Method and system for near real-time analysis and display of electrocardiographic signals

• US 5,348,020 A
• Filed: 12/15/1992
• Issued: 09/20/1994
• Est. Priority Date: 12/14/1990
• Status: Expired due to Fees

• Current Assignee: TSA America, Inc.
• Original Assignee: William H. Hutson
• Inventors: Hutson, William H.
• Primary Examiner(s): Sykes, Angela D.
• Assistant Examiner(s): Getzow, Scott M.
• Application Number:
  US07/991,291
• Time in Patent Office: 644 Days
• Field of Search: 128/696, 128/699, 128/708, 364/413.06
• US Class Current: 600/509
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: G01S 11/14 using ultrasonic, sonic, or infrasonic waves
• CPC Class Codes: G01S 15/899 Combination of imaging systems with ancillary equipment
• CPC Class Codes: G01S 3/802 Systems for determining direction or deviation from predetermined direction
• CPC Class Codes: G01S 7/20 Stereoscopic displays; Three-dimensional displays; Pseudo-three-dimensional displays
• CPC Class Codes: G01S 7/6245 Stereoscopic displays; Three-dimensional displays; Pseudo-three dimensional displays
• CPC Class Codes: G01V 1/28 Processing seismic data, e.g. for interpretation or for event detection G01V1/48 takes precedence
• CPC Class Codes: G01V 2210/20 Trace signal pre-filtering to select, remove or transform specific events or signal components, i.e. trace-in/trace-out
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 2218/02 Preprocessing
• CPC Class Codes: G06F 2218/08 Feature extraction

Abstract

The multidimensional ECG processing and display system of the present invention is used with an electrocardiographic (ECG) monitoring system. Input ECG data from multiple, sequential time intervals is collected and formatted into a two-dimensional matrix. The two-dimensional matrix is decomposed using singular value decomposition (SVD) to obtain its corresponding singular values and singular vectors, a compressed form of the matrix. The singular vectors are analyzed and filtered to identify and enhance signal components of interest. Selected singular vectors are transformed into their frequency domain representations by the Fast Fourier Transform (FFT), or related techniques. Certain data elements in the two-dimensional matrix are enhanced or diminished by modifying the singular values within groups of singular vectors to enhance certain objects that are associated with the ECG data and to diminish other features within the data. The enhanced data is expanded back into its original form and features in the ECG data are displayed as opaque objects within a transparent data cube.

Claims

1. A method of analyzing an electrocardiographic (ECG) signal, for determining diagnostic information comprising:

analyzing the ECG signal to determine enhanced predetermined characteristics of the ECG signal according to the steps of,

extracting data relating to said signals as received by an ECG monitoring system;

embedding said data into a data matrix;

compressing said data matrix into a compressed-data-form;

modifying selected data values in said compressed-data-form based on a function of said data values in said compressed-data-form;

expanding said compressed-data-form to an enhanced data matrix that contains data representing said enhanced predetermined characteristics.

determining from said enhanced data matrix said diagnostic information.

2. The method of claim 1, wherein the method further includes a displaying step for displaying said enhanced data matrix on a display screen to show said characteristics.

3. The method of claim 1, wherein said compressing step further including decomposing said data matrix into singular vectors and singular values.

4. The method of claim 3, wherein at least one said singular vector is modified by said modifying step.

5. The method of claim 3, wherein at least one said singular value is modified by said modifying step.

6. The method of claim 3, wherein the method further includes the step of saving at least one subspace of said compressed-data-form in a historical database.

7. The method of claim 3, wherein the method further includes modifying said data matrix by multiplying at least a portion of said data matrix by a singular vector.

8. The method of claim 3, wherein the method further includes partitioning said compressed-data-form into subspaces.

9. The method of claim 8, wherein said compressed-data-form is partitioned into subspaces on the basis of a function of said singular values.

10. The method of claim 8, wherein said compressed-data-form is partitioned into subspaces on the basis of a function of its singular vectors.

