Germán Castellanos

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The follow-up of some cardiac diseases may be achieved by ECG-holter record analysis. A heartbeat clustering method can be used to reduce the usually high computational cost of such holter analysis. This study describes a method aimed at cardiac arrhythmia recognition based on this approach, by means of unsupervised inspection of morphologically similar(More)
Heuristical algorithms can reduce the computational complexity. Such methods require of some stopping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as principal component analysis (PCA) allow to find a lower dimension transformed(More)
Kernel Principal Component analysis is a nonlinear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden(More)
Adequate recognition of lips posture for speech articulation analysis requires of the measurement of several anthropometric mouth parameters. These are needed to estimate the position and contour of the lips and teeth and tongue positions, as well. Here, a method is proposed for lips contour detection under natural conditions without any extra hardware(More)
Here, an analysis of different acoustic features and their influence in automatic identification of hypernasality is shown. Effective feature selection method includes preprocessing of the initial feature space based on statistical independence analysis. Simultaneously, the synthesis of a specialized diagnostic feature is proposed based on analyzing the(More)
A system for feature extraction and recognition of lip postures is presented, by constructing a shape model of the inner and outer contours of the lips, whose parameters are controlled by a canonical genetic algorithm. Features of each posture are the model parameters which better fit to the analyzed image. A Bayesian classifier is used to classify, under(More)
Hidden Markov models have shown promising results for identification of spike sources in Parkinson's disease treatment, e.g., for deep brain stimulation. Usual classification criteria consist in maximum likelihood rule for the recognition of the correct class. In this paper, we present a different classification scheme based in proximity analysis. For this(More)
HMMs are statistical models used in a very successful and effective form in speech recognition. However, HMM is a general model to describe the dynamic of stochastic processes; therefore it can be applied to a huge variety of biomedical signals. Usually, the HMM parameters are estimated by means of MLE (Maximum Likelihood Estimation) criterion.(More)
IntroductIon In the treatment of children with fixed Cleft Lip and Palate (CLP), problems such as hipponasality and hypernasality, which are related to vocal emission and resonance, might appear. Nevertheless, according to the report presented in Castellanos et al. (2006), hy-pernasality is more frequently found than hipponasality (90% vs. 10%). The(More)