Blind signal separation for heart sound and lung sound from auscultatory sound based on the high order statistics

The auscultatory sounds are mixed heart sound, lung sound and other noises. If an auscultatory sound can be separated to a heart sound and a lung sound, it is possible that it may be useful for automatic diagnosis disease of the heart and lungs. However, a conventional method using the blind signal separation based on an ICA has lower separation precision. In the conventional method, a nonlinear function based on the separated signals is used in the separation algorithm. It is supposed to be an appropriate function in terms of separation precision corresponding to the probability distribution of source signals. However, because the conventional method has not been given an appropriate nonlinear function, it is a cause of lower separation precision. In this paper, we estimate a probability distribution of the source signals from the high order statistics of the separated signals and propose a better method which uses an appropriate nonlinear function in term of separation precision. Moreover, we evaluate a proposed algorithm to improve separation precision.