The Study Of Heart Murmur Grading Quantification And Cardiac Energy

Heart sound auscultation is an important way to check the integrity of cardiac function, and the patients with heart diseases can be probably diagnosed by means of the stethoscope. To determine a certain type of disease, one of the crucial indicators is heart murmur. Heart murmur is the most common signs in the heart examination of young people and infants, if the examiner can not make a correct clinical diagnosis and analysis, it will be caused a great stress to patients and their families. Heart murmurs can be graded as to intensity, duration, and quality. Customarily, intensity or loudness is graded from one to six, based upon the original recommendations of Freeman and Levine in 1933. The method is based on the physician experience to make direct, subjective judgments, and the results will be changed with the examiner situation. Meanwhile, physicians often use "blowing", "rumbling" and other terms to describe the characteristics of the heart murmur. Obviously, these terms provides only subjective reference, while it's hard to obtain accurate physiological and pathological characteristics from heart diseases.This paper first introduces the physiological basis of heart murmur (such as heart murmur generation mechanism, features, etc.) and the applications in the diagnosis of heart disease. Based on murmurs feature, a further signal preprocessing will be needed. Then characteristic parameters are extracted as the quantitative indicators of heart murmurs grading. Finally, cardiac energy was analysis through the energy distribution of different frequency components in heart sound and the different components of their energy fractions.The pretreatment includes wavelet threshold de-noising and welch power spectrum estimation. First, the paper introduces the characteristics of noises in the wavelet decomposition, as well as the methods and procedures of wavelet threshold de-noising. Combined with the nature of wavelet functions and heart sounds characteristics, we selected the suitable wavelet function based on the reconstructed factor. The result showed that coif3 wavelet had a good de-noising for heart sounds. Moreover, the spectrum of heart sound is obtained by using welch power spectrum estimation method. It can display frequency domain characteristics in different components and pay a basis role for heart sound segmentation and positioning. We used MALLAT algorithm to isolate murmurs from heart sounds with the good multi-resolution analysis features of wavelet transform. Heart sound envelope was extracted by Hilbert transform. Differential method was used to obtain the peak values and extra peaks were discarded according to threshold. Finally, we looked for the first heart sound, the second heart sound boundaries in the peak around 1000 samples and determined duration of the first heart sound, the second heart sound. The parameters of murmur, the first heart sound and the second heart sound in three consecutive cardiac cycles were extracted,such as the area enclosed with baseline, duration, maximum amplitude, etc. By calculating the mean ratio of murmur and the first heart sound, the second heart sound (for reference), we explored three quantitative methods (murmur area ratio, weighted intensity and duration ratio, murmur energy ratio) for heart murmur grading. The results of these methods were compared with traditional murmur grading, and it provided a combination of subjective and objective study for heart murmur grading.To understand the energy distribution of different frequency components in murmurs, this paper selected wavelet packet decomposition to analysis normal heart sounds and different types of heart murmur signals. Since the force pumping the blood is derived from cardiac contraction and changes in the amplitude of the first heart sound are closely related with the maximum rate of rise of left ventricular pressure, the energy of the first heart sound can reflect cardiac energy expenditure at a certain extent, corresponding to useful work. From the viewpoint of efficiency, the energy of cardiac murmur corresponds to useless work. The first heart sound energy fraction (S1EF) and murmur energy fraction (MEF) were calculated for three consecutive cardiac cycles, the cardiac energy state was assessed in different groups by energy efficiency. The results showed that the first heart sound energy fraction were significantly different in the normal group and the heart murmur group.