ZARZOSO , Vicente

The present report summarizes the research activities that I have carried out since completion of my PhD. My attention has focused on the fundamental signal processing problem of source signal estimation from the observation of corrupted measurements, in scenarios where the measured data can be considered as unknown linear transformations of...

The extraction of signals of interest from electrocardiogram (ECG) recordings corrupted by noise and artifacts accepts a blind source separation (BSS) model. The BSS approach aims to estimate a set of underlying source signals of physiological activity from the sole observation of unknown mixtures of the sources. The statistical independence...

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. Recently, a tensor decomposition approach has been put forward for noninvasive analysis of AF from surface electrocardiogram (ECG) records. Multilead ECG data are stored in tensor form and factorized via the block term decomposition (BTD)....

A second-order criterion for blind signal extraction in instantaneous linear mixtures has recently been proposed. It is proved that, with an adequate choice of autocorrelation time lags, the criterion leads indeed to a successful source extraction in the noiseless case. Using this criterion, the source identifiability conditions turn out to be...

The present chapter surveys computational algorithms for solving the independent component analysis (ICA) problem. Most of these algorithms rely on gradient or Newton iterations for contrast function maximization, and can work either in batch or adaptive processing mode. After briefly summarizing the common tools employed in their design and...

During atrial fibrillation (AF), atrial activity (AA) on the surface ECG consists of a pattern of quasi-periodic oscillations (f-waves), which are related to the electrical activation of the atrial substrate. However, to date no direct comparison between the extracted f-wave pattern in surface recordings and specific activation sites within the...

Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the independent components one after another. A novel method for deflationary ICA, referred to as RobustICA, is put...

The non-invasive analysis of atrial fibrillation (AF) relies on the extraction of atrial activity from surface ECG recordings. The present study compares three different methods for AA extraction from multi-lead ECG recordings: The adaptive singular value QRST cancellation, the spatio-temporal QRST cancellation and Independent Component...

The problems of signal separation and signal extraction arise in a wide variety of applications in biomedical engineering and other areas. Under the source statistical independence assumption, these problems can be solved by independent component analysis (ICA) methods. A simple ICA technique, referred to as RobustICA, has recently been...

The step size leading to the global minimum of the constant modulus (CM) criterion along the search direction can be obtained algebraically at each iteration among the roots of a third-degree polynomial. The resulting optimal step-size CMA (OS-CMA) is compared with other CM-based iterative techniques in terms of performance-versus-complexity trade-off.

This paper focuses on the constant power (CP) criterion for blind linear equalization of digital communication channels. This recently proposed criterion is specially designed for the extraction of q-ary phase shift keying (q-PSK) signals using finite impulse response equalizers. When zero-forcing equalizers exist, the CP cost function accepts...

The non invasive analysis of atrial fibrillation (AF) arrhythmia represents a challenge nowadays. The fibrillatory pattern of AF, known as f-wave, is partially masked by the ventricular activity of the heartbeat in the surface electrocardiogram (ECG). Classical techniques aiming to extract the f-wave are based on average beat subtraction (ABS)...

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered by physicians. The analysis of AF from the surface electrocardiogram (ECG) requires the suppression of artifacts such as ventricular activity (VA) and noise corrupting the recordings. Independent component analysis (ICA) has recently been shown to tackle...

A new method for the blind separation of sources is presented. The estimation algorithm only requires the computation of certain first-order order statistics of the whitened observed signals, so that the computational complexity of the resulting method is very low. Computer experiments and experiments with real data show the effectiveness of...

L1-norm criteria have been the subject a flurry of research in signal processing and machine learning over the last decade, especially due to their ability to exploit the sparsity of latent variables and their robustness in the presence of faulty data. Among such criteria, L1-norm principal component analysis (L1-PCA) has drawn considerable...

Principal component analysis (PCA) based on L1- norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening...

Principal component analysis (PCA) is an ubiquitous data compression and feature extraction technique in signal processing and machine learning. As compared with the classical L2-norm PCA, its L1-norm version offers increased robustness to outliers that are usually present in faulty data. Recently, L1- PCA was shown to perform source recovery...

Principal component analysis (PCA) and Fisher's linear discriminant analysis (LDA) are widespread techniques in data analysis and pattern recognition. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA, drawing considerable research interest on account of its increased robustness to outliers. The...

International audience

A contrast function for Independent Component Analysis (ICA) is presented incorporating the prior knowledge on the sub-Gaussian or super-Gaussian character of the sources as described by their kurtosis signs. The contrast is related to the maximum likelihood principle, reduces the permutation indeterminacy typical of ICA, and proves...

Atrial fibrillation dominant frequency (AFDF) has been demonstrated to provide useful information on the characteristics of atrial fibrillation. The present work points forward a new method for AFDF estimation directly from a single-lead ECG recording, exploiting the concept of compressed spectrum as spectral estimator. The main purpose is to...

This brief deals with the problem of blind source separation (BSS) via independent component analysis (ICA). We prove that a linear combination of the separator output fourth-order marginal cumulants (kurtoses) is a valid contrast function for ICA under prewhitening if the weights have the same sign as the source kurtoses. If, in addition, the...

This work presents a spatial filtering method for the estimation of atrial fibrillation activity in the cutaneous electrocardiogram. A linear extraction filter is obtained by maximizing the extractor output power on the significant spectral support of the signal of interest. An iterative procedure based on a quasi-maximum likelihood estimator...

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF...