Rubén Martín-Clemente

Fast Technique for Noninvasive Fetal ECG Extraction

This letter describes a fast and very simple algorithm for estimating the fetal electrocardiogram (FECG). It is based on independent component analysis, but we substitute its computationally demanding calculations for a much simpler procedure. The resulting method consists of two steps: 1) a dimensionality reduction step and 2) a computationally light postprocessing stage used to enhance the FECG signal.

Blind source separation in post-nonlinear mixtures using competitive learning, Simulated annealing, and a genetic algorithm

This paper presents a new adaptive procedure for the linear and nonlinear separation of signals with nonuniform, symmetrical probability distributions, based on both simulated annealing and competitive learning methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple linearization in the mixture space. Moreover, the paper proposes the fusion of two important paradigms-genetic algorithms and the blind separation of sources in nonlinear mixtures. In nonlinear mixtures, optimization of the system parameters and, especially, the search for invertible functions is very difficult due to the existence of many local minima. The main characteristics of the methods are their simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

The Maternal Abdominal ECG as Input to MICA in the Fetal ECG Extraction Problem

This letter presents a successful system for recovering the fetal electrocardiogram using multidimensional ICA (MICA). MICA requires as many observations as sources. To increase the number of observations, MICA is often applied to data sets that include measurements taken at the mothers thoracic region. However, experiments suggest that the propagation from the maternal heart to the mothers abdomen is not a simple delay, and that approach may fail. Alternatively, our method first estimates the maternal ECG directly from the mothers abdomen. Then, inputs this estimated ECG to MICA. Experiments show superior performance as compared with the traditional approach.

Eigendecomposition of self-tuned cumulant-matrices for blind source separation

Existing algorithms for blind source separation are often based on the eigendecomposition of fourth-order cumulant matrices. However, when the cumulant matrices have close eigenvalues, their eigenvectors are very sensitive to errors in the estimation of the matrices.In this paper, we show how to produce a cumulant matrix that has a well-separated extremal eigenvalue. The corresponding eigenvector is thus well conditioned and can be used to develop robust algorithms for blind source extraction. Some numerical experiments are provided to illustrate the effectiveness of the proposed approach.

Application of Independent Component Analysis to Edge Detection and Watermarking

It has been shown that a typical Independent Component Analysis (ICA) of an image provides a good estimation for the responses of the neurons of the visual cortex and their patterns of excitation. In this paper we apply ICA to obtain a decomposition of the image into a set of basis or features. These particular features can be used to develop a new algorithm for detecting the edges of the image. The performance of the proposed approach is demonstrated experimentally.

A Conjugate Gradient Method and Simulated Annealing for Blind Separation of Sources

This paper presents a new procedure for Blind Separtion of non-Gaussian Sources. It is proven that the estimation of the separating system can be based on the cancellation of some second partial derivatives of the output cross-cumulants. The resulting method is based on a conjutate gradient algorithm on the Stiefel manifold. Simulated annealing is used to obtain a good initial value and improve the rate of convergence.

Independent component analysis based on first-order statistics

This communication puts forward a novel method for independent source extraction in instantaneous linear mixtures. The method is based on the conditional mean of the whitened observations and requires some prior knowledge of the positive support of the desired source. A theoretical performance analysis yields the closed-form expression of the asymptotic interference-to-signal ratio achieved by this technique. The analysis includes the effects of inaccuracies in the estimation of the positive support of the desired source in single-step and iterative implementations of the algorithm. Numerical experiments validate the fitness of the asymptotic approximations. As it is based on first-order statistics, the method is extremely cost-effective, which makes it an attractive alternative to second- and higher-order statistical techniques in power-limited scenarios.

Connections between ICA and sparse coding revisited

Recently, the application of Independent Component Analysis (ICA) to natural images has raised a great interest. Some outstanding features have been observed, like the sparse distribution of the independent components and the special appearance of the ICA bases (most of them look like edges). This paper provides a new insight on this behaviour, being supported by experimental results. In particular, a mathematical proof of the relation between ICA and sparse coding is given.

Applying Neural Networks and Genetic Algorithms to the Separation of Sources

This paper presents a new adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated annealing (SA) and competitive learning (CL) methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple linearization in the mixture space. Also, the paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). From experimental results, this paper demonstrates the possible benefits offered by GAs in combination with BSS, such as robustness against local minima, the parallel search for various solutions, and a high degree of flexibility in the evaluation function. The main characteristics of the method are its simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

New equations and iterative algorithm for blind separation of sources

This paper addresses the blind separation of non-gaussian sources for instantaneous mixtures by using higher-order statistics. It is proven that the estimation of the separating matrix can be achieved by solving linear equations repeatedly. The convergence of the algorithm is proven analytically. A study of the complexity of the algorithm is also presented.