| Heart failure is a common heart disease, which is very serious. Almost all of theheart disease and macrovascular disease would be exacerbated to heart failure. Heartfailure has been a heavy burden to the health care and society. The transformation ofenergy plays an important role in generant mechanism of heart failure. At present, heartenergy is measured based on biochemistry and nuclear medicine. Heart sound is amechanical signal produced by heart pulsates. Heart sound reflects beats of heart. Soanalysis of the energy of heart sound could be used to evaluate heart energy state. Thispaper study on energy analysis methods for heart sound, and its application onevaluation of heart energy state of different heart diseases.Firstly, heart sound is preprocessed. The coif3wavelet transform is used forde-noising heart sound. Only part of detail coefficients are de-noised via threshold, sothe main energy of signal could retained. After extracting envelope of heart sound byusing HHT, S1and S2are located via threshold to lay the foundation for heart soundanalysis.Secondly, analysis methods for heart sound in frequency domain are studiedcorrespondingly. The theories of Fourier transform, Wigner-Ville distribution （WVD）and wavelet transform are introduced, and then heart sounds are processed by the threemethods to analyzing energy distribution in frequency domain. The results show thatFourier transform could obtain energy distribution of the whole frequency domainintuitively, but information of parameter is poor so that Fourier transform is limited inmore detailed analysis. Wigner-Ville distribution is intuitionistic and high-resolution.The energy distribution of heart sound in time and frequency domain can be obtainedfrom the WVD spectrum directly. But WVD is lack of quantified parameter in energyanalysis and depend on espial of researchers. The energy percent of the wavelet packetdecomposition of heart sound could analyze energy distribution quantitatively. Thepercent analysis could avoid error caused by heart sound acquisition availably, soresults are more accurate and believable. In conclusion, this paper analyzes heart soundenergy through analysis of the energy percent of wavelet packet decomposition of heartsound.Lastly, energy distribution of normal heart sound and mitral insufficiency, mitralstenosis, aortic insufficiency, aortic stenosis and ventricular septal defect are analyzed by energy percent (E1~E8) of wavelet packet decomposition and componential energyfraction(S1EF and MEF) of heart sound. The results of statistical analysis show thatdistribution of E1~E8is different in different heart sounds, and it could be used toidentify different heart sounds. Energy percent in high frequency are especially useful inheart sound analysis. Moreover, S1EF of normal heart sound is higher that S1EF ofpathological heart sound and the difference is significant (P<0.01). Also, MEF isdifferent in different heart sounds. Finally, energy percent and componential energyfraction are combined to evaluate heart energy state. For different heart disease, heartenergy efficiency for ejection and corresponding heart energy distribution are alldifferent. So this paper provides a new approach for assisted diagnosis of heart diseaseby heart energy analysis. |
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