Summary
Objectives
: Many pathological conditions of the cardiovascular system cause murmurs and aberrations
in heart sounds. Phonocardiography provides the clinician with a complementary tool
to record the heart sounds heard during auscultation. The advancement of intracardiac
phonocardiography combined with modern digital signal processing techniques has strongly
renewed researchers' interest in studying heart sounds and murmurs.
The aim of this work is to investigate the applicability of different spectral analysis
methods to heart sound signals and explore their suitability for PDA-based implementation.
Methods
: Fourier transform (FT), short-time Fourier transform (STFT) and wavelet transform
(WT) are used to perform spectral analysis on heart sounds. A segmentation algorithm
based on Shannon energy is used to differentiate between first and second heartsounds.
Then wavelet transform is deployed again to extract 64 features of heart sounds.
Results
: The FT provides valuable frequency information but the timing information is lost
during the transformation process. The STFT or spectrogram provides valuable time-frequency
information but there is a trade-off between time and frequency resolution. Waveletanalysis,
however, does not suffer from limitations of the STFT and provides adequate time and
frequency resolution to accurately characterize the normal and pathological heartsounds.
Conclusions
: The results show that the wavelet-based segmentation algorithm is quite effective
in localizing the important components of both normal and abnormal heart sounds. They
also demonstrate that wavelet-based feature extraction provides suitable feature vectors
which are clearly differentiable and useful for automatic classification of heart
sounds.
Keywords
Advanced biomedical signal processing - feature extraction - heart sound analysis
- intelligent phonocardiography - wavelet decomposition
