Methods, systems, and computer program products for analyzing cardiovascular sounds using eigen functions

• US 20050234349A1
• Filed: 02/08/2005
• Published: 10/20/2005
• Est. Priority Date: 02/10/2004
• Status: Active Grant

• Current Assignee: East Carolina University (University of North Carolina System)
• Original Assignee: East Carolina University (University of North Carolina System)
• Inventors: Brock, Robert Scott, Pravica, David William, Day, Orville W. Jr.
• Granted Patent: US 7,621,875 B2
• Time in Patent Office: Days
• US Class Current: 600/481
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 7/045 Detection of Korotkoff sounds A61B5/0225 takes precedence; measuring blood pressure A61B5/021

Abstract

Non-invasive methods for detecting arterial disease in vivo include obtaining acoustic signals from a sensor held on an external body region proximate an artery. A complex frequency grid of frequencies and associated lifetimes of the obtained acoustic signals is generated. A predictive model of complex frequencies associated with peak-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with early stage arterial disease is provided. A predictive model of complex frequencies associated with line-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with later stage arterial disease and thickening of arterial junctions is also provided. It is determined whether peak and/or line-perturbation acoustic signals of the predictive models are present to detect whether the subject has arterial disease.

Claims

1. A non-invasive method of detecting arterial disease in vivo, comprising:

obtaining acoustic signals from one or more sensors held on an external body region proximate at least one artery;

generating a complex frequency grid of frequencies and associated lifetimes of the obtained acoustic signals;

providing a predictive model of complex frequencies associated with peak-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with early stage arterial disease;

providing a predictive model of complex frequencies associated with line-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with later stage arterial disease an/or the thickening of arterial junctions; and

determining whether peak and/or line-perturbation acoustic signals of the predictive models are present to detect whether the subject has arterial disease.

2. A method according to claim 1, wherein the generating step comprises:

generating a frequency scatter-plot of data corresponding to the obtained acoustic signals in the complex frequency grid; and

wherein the determining step assesses whether any of the generated complex frequencies lie substantially in ellipses with a plurality of frequencies on each ellipse to determine the presence of line-perturbations.

3. A method according to claim 1, wherein the generating step comprises generating a frequency scatter-plot of data corresponding to the obtained acoustic signals in the complex frequency grid;

and wherein the determining step assesses whether the generated complex frequencies are isolated complex frequencies of low frequency to determine the presence of peak-perturbations.

4. A method according to claim 1, wherein the obtaining step comprises detecting acoustic signals associated with the carotid artery of a subject using one or more external sensors.

5. A method according to claim 4, wherein the one or more external sensors are on a cuff configured for placement on the subject'"'"'s neck.

6. A method according to claim 1, wherein providing a predictive model of complex frequencies associated with peak-perturbation acoustic signals comprises determining eigenfunctions corresponding to characteristic peak-perturbations of flow through the artery.

7. A method according to claim 6, wherein the determining step comprises identifying frequencies and associate lifetimes from the frequency grid and identifying eigenvalues corresponding to the eigenfunctions based on the identified frequencies and associated frequency lifetimes.

8. A method according to claim 7, further comprising characterizing the condition of the artery based on the eigenvalues.

9. A method according to claim 7, wherein identifying eigenvalues corresponding to the eigenfunctions comprises determining a pattern of resonance frequencies.

10. A method according to claim 9, wherein determining a pattern of resonance frequencies comprises determining ratios of the resonance frequencies.

