Analysis of auscultatory sounds using voice recognition

• US 20060167385A1
• Filed: 08/31/2005
• Published: 07/27/2006
• Est. Priority Date: 01/24/2005
• Status: Abandoned Application

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: 3M Innovative Properties Company (3M Company)
• Inventors: Guion, Marie A.
• Application Number:
  US11/217,129
• Publication Number:
  US 20060167385A1
• Time in Patent Office: Days
• US Class Current: 600/586
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R

Abstract

Techniques are described for analyzing auscultatory sounds to aid a medical professional in diagnosing physiological conditions of a patient. A data analysis system, for example, applies voice recognition and principle component analysis (e.g., singular value decomposition) to auscultatory sounds associated known physiological conditions to define a set of one or more disease regions within a multidimensional space. A diagnostic device, such as an electronic stethoscope or personal digital assistant, applies configuration data from the data analysis system to generate a set of one or more vectors within the multidimensional space representative of auscultatory sounds associated with a patient. The diagnostic device outputs a diagnostic message associated with a physiological condition of the patient based on the orientation of the vectors relative to the disease regions within the multidimensional space.

Claims

1. A method comprising:

applying voice recognition to auscultatory sounds associated with known physiological conditions to generate voice recognition coefficients; and

mapping the coefficients to a set of one or more disease regions defined within a multidimensional space.

2. The method of claim 1, wherein applying voice recognition comprises:

dividing each of the auscultatory sounds into a plurality of sub-bands; and

computing mel-cepstrum coefficients for the sub-bands.

3. The method of claim 1, further comprising outputting a diagnostic message associated with a physiological condition of the patient as a function of the coefficients and the disease regions defined within the multidimensional space.

4. The method of claim 3, wherein outputting a diagnostic message comprises:

selecting one of the disease regions of the multidimensional space; and

outputting the diagnostic message based on the selection.

5. The method of claim 4, wherein selecting one of the disease regions comprises:

computing a plurality of vectors within the multidimensional space from the vectors of coefficients;

identifying which of the vectors has a minimum distance from its respective disease region; and

selecting the disease region associate with the identified vectors.

6. The method of claim 1, wherein each disease region within the multi-dimensional space is defined by characteristics of the auscultatory sounds associated with the known physiological conditions that have been identified as indicators for the respective physiological condition.

7. The method of claim 3, wherein outputting a diagnostic message comprises outputting a pass/fail message that indicates whether an abnormal physiological condition has been detected.

8. The method of claim 3, wherein outputting a diagnostic message comprises outputting a diagnostic message identifying one or more specific pathologies currently being experienced by patient.

9. The method of claim 3, wherein outputting a diagnostic message comprises outputting the diagnostic message to indicate the patient is susceptible to one or more of the physiological conditions.

10. The method of claim 3, wherein outputting a diagnostic message comprises selecting a message type for the diagnostic message based on a user configurable mode.

11. The method of claim 3, wherein the message type comprises one of a pass/fail message type, a suggested diagnosis message type, and a predictive diagnosis message type.

12. The method of claim 5, further comprising outputting a severity indicator based on a calculated distance from at least one of the vectors and a normal region within the multidimensional space.

13. The method of claim 1, wherein mapping auscultatory sounds comprises:

formulating a set of matrices that store the coefficients, wherein each matrix is associated with a different one of the physiological conditions; and

applying singular value decomposition (“

SVD”

) to each of the matrices to compute respective sets of sub-matrices that define the disease regions within the multidimensional space.

14. The method of claim 13, wherein formulating a set of matrices comprises formulating the set of matrices to store digitized representations in a raw format that has not been filtered.

15. The method of claim 13, further comprising storing at least a portion of one or more of the sub-matrices within a database for use as configuration data for a diagnostic device.

16. The method of claim 13, further comprising:

programming a diagnostic device in accordance with configuration data generated by the application of SVD to the set of matrices, wherein the configuration data includes at least one of the sub-matrices associated with the different physiological conditions; and

applying the configuration data with the diagnostic device to a digitized representation of the auscultatory sounds associated with the patient to produce the vectors within the multidimensional space.

17. The method of claim 13, wherein applying SVD comprises applying SVD to decompose a matrix A of the set of matrices into the product of three sub-matrices as:

A=UDV^T,

where U is an N×

M matrix with orthogonal columns, D is an M×

M non-negative diagonal matrix and V is an M×

M orthogonal matrix.

18. The method of claim 17, further comprising:

computing a set of matrices T by pair-wise multiplying each of the computed U matrices with the other U matrices;

performing SVD on each of the resultant matrices T to decompose each matrix T into a respective set of sub-matrices; and

applying the sub-matrices generated from each of the matrices T to identify portions of the U matrices to be used in diagnosis of the patient.

19. The method of claim 18, wherein applying the sub-matrices generated from each of the matrices T comprises applying the sub-matrices generated from each of the matrices T to identify portions of the U matrices that maximize the orthogonality of the respective disease regions within the multidimensional space.

