Nonlinear analysis of heart murmurs using wavelet-based higher-order spectral parameters

Area of Science:

• Cardiology
• Signal Processing
• Nonlinear Dynamics

Background:

• Heart sounds contain complex frequency information that changes over time.
• Nonlinear characteristics of heart sounds, particularly murmurs, are not fully understood.
• Non-stationarity and non-Gaussian properties of heart sounds require advanced analysis techniques.

Purpose of the Study:

• To reveal and analyze the nonlinear characteristics of heart sounds over time.
• To investigate the evolution of quadrature phase coupling in heart sound frequencies.
• To quantify the temporal changes in heart murmur nonlinearities.

Main Methods:

• Combined continuous wavelet transform (CWT) with third-order statistics/spectra.
• Analyzed non-Gaussian character and non-stationarity of heart sounds.
• Utilized a heart sound database from patients with various pathologies causing murmurs.
• Performed time-bi-frequency domain analysis.

Main Results:

• The combined approach successfully revealed nonlinear characteristics of heart sounds.
• Quadrature phase coupling evolution was analyzed in the time-bi-frequency domain.
• The method demonstrated efficient performance in quantifying murmur nonlinearities over time.

Conclusions:

• The combinatory approach of CWT and third-order statistics is effective for analyzing nonlinear heart sound dynamics.
• This method allows for the quantification of evolving nonlinearities in heart murmurs.
• Understanding these nonlinearities can aid in diagnosing and characterizing cardiac pathologies.