Acoustic source separation for the detection of coronary artery sounds

Area of Science:

• Biomedical Engineering
• Acoustics
• Cardiovascular Disease Diagnostics

Background:

• Coronary artery disease (CAD) is a leading cause of death in the US.
• Current diagnostic screening for CAD faces significant challenges.
• Phonoangiography aims to detect CAD using acoustic signatures of turbulent blood flow, but is limited by low signal-to-noise ratio.

Purpose of the Study:

• To demonstrate the computational feasibility of an acoustic source separation methodology for isolating coronary artery sounds.
• To develop a method for detecting CAD by separating coronary artery sounds from background chest noise.
• To assess the vulnerability of the proposed method to measurement errors.

Main Methods:

• Acoustic finite element analysis (FEA) was performed on physiologically accurate chest geometries.
• An original acoustic source separation methodology was developed based on pseudoinversion of mixing matrices.
• Mixing matrices were determined through a combination of experimental and computational approaches.
• Coronary artery sound emission was calculated from acoustic velocity measurements on the chest surface.

Main Results:

• The study computationally demonstrated the feasibility of the proposed acoustic source separation technique.
• The method allows for the calculation of sounds emitted by coronary arteries.
• The vulnerability of the approach to potential measurement errors was examined.

Conclusions:

• The developed acoustic source separation methodology is computationally feasible for isolating coronary artery sounds.
• This technique holds potential for improving the diagnostic capabilities of phonoangiography for CAD.
• Further investigation into real-world application and robustness against measurement errors is warranted.