Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Validity and reliability of the Portuguese version of the Epilepsy Medication Treatment Complexity Index for Brazil

We evaluated the reliability and validity of a Brazilian-Portuguese version of the Epilepsy Medication Treatment Complexity Index (EMTCI). Interrater reliability was evaluated with the intraclass correlation coefficient (ICC), and validity was evaluated by correlation of mean EMTCI scores with the following variables: number of antiepileptic drugs (AEDs), seizure control, patients` perception of seizure control, and adherence to the therapeutic regimen as measured with the Morisky scale. We studied patients with epilepsy followed in a tertiary university-based hospital outpatient clinic setting, aged 18 years or older, independent in daily living activities, and without cognitive impairment or active psychiatric disease. ICCs ranged from 0.721 to 0.999. Mean EMTCI scores were significantly correlated with the variables assessed. Higher EMTCI scores were associated with an increasing number of AEDs, uncontrolled seizures, patients` perception of lack of seizure control, and poorer adherence to the therapeutic regimen. The results indicate that the Brazilian-Portuguese EMTCI is reliable and valid to be applied clinically in the country. The Brazilian-Portuguese EMTCI version may be a useful tool in developing strategies to minimize treatment complexity, possibly improving seizure control and quality of life in people with epilepsy in our milieu. (C) 2011 Elsevier Inc. All rights reserved.

GENERALIZED FINITE ELEMENT METHOD FOR NONLINEAR THREE-DIMENSIONAL ANALYSIS OF SOLIDS

The Generalized Finite Element Method (GFEM) is employed in this paper for the numerical analysis of three-dimensional solids tinder nonlinear behavior. A brief summary of the GFEM as well as a description of the formulation of the hexahedral element based oil the proposed enrichment strategy are initially presented. Next, in order to introduce the nonlinear analysis of solids, two constitutive models are briefly reviewed: Lemaitre`s model, in which damage and plasticity are coupled, and Mazars`s damage model suitable for concrete tinder increased loading. Both models are employed in the framework of a nonlocal approach to ensure solution objectivity. In the numerical analyses carried out, a selective enrichment of approximation at regions of concern in the domain (mainly those with high strain and damage gradients) is exploited. Such a possibility makes the three-dimensional analysis less expensive and practicable since re-meshing resources, characteristic of h-adaptivity, can be minimized. Moreover, a combination of three-dimensional analysis and the selective enrichment presents a valuable good tool for a better description of both damage and plastic strain scatterings.

Residence time distribution in holding tubes using generalized convection model and numerical convolution for non-ideal tracer detection

A procedure is proposed for the determination of the residence time distribution (RTD) of curved tubes taking into account the non-ideal detection of the tracer. The procedure was applied to two holding tubes used for milk pasteurization in laboratory scale. Experimental data was obtained using an ionic tracer. The signal distortion caused by the detection system was considerable because of the short residence time. Four RTD models, namely axial dispersion, extended tanks in series, generalized convection and PER + CSTR association, were adjusted after convolution with the E-curve of the detection system. The generalized convection model provided the best fit because it could better represent the tail on the tracer concentration curve that is Caused by the laminar velocity profile and the recirculation regions. Adjusted model parameters were well cot-related with the now rate. (C) 2010 Elsevier Ltd. All rights reserved.

Generalized Coupled Algebraic Riccati Equations for Discrete-time Markov Jump with Multiplicative Noise Systems

In this paper we consider the existence of the maximal and mean square stabilizing solutions for a set of generalized coupled algebraic Riccati equations (GCARE for short) associated to the infinite-horizon stochastic optimal control problem of discrete-time Markov jump with multiplicative noise linear systems. The weighting matrices of the state and control for the quadratic part are allowed to be indefinite. We present a sufficient condition, based only on some positive semi-definite and kernel restrictions on some matrices, under which there exists the maximal solution and a necessary and sufficient condition under which there exists the mean square stabilizing solution fir the GCARE. We also present a solution for the discounted and long run average cost problems when the performance criterion is assumed be composed by a linear combination of an indefinite quadratic part and a linear part in the state and control variables. The paper is concluded with a numerical example for pension fund with regime switching.

A generalized multi-period mean-variance portfolio optimization with Markov switching parameters

In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

The generalized inverse Weibull distribution

The inverse Weibull distribution has the ability to model failure rates which are quite common in reliability and biological studies. A three-parameter generalized inverse Weibull distribution with decreasing and unimodal failure rate is introduced and studied. We provide a comprehensive treatment of the mathematical properties of the new distribution including expressions for the moment generating function and the rth generalized moment. The mixture model of two generalized inverse Weibull distributions is investigated. The identifiability property of the mixture model is demonstrated. For the first time, we propose a location-scale regression model based on the log-generalized inverse Weibull distribution for modeling lifetime data. In addition, we develop some diagnostic tools for sensitivity analysis. Two applications of real data are given to illustrate the potentiality of the proposed regression model.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

A generalized modified Weibull distribution for lifetime modeling

A four parameter generalization of the Weibull distribution capable of modeling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone as well as non-monotone failure rates, which are quite common in lifetime problems and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh and modified Weibull distributions, among others. We derive two infinite sum representations for its moments. The density of the order statistics is obtained. The method of maximum likelihood is used for estimating the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the proposed distribution. (C) 2008 Elsevier B.V. All rights reserved.

