Abstract

Based on the wavelet packet, a method for extracting the sub-band energy is developed to extract pathological features of heart sound signal. The db6 wavelet and sym7 wavelet are taken as the mother functions and the best wavelet packet basis of heart sound signal is picked out. Then, seven kinds of heart sound signals are decomposed into five levels and the wavelet packet coefficients of the best basis are obtained. According to the equal-value relation between wavelet packet coefficients and signal energy, the normalized sub-band energy of the best basis is extracted as the feature vector. Then, seven recognition models are trained separately based on BP network. These models are tested by using 70 heart sounds and the mean of recognition accuracy is 77.14%.

Keywords:

Feature extraction, heart sound, recognition model, wavelet packet,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.