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Heart sound is compound sound of mechanical vibration, and it contains different parts of the heart (such as the atrium and ventricle, blood vessels and heart valve) functional status of a large number of physiological and pathological information, which directly reflects the mechanical movement status of large blood vessels and heart system and closely related to human pathology. There is a very long history for its application in auscultation and adjuvant therapy. Heart sound signal is one kind of internal signal, one of the most important physiological signals. It is a kind of important means to do research on various physiological signals for the disease diagnosis and treatment.
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Keywordsīiophysics, Heart sound, Symbol entropy, Probability distribution. Simulation results show that the algorithm not only has significant feasibility and effectiveness but also provides a new way for the rapid diagnosis of heart failure. Thus the algorithm can make little or no impact of the non-stationary mutation interference and the sequence probability distribution on the entropy. Then the symbol entropy becomes more sensitive to the changes of the heart sound signal and could capture rapidly the nonlinear abnormal state of heart signal. On the one hand, it distributes more symbols for the region where the amplitude distribution of the first heart is dense and distributes relatively less symbols for the sparse region, so as to achieve the reduction of redundancy of data On the other hand, it use an self-adaptive method to determine the size of the symbol set. The algorithm makes a breakthrough at linear constraints. According to the characteristics of heart sound, the symbol entropy based on probability distribution is proposed. Heart sound is an important physiological signal, and it contains a large number of physiological and pathological information.