in a subset of ECG complexes in the ECG data.

61. The method of claim 59, wherein the third quantitative assessment is of T wave deformities in the ECG data.

62. The method of claim 59, wherein the fourth quantitative assessment is variability in QTC, RR interval or Tdiff in the ECG data.

63. The method of claim 53, wherein the first factor is a confidence index for an entire reading and the second factor is a confidence index for functionality.