A novel cardiac spectral segmentation based on a multi-Gaussian fitting method for regurgitation murmur identification

作者：Choi Samjin^*; Jung Gyeong Bok; Park Hun Kuk

来源：Signal Processing, 2014, 104: 339-345.

DOI：10.1016/j.sigpro.2014.04.024

• Cardiac spectral curve
• Multi-Gaussian decomposition
• Regurgitation murmur
• Wavelet decomposition
• Autoregressive PSD

A new cardiac spectral segmentation method was developed for discriminating between normal heart sound and heart valvular diseases. This approach was based on a multi-Gaussian fitting algorithm of cardiac spectral curve. The spectral autoregressive power spectral density (aPSD) curve was estimated from the cardiac sounds noise-cancelled by the wavelet decomposition. 5-GaPSD was approximated by a five-Gaussian model consisting of five Gaussian peaks, P1 to P5. The spectral profiles, the maximum frequency f(k), the amplitude H-k, the half-width W-k, the area portion S-k, and the loss of area, of five Gaussian peaks were investigated and compared for segmenting the spectral information of normal heart sound and two regurgitation murmurs.

• 出版日期2014-11