The exponentiated generalized gamma distribution with application to lifetime data

A four-parameter extension of the generalized gamma distribution capable of modelling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone and non-monotone failure rate functions, which are quite common in lifetime data analysis and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the exponentiated Weibull, exponentiated generalized half-normal, exponentiated gamma and generalized Rayleigh, among others. We derive two infinite sum representations for its moments. We calculate the density of the order statistics and two expansions for their moments. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is obtained. Finally, a real data set from the medical area is analysed.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Prevalence of Epilepsy in Children From a Brazilian Area of High Deprivation

This study assessed the prevalence rate of epilepsy and its causes in children and adolescents in one area of high deprivation in Sao Paulo, Sao Paulo, in Southeast Brazil. Between July 2005 and June 2006, 4947 families from a population of 22,013 inhabitants (including 10,405 children and adolescents between the ages of 0 and 16 years) living in the shantytown of Paraisopolis, were interviewed. In the first phase, a validated questionnaire was administered, to identify the occurrence of seizures. In the second phase, clinical history, neurologic examination, electroencephalography, and structural neuroimaging were performed. The diagnosis of epilepsy, including etiology, seizure types, and epileptic syndrome classification, was according to criteria of the International League Against Epilepsy. The screening phase identified 353 presumptive cases. In the second phase, 101 of these cases (33.8%) received the diagnosis of epilepsy. Crude prevalence of epilepsy was 9.7/1000 and prevalence of active epilepsy was 8.7/1000. Partial seizures were the most frequent seizure type (62/101). Symptomatic focal epilepsy was the most common form, and hypoxic-ischemic encephalopathy the most common etiology, reflecting the socioeconomic conditions of this specific population. Adequate public policies regarding perinatal assistance could help reduce the prevalence of epilepsy. (C) 2010 by Elsevier Inc. All rights reserved.

Two distinct regions in 2q24.2-q24.3 associated with idiopathic epilepsy

P>Approximately 50% of all carriers of 2q21-q31 deletions present epileptic seizures. The band 2q24 constitutes the smallest commonly deleted segment in these patients, and contains the voltage-gated sodium channel genes SCN1A and SCN2A, associated with Dravet syndrome and benign familial neonatal-infantile seizures, respectively. A further putative locus involving epilepsy in the region was previously identified through disruption of the SLC4A10 gene by translocation. In the course of performing high-resolution DNA copy number analyses on syndromic mentally impaired individuals, we encountered three patients with overlapping deletions in chromosome region 2q24. Two of these patients exhibited epileptic seizures in addition to mental deficiency. The deletion in one of the epileptic patients did not include the SCN cluster, demonstrating that a less severe form of epilepsy maps to an adjacent genomic region. This second region comprises about 3 Mb and contains the candidate gene SLC4A10, providing further support for the potential role of this gene in epilepsy.

Novel mutations of the BSCL2 and AGPAT2 genes in 10 families with Berardinelli-Seip congenital generalized lipodystrophy syndrome

P>Context Congenital generalized lipodystrophy, or Berardinelli-Seip syndrome, is a rare autosomal recessive disease caused by mutations in either the BSCL2 or AGPAT2 genes. This syndrome is characterized by an almost complete loss of adipose tissue usually diagnosed at birth or early infancy resulting in apparent muscle hypertrophy. Common clinical features are acanthosis nigricans, hepatomegaly with or without splenomegaly and high stature. Acromegaloid features, cardiomyopathy and mental retardation can also be present. Design We investigated 11 kindreds from different geographical areas of Brazil (northeast and southeast). All coding regions as well as flanking intronic regions of both genes were examined. Polymerase chain reaction (PCR) amplifications were performed using primers described previously and PCR products were sequenced directly. Results Four AGPAT2 and two BSCL2 families harboured the same set of mutations. BSCL2 gene mutations were found in the homozygous form in four kindreds (c.412C > T c.464T > C, c.518-519insA, IVS5-2A > G), and in two kindreds compound mutations were found (c.1363C > T, c.424A > G). In the other four families, one mutation of the AGPAT2 gene was found (IVS3-1G > C and c.299G > A). Conclusions We have demonstrated four novel mutations of the BSCL2 and AGPAT2 genes responsible for Berardinelli-Seip syndrome and Brunzell syndrome (AGPAT2-related syndrome).