Application of Symbol Entropy based on Probability Distribution to Heart Sound Analysis
Heart sound is an important physiological signal, and it contains a large number of physiological and pathological information. According to the characteristics of heart sound, the symbol entropy based on probability distribution is proposed. The algorithm makes a breakthrough at linear constraints. 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. 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. Thus the algorithm can make little or no impact of the non-stationary mutation interference and the sequence probability distribution on the entropy. 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.
Xie-Feng C, Chen-Jun S, Yong MA, Ke-Xue S and Yu-qi J