White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

A framework for automatic heart sound analysis without segmentation

Abstract: A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.

Calculus on Gaussian and Poisson white noises

Abstract: Recently one of the authors has introduced the concept of generalized Poisson functionals and discussed the differentiation, renormalization, stochastic integrals etc ([8], [9]), analogously to the works of T Hida ([3], [4], [5]) Here we introduce a transformation for Poisson fnnctionals with the idea as in the case of Gaussian white noise (cf [10], [11], [12], [13]) Then we can discuss the differentiation, renormalization, multiple Wiener integrals etc in a way completely parallel with the Gaussian case The only one exceptional point, which is most significant, is that the multiplications are described by for the Gaussian case, for the Poisson case, as will be stated in Section 5 Conversely, those formulae characterize the types of white noises

Harmonics in multiplicative and additive noise: performance analysis of cyclic estimators

Abstract: Multiplicative noise causes smearing of spectral lines and thus hampers frequency estimation relying on conventional spectral analysis. In contrast, cyclic mean and correlation statistics have proved to be useful for harmonic retrieval in the presence of multiplicative and additive noise of arbitrary color and distribution. Performance analysis of cyclic estimators is carried through both for nonzero and zero mean multiplicative noises. Cyclic estimators are shown to be asymptotically equivalent to certain nonlinear least squares estimators, and are also compared with the maximum likelihood ones. Large sample variance expressions of the cyclic estimators are derived and compared with the corresponding Cramer-Rao bounds when the noises are white Gaussian. It is demonstrated that previously well established results on constant amplitude harmonics are special cases of the present analysis. Simulations not only validate the large sample performance analysis, but also provide concrete examples regarding relative statistical efficiency of the cyclic estimators. >

Restoration of Digital Multiplane Tomosynthesis by a Constrained Iteration Method

Abstract: Tomosynthetic reconstructions suffer from the disadvantage that blurred images of object detail lying outside the plane of interest are superimposed over the desired image of structures in the tomosynthetic plane. It is proposed to selectively reduce these undesired superimpositions by a constrained iterative restoration method, suitably generalized to permit simultaneous deconvolution of multiple planes. Sufficient conditions are derived ensuring the convergence of the iterations to the exact solution in the absence of noise and constraints. Although in practice the restoration process must be left incomplete because of inescapable noise and quantization artifacts, the experimental results demonstrate that for reasons of stability the convergence conditions derived for the noise-free, unconstrained case should be satisfied. In order to establish a basis for a formal stopping criterion of the iteration procedure, the buildup of noise in the sequence of iterative restorations arising from white noise in the original radiographs is investigated theoretically and experimentally. This results in the derivation of an approximation to the limiting noise variance in the reconstructions which is verified experimentally.