20. The method of claim 13, further comprising computing:

computing respective average vectors from the set of matrices, wherein each average vector represents an average of the digitized representations of the auscultatory sounds associated with the respective physiological conditions; and

applying the average vectors and the configuration data with the diagnostic device to the auscultatory sounds associated with the patient to generate the set of vectors within the multidimensional space.

21. The method of claim 20, wherein applying the average vectors and the configuration data with the diagnostic device comprises:

subtracting the corresponding average vectors from a vector representing the auscultatory sounds associated with the patient to generate a set of difference vectors, wherein each difference vector corresponds to a different one of the disease regions in the multi-dimensional space; and

applying the sub-matrices of the configuration data to the difference vectors to generate the vectors representative of the auscultatory sounds associated with the patient.

22. The method of claim 21, wherein applying the sub-matrices of the configuration data comprises multiplying the difference vectors by the corresponding one of the U sub-matrices to produce a respective one of the vectors representative of the auscultatory sounds associated with the patient.

23. The method of claim 1, wherein mapping auscultatory sounds comprises applying principle component analysis to the voice recognition coefficients to define the disease regions and their boundaries within the multidimensional space.

24. The method of claim 1, wherein mapping auscultatory sounds comprises applying the voice recognition coefficients to a neural network to define the disease regions and their boundaries within the multidimensional space.

25. The method of claim 1, wherein each of the auscultatory sounds associated with known physiological conditions comprises a digitized representation of sounds recorded over a plurality of heart cycles.

26. The method of claim 1, wherein the physiological conditions include one or more of a normal physiological condition, aortic regurgitation, aortic stenosis, tricuspid regurgitation, tricuspid stenosis, pulmonary stenosis, pulmonary regurgitation, mitrial regurgitation, aortic aneurisms, carotid artery stenosis and mitrial stenosis.

27. The method of claim 1, further comprising:

capturing the auscultatory sounds associated with the patient using a first device;

communicating a digitized representation of the captured auscultatory sounds from the first device to a second device;

analyzing the digitized representation with the second device to generate the coefficients; and

outputting the diagnostic message with the second device.

28. The method of claim 27, wherein the first device comprises an electronic stethoscope.

29. The method of claim 27, wherein the second device comprises one of a mobile computing device, a personal digital assistant, and an echocardiogram analyzer.

30. The method of claim 1, further comprising:

capturing the auscultatory sounds associated with the patient using an electronic stethoscope;

analyzing the digitized representation with the electronic stethoscope to generate the coefficients; and

outputting the diagnostic message to a display of the electronic stethoscope.

31. The method of claim 1, wherein the physiological conditions comprise cardiac conditions and the auscultatory sounds associated with the patient comprises heart sounds.

32. The method of claim 1, wherein the auscultatory sounds associated with the patient comprises lungs sounds.

33. A method comprising:

applying voice recognition and singular value decomposition (“

SVD”

) to digitized representations of auscultatory sounds associated with physiological conditions to map the auscultatory sounds to a set of one or more disease regions within a multidimensional space; and

outputting configuration data for application by a diagnostic device based on the multidimensional mapping.

34. The method of claim 33, wherein applying voice recognition and SVD further comprises:

formulating a set of matrices that store digitized representations of the auscultatory sounds associated with the physiological conditions, wherein each matrix is associated with a different one of the physiological conditions and stores the digitized representations of the auscultatory sounds associated with the respective physiological condition; and

applying SVD to each of the matrices to decompose the matrices into respective sets of sub-matrices that define the disease regions within the multidimensional space.

35. The method of claim 34, wherein outputting configuration data comprises storing at least a portion of one or more of the sub-matrices for each of the physiological conditions within a database.

36. The method of claim 34, wherein applying voice recognition and SVD comprises applying SVD to decompose a matrix A of the set of matrices into the product of three sub-matrices as:

A=UDV^T,

where U is an N×

M matrix with orthogonal columns, D is an M×

M non-negative diagonal matrix and V is an M×

M orthogonal matrix.

37. The method of claim 36, further comprising:

computing a set of matrices T by pair-wise multiplying each of the computed U matrices with the other U matrices;

performing SVD on each of the resultant matrices T to decompose each matrix T into a respective set of sub-matrices; and

applying the sub-matrices generated from each of the matrices T to identify portions of the U matrices to be used in diagnosis of the patient.

38. The method of claim 37, wherein applying the sub-matrices generated from each of the matrices T comprises applying the sub-matrices generated from each of the matrices T to identify portions of the U matrices that maximize the orthogonality of the respective disease regions within the multidimensional space.

39. The method of claim 34, further comprising computing:

computing respective average vectors from the set of matrices, wherein each average vector represents an average of the digitized representations of the auscultatory sounds associated with the respective physiological conditions; and

generating the configuration data to include the average vectors.

