Key result
Novel custom heart sound wavelet improves denoising and classification accuracy over standard wavelets.
Why the study?
Improving wavelet analysis performance for heart sound signals requires a wavelet basis specifically designed for these signals.
A newly constructed biorthogonal wavelet specifically designed for heart sound signals improves denoising and feature extraction compared to standard wavelets.
May enhance heart sound signal processing in research; leaves open clinical diagnostic impact pending validation.
In order to improve the performance of the wavelet analysis in the process of the heart sound signals, in this paper, we construct a wavelet basis which is exclusively used for processing the heart sound signals on the basis of wavelet theory construction. Firstly, we propose a general method of constructing a compactly supported biorthogonal wavelet which has even length filter banks, Secondly, according to the characteristics of heart sound signals, we discuss the structure principle of heart sound wavelet and a synthesis model of heart sound signals based on the heart sound wavelets. Finally, we construct the heart sound wavelet on the basis. In order to highlight the advanced nature and practical application of heart sound wavelet in processing heart sound signals, the theory and numerical simulation of heart sound wavelet are analyzed more comprehensively. Experimental results show that compared with commonly using the db and bior series wavelets, using the heart sound wavelet to process the heart sound signals can obtain good denoising effect, accurate classified information about heart sound and low reconstruction error rate. So the heart sound wavelet provides a new method of deep studying of heart sound feature extraction and identification and has a positive significance in describing the details of the individual characteristics of the heart sounds. The paper designs a method of the special wavelet which is based on the application object, which provides a new approach to the selection of wavelet basis in engineering applications.
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Zheng Zhang (2013) studied Heart sound signals. Heart sound wavelet vs. db and bior series wavelets was evaluated on Denoising effect, classification accuracy, and reconstruction error rate. Using the constructed heart sound wavelet to process heart sound signals provided better denoising, more accurate classification, and lower reconstruction error rates compared to standard wavelets.
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