Key result
Wavelet transform methods enable the extraction of time-frequency features to effectively differentiate between normal and abnormal heart sounds.
Wavelet transforms offer a time-frequency domain approach to analyze complex, non-stationary heart sounds for diagnostic purposes.
May support automated heart sound classification tools; leaves open prospective clinical validation before diagnostic adoption.
Heart sounds can be used more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and non-stationary that they are very difficult to analyse in time or frequency domains. We have studied the extraction of features from heart sounds in the time-frequency domain for recognition of heart sounds through time-frequency analysis. The application of wavelet transform for the heart sounds is thus described. The performance of continuous wavelet transform, discrete wavelet transform and packet wavelet transform is discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify clinical usefulness of our extraction methods for recognition of heart sounds.
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Debbal et al. (2009) studied Normal and abnormal heart sounds. Wavelet transform methods (continuous, discrete, and packet) was evaluated on Feature extraction and recognition of heart sounds. Wavelet transform methods enable the extraction of time-frequency features to effectively differentiate between normal and abnormal heart sounds.
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