Standardization of the Electromyographic Signal Through the Maximum Isometric Voluntary Contraction

The objective of this study was to analyze the electromyographic (EMG) data, before and after normalization. One hundred (100) normal subjects (with no signs and symptoms of temporomandibular disorders) participated in this study. A surface EMG of the masticatory muscles was performed. Two different tests were performed: maximum voluntary clench (MVC) on cotton rolls and MVC in intercuspal position. The normalization was done using the mean value of the EMG signal of the first examination. The coefficient of variation CV showed lower values for the standardized data. The standardization was effective in reducing the differences between records from the same subject and in different subjects.

Masticatory muscle activity during maximum voluntary clench in different research diagnostic criteria for temporomandibular disorders (RDC/TMD) groups

The research diagnostic criteria for temporomandibular disorders (RDC/TMD) are used for the classification of patients with temporomandibular disorders (TMD). Surface electromyography of the right and left masseter and temporalis muscles was performed during Maximum teeth clenching in 103 TMD patients subdivided according to the RDC/TMD into 3 non-overlapping groups: (a) 25 myogenous; (b) 61 arthrogenous; and (c) 17 psycogenous patients. Thirty-two control subjects matched for sex and age were also measured. During clenching, standardized total muscle activities (electromyographic potentials over time) significantly differed: 131.7 mu V/mu V s % in the normal subjects, 117.6 mu V/mu V s % in the myogenous patients, 105.3 mu V/mu V s % in the arthrogenous patients, 88.7 mu V/mu V s % in the psycogenous patients (p < 0.001, analysis of covariance). Symmetry in the temporalis muscles was larger in normal subjects (86.3%) and in myogenous patients (84.9%) than in arthrogenous (82.7%), and psycogenous patients (80.5%) (p=0.041). No differences were found for masseter muscle symmetry and torque coefficient (p>0.05). Surface electromyography of the masticatory muscles allowed an objective discrimination among different RDC/TMD subgroups. This evaluation could assist conventional clinical assessments. (C) 2007 Elsevier Ltd. All rights reserved.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.

Algorithms for Maximum Independent Set in Convex Bipartite Graphs

A bipartite graph G = (V, W, E) is convex if there exists an ordering of the vertices of W such that, for each v. V, the neighbors of v are consecutive in W. We describe both a sequential and a BSP/CGM algorithm to find a maximum independent set in a convex bipartite graph. The sequential algorithm improves over the running time of the previously known algorithm and the BSP/CGM algorithm is a parallel version of the sequential one. The complexity of the algorithms does not depend on |W|.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A matrix formula for the skewness of maximum likelihood estimators

We give a general matrix formula for computing the second-order skewness of maximum likelihood estimators. The formula was firstly presented in a tensorial version by Bowman and Shenton (1998). Our matrix formulation has numerical advantages, since it requires only simple operations on matrices and vectors. We apply the second-order skewness formula to a normal model with a generalized parametrization and to an ARMA model. (c) 2010 Elsevier B.V. All rights reserved.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.