11. A method according to claim 10, wherein determining a pattern of resonance frequencies for uniform flow speed is U₀ in a blood vessel of width h, for blood having a viscosity ν

₀ for a simple boundary perturbation with peak curvature κ

, comprises identifying a family of complex frequencies having the formula;

f ˜

0.212κ

U₀−

5.35ν

₀κ

²

12. A method according to claim 7, wherein the eigenfunctions comprise a complex scaling applied to a conformal transformation of the Navier-Stokes equation:

∂

_t{right arrow over (q)}+{tilde over (q)}·

{tilde over (∇

)}{right arrow over (q)}=−

ρ

₀<sup>−

1</sup>{right arrow over (∇

)}p+ν

₀∇

²{right arrow over (q)}, 

 

(2.1a)
{right arrow over (∇

)}·

{right arrow over (q)}=0,  

 

(2.1b)

13. A method according to claim 1, wherein providing a predictive mode of complex frequencies associated with line-perturbation acoustic signals comprises determining eigenfunctions corresponding to characteristic line-perturbations of flow through the artery.

14. A method according to claim 13, wherein the determining step comprises identifying frequencies and associate lifetimes from the frequency grid and identifying eigenvalues corresponding to the eigenfunctions based on the identified frequencies and associated frequency lifetimes.

15. A method according to claim 14, further comprising characterizing the condition of the artery based on the eigenvalues.

16. A method according to claim 14, wherein identifying eigenvalues corresponding to the eigenfunctions comprises determining a pattern of resonance frequencies.

17. A method according to claim 16, wherein determining a pattern of resonance frequencies comprises determining ratios of the resonance frequencies.

18. A method according to claim 17, wherein determining a pattern of resonance frequencies for uniform flow U₀ in a blood vessel of width h, for blood having a viscosity ν

₀ for a simple boundary perturbation with peak curvature κ

, comprises identifying a family of complex frequencies having the formula;

f_n˜

λ

₀ν

₀ exp[iβ

_n]

where the frequency radius is;

λ

₀=(U₀/2ν

₀)²exp[−

2(h κ

)/3]

and the angles are;

β

_n=2 arccos[(ν

₀(hκ

)^1/2/U₀h) (2n+1)]

for n=0,1,2, . . . M.

19. A method according to claim 1, wherein the determining step comprises identifying an occlusion in a blood vessel.

20. A method according to claim 19, further comprising identifying a location of the occlusion.

21. A method according to claim 19, further comprising identifying a size of the occlusion.

22. A method according to claim 19, further comprising identifying a rigidity of the occlusion.

23. A method according to claim 1, wherein the determining step comprises identifying an aneurism in the blood vessel.

24. A method according to claim 1, further comprising providing a driving acoustic signal to the blood vessel.

25. A method according to claim 24, further comprising detecting changes between the detected acoustic signal and the driving acoustic signal.

26. A non-invasive system for detecting arterial disease in vivo, comprising:

one or more acoustic signal sensors configured for positioning on an external body region proximate at least one artery for obtaining acoustic signals;

a complex frequency data point generator module configured to generate a complex frequency grid of frequencies and associated lifetimes using the obtained acoustic signals;

a peak-perturbation module in communication with the complex frequency data point generator comprising a predictive model of complex frequencies associated with peak-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with early stage arterial disease;

a line-perturbation module in communication with the complex frequency data point generator comprising a predictive model of complex frequencies associated with line-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with later stage arterial disease and/or acoustic signals attributed to junction perturbations where arteries split in vivo; and

the peak-perturbation module and the line-perturbation module being configured for determining whether peak and/or line-perturbation acoustic signals of the predictive models are present to detect whether the subject has arterial disease.

27. A computer program product for detecting arterial disease in vivo, the computer program product comprising program code embodied in a computer-readable storage medium, the computer program code comprising:

computer program code that is configured to obtain acoustic signals from one or more sensors held on an external body region proximate at least one artery;

computer program code that is configured to generate a complex frequency grid of frequencies and associated lifetimes of the obtained acoustic signals;

computer program code that is configured to provide a predictive model of complex frequencies associated with peak-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with early stage arterial disease;

computer program code that is configured to provide a predictive model of complex frequencies associated with line-perturbation acoustic signals attributed to boundary perturbations in vivo that occur with later stage arterial disease an/or the thickening of arterial junctions; and

computer program code that is configured to determine whether peak and/or line-perturbation acoustic signals of the predictive models are present to detect whether the subject has arterial disease.