IntroductionIn coronary artery bypass (CABG) surgery, the common complications are the need for reintubation, prolonged mechanical ventilation (PMV) and death. Thus, a reliable model for the prognostic evaluation of those particular outcomes is a worthwhile pursuit. The existence of such a system would lead to better resource planning, cost reductions and an increased ability to guide preventive strategies. The aim of this study was to compare different methods – logistic regression (LR) and artificial neural networks (ANNs) – in accomplishing this goal.Material and methodsSubjects undergoing CABG (n = 1315) were divided into training (n = 1053) and validation (n = 262) groups. The set of independent variables consisted of age, gender, weight, height, body mass index, diabetes, creatinine level, cardiopulmonary bypass, presence of preserved ventricular function, moderate and severe ventricular dysfunction and total number of grafts. The PMV was also an input for the prediction of death. The ability of ANN to discriminate outcomes was assessed using receiver-operating characteristic (ROC) analysis and the results were compared using a multivariate LR.ResultsThe ROC curve areas for LR and ANN models, respectively, were: for reintubation 0.62 (CI: 0.50–0.75) and 0.65 (CI: 0.53–0.77); for PMV 0.67 (CI: 0.57–0.78) and 0.72 (CI: 0.64–0.81); and for death 0.86 (CI: 0.79–0.93) and 0.85 (CI: 0.80–0.91). No differences were observed between models.ConclusionsThe ANN has similar discriminating power in predicting reintubation, PMV and death outcomes. Thus, both models may be applicable as a predictor for these outcomes in subjects undergoing CABG.

In this paper, we describe the proposal of an open-source and open access website designed to share a set of web accessibility guidelines for people with Autism Spectrum Disorder (ASD) called GAIA, that intends to help web developers to design accessible web interfaces for these users. The guidelines were extracted from a revision process of 17 works published between 2005 and 2015 including international recommendations, commercial or academic software and peer-reviewed papers. We identified 107 guidelines that were grouped in 10 categories through affinity diagram technique. Then, we normalized the guidelines in each group according to similarities and duplicated statements, generating a set of 28 guidelines. As a result, we evidenced best practices to design accessible web interfaces for people with ASD based on well succeeded solutions presented in works of different contexts. With those results, we aim to contribute to the state of the art of cognitive web accessibility. Therefore, we made the set of guidelines available in a repository on GitHub, so it can be used both by researchers and technical professionals. Resumo.Neste artigo, descrevemos a proposta de um website de código e acesso abertos projetado para divulgar um conjunto de recomendações de acessibilidade web para pessoas com Transtorno do Espectro do Autismo (TEA) chamado GAIA, o qual tem a intenção de auxiliar desenvolvedores web a projetar interfaces web mais acessíveis a estes usuários. Estas recomendações foram extraídas através de um processo de revisão de 17 trabalhos publicados entre 2005 e 2015, incluindo recomendações internacionais, softwares comerciais ou acadêmicos e artigos revisados por pares. Identificamos 107 recomendações que foram agrupadas em 10 categorias através da técnica de diagrama de afinidades. Em seguida, normalizamos as recomendações em cada categoria de acordo com similaridades e declarações duplicadas, gerando um conjunto de 28 recomendações únicas. Como resultado, evidenciamos melhores práticas para projetar interfaces web acessíveis a pessoas com TEA baseado em soluções de sucesso presentes em trabalhos de diferentes contextos. Com estes resultados, esperamos contribuir com o estado da arte de acessibilidade web cognitiva. Dessa forma, disponibilizamos as recomendações em um repositório no GitHub, para que estes resultados possam ser utilizados tanto por pesquisadores quanto por profissionais técnicos.

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Several studies have found evidence for corticolimbic Theta electroencephalographic (EEG) oscillation in the neural processing of visual stimuli perceived as fear or threatening scene. Recent studies showed that neural oscillations' patterns in Theta, Alpha, Beta and Gamma sub-bands play a main role in brain's emotional processing. The main goal of this study is to classify two different emotional states by means of EEG data recorded through a single-electrode EEG headset. Nineteen young subjects participated in an EEG experiment while watching a video clip that evoked three emotional states: neutral, relaxation and scary. Following each video clip, participants were asked to report on their subjective affect by giving a score between 0 to 10. First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based denoising to remove artifacts. Afterward, the distribution of power in time-frequency space was obtained using short-time Fourier transform (STFT) and then, the mean value of energy was calculated for each EEG sub-band. Finally, 46 features, as the mean energy of frequency bands between 4 and 50 Hz, containing 689 instances - for each subject -were collected in order to classify the emotional states. Our experimental results show that EEG dynamics induced by horror and relaxing movies can be classified with average classification rate of 92% using support vector machine (SVM) classifier. We also compared the performance of SVM to K-nearest neighbors (K-NN). The results show that K-NN achieves a better classification rate by 94% accuracy. The findings of this work are expected to pave the way to a new horizon in neuroscience by proving the point that only single-channel EEG data carry enough information for emotion classification.