11. The method of claim 1, wherein the method further includes the step of saving at least a portion of said compressed-data-form in a historical database.

12. The method of claim 11, wherein the method further includes the step of concatenating said historical database with said data matrix before compressing said data matrix.

13. The method of claim 11, wherein the method further includes the step of concatenating said historical database with said compressed-matrix-form after compressing said data matrix.

14. The method of claim 11, wherein the method further includes modifying said historical database before saving said database.

15. The method of claim 1, wherein said compressing step further includes:

partitioning said data signals into at least two two-dimensional data matrices;

concatenating said two-dimensional data matrices along a common dimension to form a concatenated two-dimensional data matrix; and

decomposing said concatenated two-dimensional data matrix into singular vectors and singular values, with said compressed matrix form including said singular vectors and singular values.

16. The method of claim 1, wherein said compressing step further includes:

partitioning said data signals into at least a first two-dimensional data matrix and a second two-dimensional data matrix;

decomposing said first two-dimensional data matrix into first singular vectors and first singular values;

concatenating said first singular vectors and said first singular values with said second two-dimensional data matrix; and

decomposing said concatenated second two-dimensional data matrix into second singular vectors and second singular values, with said compressed-matrix form including said second singular vectors and said second singular values.

17. The method of claim 1, wherein said compressing step further includes:

partitioning said data signals into at least a first two-dimensional data matrix and a second two-dimensional data matrix;

decomposing said first two-dimensional data matrix into first singular vectors and first singular values;

decomposing said second two-dimensional data matrix into second singular vectors and second singular values;

concatenating said first singular vectors with said second singular vectors to form concatenated singular vectors; and

decomposing said concatenated singular vectors into final singular vectors and final singular values, with said compressed-matrix form including said final singular vectors and said final singular values.

18. The method of claim 1, said compressing step further including decomposing said data matrix into eigenvectors and eigenvalues.

19. The method of claim 1, wherein said data matrix is three-dimensional.

20. The method of claim 19, wherein one of said dimensions is ECG lead.

21. The method of claim 19, wherein one of said dimensions is heartbeat time history.

22. The method of claim 19, wherein one of said dimensions is composed of waveform segments.

23. The method of claim 1, wherein said data matrix contains data relating to a function of the amplitude of said signals.

24. The method of claim 1, wherein said method further includes scaling said information in said data matrix by multiplying said information at a selected location in said multi-dimensional data matrix by a data coefficient associated with said location.

25. The method of claim 1, wherein the method further includes the step of displaying said enhanced data matrix as an opaque data cube with said enhanced data signals being displayed as objects within said data cube.

26. The method of claim 25, wherein the method further includes the step of selecting portions of said transparent data cube display and displaying said selected portions as two-dimensional data.

27. A method of determining the presence or absence of a predetermined level of high frequency spectral energy in the late QRS signal of an electrocardiograph signal, comprising the steps of:

extracting data relating to said electrocardiograph signal as received by an ECG monitoring system;

embedding said data into a data matrix;

decomposing said data matrix into singular vectors;

segmenting said singular vectors into a series of time intervals;

transforming said segmented singular vectors into a corresponding power spectra form; and

indicating the presence of ventricular late potentials by determining whether said predetermined level of spectral energy is present in said spectra form.

28. The method of claim 27 including the step of alerting an attendant when the presence of ventrical late potentials is indicated.

29. A system for analyzing an electrocardiograph signal, and for determining diagnostic information therefrom comprising:

means for analyzing said ECG signal to determine enhanced predetermined characteristics thereof,

means for extracting data signals relating to said signals as received by an ECG monitoring system;

means for embedding said data into a data matrix;

means for compressing said data matrix into a compressed-data-form;

means for modifying selected data values in said compressed-data-form based on a function of said data values in said compressed-data-form;

means for expanding said compressed-data-form to an enhanced data matrix that contains data representing said enhanced predetermined characteristics,

means for determining said diagnostic information from said enhanced data matrix.

