Decorrelation of Lung and Heart Sound

Abstract

Abstract— Signal separation is very useful where several signals have been mixed together to form combined signal and our objective is to recover individual original component signals from that combined signal. One of the major problem in neural network and research in other disciplines is finding a suitable representation of multivariate data, i.e. random vectors. For concept and computational simplicity representation is in terms of linear transformation of the original data. This means that each component of the representation is a linear combination of the original variables. There are linear transformation methods such as principal component analysis and Independent Component Analysis (ICA). ICA is a recently developed method in which the goal is to find a linear representation of non-gaussian data so that the components are statistically independent or as independent as possible.
DOI: 10.17762/ijritcc2321-8169.1506157