40. The method of claim 33, wherein the physiological conditions include one or more of a normal physiological condition, aortic regurgitation, aortic stenosis, tricuspid regurgitation, tricuspid stenosis, pulmonary stenosis, pulmonary regurgitation, mitrial regurgitation, aortic aneurisms, carotid artery stenosis and mitrial stenosis.

41. The method of claim 33, wherein applying voice recognition and SVD comprise:

dividing each of the auscultatory sounds into a plurality of sub-bands; and

computing mel-cepstrum coefficients for the sub-bands.

42. A method comprising:

storing within a diagnostic device configuration data generated by the application of voice recognition techniques and principle component analysis (PCA) to digitized representations of electrical recordings associated with physiological conditions, wherein the configuration data maps the electrical recordings to a set of one or more disease regions within a multidimensional space;

applying the configuration data to a digitized representation of an electrical recording associated with a patient to select one of the physiological conditions; and

outputting a diagnostic message indicating the selected one of the physiological conditions.

43. The method of claim 42,

wherein applying the configuration data comprises applying the configuration data to the digitized representation representative of the auscultatory sounds associated with the patient to generate a set of one or more vectors within the multidimensional space;

and

wherein outputting a diagnostic message comprises outputting a diagnostic message as a function of the vectors and the disease regions defined within the multidimensional space.

44. The method of claim 43,

wherein applying the configuration data comprises selecting one of the disease regions of the multidimensional space as a function of orientations of the vectors within the multidimensional space;

and

wherein outputting the diagnostic message comprises outputting the diagnostic message based on the selection.

45. The method of claim 43, wherein each of the vectors correspond to a respective one of the disease regions, and wherein selecting one of the disease regions comprises selecting one of the disease regions as a function of a distance between each of the vectors and the respective disease region.

46. The method of claim 43, wherein the configuration data comprises a sub-matrix generated by the application of PCA to the digitized representations of the auscultatory sounds associated with the known physiological conditions.

47. The method of claim 43, wherein the electrical recordings comprises echocardiograms.

48. The method of claim 43, wherein the electrical recordings comprises digitized representation of auscultatory sounds.

49. A diagnostic device comprising:

a medium that stores data generated by the application of voice recognition to digitized representations of auscultatory sounds associated with known physiological conditions; and

a control unit that applies the configuration data to a digitized representation representative of auscultatory sounds associated with a patient to select one of the physiological conditions, wherein the control unit outputs a diagnostic message indicating the selected one of the physiological conditions.

50. The diagnostic device of claim 49,

wherein the control unit applies the configuration data to the digitized representation representative of the auscultatory sounds associated with the patient to generate a set of one or more vectors within a multidimensional space having a set of defined disease regions, and

wherein the control unit selects one of the physiological conditions based on orientations of the vectors relative to the disease regions within the multidimensional space.

51. The diagnostic device of claim 50, wherein each of the vectors correspond to a respective one of the disease regions, and wherein the control unit selects one of the disease regions as a function of a distance between each of the vectors and the respective disease region.

52. The diagnostic device of claim 50, wherein the configuration data comprises a sub-matrix generated by the application of SVD to the digitized representations of the auscultatory sounds associated with the known physiological conditions.

53. The diagnostic device of claim 49, wherein the diagnostic device comprises one of a mobile computing device, a personal digital assistant, an echocardiogram analyzer, and an electronic stethoscope.

54. A data analysis system comprising:

an analysis module to apply voice recognition and principle component analysis (PCA) to digitized representations of electrical recordings associated with known physiological conditions to map the auscultatory sounds to a set of one or more disease regions within a multidimensional space; and

a database to store data generated by analysis module.

55. The method of claim 54, wherein the electrical recordings comprises echocardiograms.

56. The method of claim 54, wherein the electrical recordings comprises digitized representation of auscultatory sounds.

57. The data analysis system of claim 54,

wherein the analysis module formulates a set of matrices that store the digitized representations of the auscultatory sounds associated with the physiological conditions, wherein each matrix is associated with a different one of the physiological conditions and stores the digitized representations of the auscultatory sounds associated with the respective physiological condition, and

wherein the analysis module applies PCA to each of the matrices to decompose the matrices into respective sets of sub-matrices that define the disease regions within the multidimensional space, and

wherein the analysis module stores within the database at least one of the sub-matrices for each of the disease regions.

58. A computer-readable medium comprising instructions that cause a processor to:

apply configuration data to a digitized representation representative of auscultatory sounds associated with a patient to select one of a set of physiological conditions, wherein the configuration maps the auscultatory sounds to a set of one or more disease regions within a multidimensional space using voice recognition and principle component analysis (PCA); and

output a diagnostic message indicating the selected one of the physiological conditions.

59. The computer-readable medium of claim 58 further comprising instructions to cause the processor to:

apply the configuration data to the digitized representation representative of the auscultatory sounds associated with the patient to generate a set of one or more vectors within the multidimensional space;

select one of the disease regions of the multidimensional space as a function of orientations of the vectors relative to the disease regions within the multidimensional space; and

output the diagnostic message based on the selection.