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This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity pattern of the inverse of spectral density matrix. When applied to a given time series, the algorithm produces a set of candidate models. Various information theoretic (IT) criteria are employed for deciding the winner. A novel IT criterion, which is tailored to our model selection problem, is introduced. Some options for reducing the computational burden are proposed and tested via numerical examples. We conduct an empirical study in which the algorithm is compared with the state-of-the-art. The results are good, and the major advantage is that the subjective choices made by the user are less important than in the case of other methods. [ABSTRACT FROM AUTHOR]

The newest approach to composite hypothesis testing proposed by Rissanen relies on the concept of optimally distinguishable distributions (ODD). The method is promising, but so far it has only been applied to a few simple examples. We derive the ODD detector for the classical linear model. In this framework, we provide answers to the following problems that have not been previously investigated in the literature: i) the relationship between ODD and the widely used Generalized Likelihood Ratio Test (GLRT); ii) the connection between ODD and the information theoretic criteria applied in model selection. We point out the strengths and the weaknesses of the ODD method in detecting subspace signals in broadband noise. Effects of the subspace interference are also evaluated. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests. [ABSTRACT FROM AUTHOR]

The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R–R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR–ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3–4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms. [Copyright &y& Elsevier]