30. The system of claim 29, wherein the system further includes means for displaying said enhanced data matrix on a display screen with said display showing said characteristics of said signal-generating objects.

31. The system of claim 29, wherein said means for compressing further including means for decomposing said data matrix into singular vectors and singular values.

32. The system of claim 31, wherein at least one said singular vector is modified by said modifying means.

33. The system of claim 31, wherein at least one said singular value is modified by said modifying means.

34. The system of claim 31, wherein the system further includes means for modifying said data matrix by multiplying at least a portion of said data matrix by a singular vector.

35. The system of claim 31, wherein the system further includes means for partitioning said compressed-data-form into subspaces.

36. The system of claim 35 wherein said compressed-data-form is partitioned into subspaces on the basis of a function of said singular values.

37. The system of claim 35 wherein said compressed-data-form is partitioned into subspaces on the basis of a function of its singular vectors.

38. The system of claim 35, wherein the system further includes means for saving at least one subspace of said compressed-data-form in a historical database.

39. The system of claim 29, wherein the system further includes means for saving at least a portion of said compressed-data-form in a historical database.

40. The system of claim 39, wherein the system further includes means for concatenating said historical database with said data matrix before compressing said data matrix.

41. The system of claim 39, wherein the system further includes means for concatenating said historical database with said compressed-matrix-form after compressing said data matrix.

42. The system of claim 39, wherein the system further includes means for modifying said historical database before saving said database.

43. The system of claim 29, wherein said means for compressing further includes:

means for partitioning said data signals into at least two two-dimensional data matrices;

means for concatenating said two-dimensional data matrices along a common dimension to form a concatenated two-dimensional data matrix; and

means for decomposing said concatenated two-dimensional data matrix into singular vectors and singular values, with said compressed matrix form including said singular vectors and said singular values.

44. The system of claim 29, wherein said means for compressing further includes:

means for partitioning said data signals into at least a first two-dimensional data matrix and a second two-dimensional data matrix;

means for decomposing said first two-dimensional data matrix into first singular vectors and first singular values;

means for concatenating said first singular vectors and said first singular values with said second two-dimensional data matrix; and

means for decomposing said concatenated second two-dimensional data matrix into second singular vectors and second singular values, with said compressed-matrix form including said second singular vectors and said second singular values.

45. The system of claim 29, wherein said means for compressing further includes:

means for partitioning said data signals into at lease a first two-dimensional data matrix and a second two-dimensional data matrix;

means for decomposing said first two-dimensional data matrix into first singular vectors and first singular values;

means for decomposing said second two-dimensional data matrix into second singular vectors and second singular values;

means for concatenating said first singular vectors with said second singular vectors to form concatenated singular vectors; and

means for decomposing said concatenated singular vectors into final singular vectors and final singular values, with said compressed-matrix form including said final singular vectors and said final singular values.

46. The system of claim 29, wherein said means for compressing further including means for decomposing said data matrix into eigenvectors and eigenvalues.

47. The system of claim 29, wherein said data matrix is three-dimensional.

48. The system of claim 47, wherein one of said dimensions is ECG lead.

49. The system of claim 47, wherein one of said dimensions is heartbeat time history.

50. The system of claim 47, wherein one of said dimensions is waveform segments.

51. The system of claim 29, wherein said data matrix contains data relating to a function of the amplitude of said signals.

52. The system of claim 29, wherein said system further includes means for scaling said information in said data matrix by multiplying said information at a selected location in said multi-dimensional data matrix by a data coefficient associated with said location.

53. The system of claim 29, wherein said system includes means for displaying said enhanced data matrix as a transparent data cube wherein said data signals are displayed as opaque objects within said transparent data cube.

54. The system of claim 53, wherein the system further includes means for selecting portions of said transparent data cube display and means for displaying said selected portions as two-dimensional data.