New approach in features extraction for EEG signal detection

4 pages, 3 figures.-- Contributed to: "Engineering the Future of Biomedicine", EMBC2009, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Minneapolis, Minnesota, USA, Sep 2-6, 2009).This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.This work has been funded by the Spain CICYT grant TEC2008-02473.Publicad

Plant identification via adaptive combination of transversal filters

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.Publicad

Adaptively combined LMS and logistic equalizers

An adaptive, convex linear combination of the outputs of a standard least mean square (LMS) equalizer and a sigmoidal equalizer is proposed. This procedure results in improving the speed of the LMS equalizer while retaining the low steady-state error of the sigmoidal filter. Appropriate adaption schemes for both of the filters and for the combination parameters are established. Simulations of practical communication applications demonstrate the effectiveness of this adaptive combination.Publicad

Approximate Kernel Orthogonalization for Antenna Array Processing

We present a method for kernel antenna array processing using Gaussian kernels as basis functions. The method first identifies the data clusters by using a modified sparse greedy matrix approximation. Then, the algorithm performs model reduction in order to try to reduce the final size of the beamformer. The method is tested with simulations that include two arrays made of two and seven printed half wavelength thick dipoles, in scenarios with 4 and 5 users coming from different angles of arrival. The antenna parameters are simulated for all DOAs, and include the dipole radiation pattern and the mutual coupling effects of the array. The method is compared with other state-of-the-art nonlinear processing methods, to show that the presented algorithm has near optimal capabilities together with a low computational burden.Spanish Governnment under Grant TEC2008-02473IEEE Antennas and Propagation SocietyPublicad

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

10 pages, 6 figures.-- PMID: 20217264.This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time–frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.This work has been funded by the Spain CICYT grant TEC2008-02473.Publicad

Support vector machines framework for linear signal processing

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.Publicad

Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad

Implications of global pricing policies on access to innovative drugs: : the case of trastuzumab in seven Latin American countries

Background: Differential pricing, based on countries’ purchasing power, is recommended by the WHO to secure affordable medicines. However, in developing countries innovative drugs often have similar or even higher prices than in high-income countries. We evaluated the potential implications of trastuzumab global pricing policies in terms of cost-effectiveness (CE), coverage and accessibility for patients with breast cancer in Latin America (LA). Methods: A Markov model was designed to estimate life years (LYs), quality-adjusted life years (QALYs) and costs from a health care perspective. To better fit local cancer prognosis, a base case scenario using transition probabilities from clinical trials was complemented with two alternative scenarios with transition probabilities adjusted to reflect breast cancer epidemiology in each country. Findings: Incremental discounted benefits ranged from 0.87 to 1.00 LY and 0.51 to 0.60 QALY and incremental CE ratios from USD 42,104 to USD 110,283 per QALY (2012 US dollars), equivalent to 3.6 gross domestic product per capita (GDPPC) per QALY in Uruguay and to 35.5 GDPPC in Bolivia. Probabilistic sensitivity analysis showed 0% probability that trastuzumab is CE if the willingness-to-pay (WTP) threshold is one GDPPC per QALY, and remained so at three GDPPC threshold except for Chile and Uruguay (4.3% and 26.6% respectively). Trastuzumab price would need to decrease between 69.6% to 94.9% to became CE in LA. Interpretation: Although CE in other settings, trastuzumab was not CE in LA. The use of health technology assessment to prioritize resource allocation and support price negotiations is critical to making innovative drugs available and affordable in developing countries

[Futbolistas asturianos VIII] [Material gráfico]

Contiene fotografías pertenecientes al archivo fotográfico del diario "Región", publicadas entre 1963 y 1983Algunas fotos no indican autoría; el resto firmadas por Foto E. Gar (Oviedo), Foto Sierra (Oviedo), Foto Arsenio (Trubia, Oviedo), Foto Segura (Oviedo), Reportajes Fotográficos Marcos (Avilés), Foto Vázquez (Olloniego), Foto Gutiérrez (Navia