Exploiting nonlinearity of the speech production system for voice disorder assessment by recurrence quantification analysis.

This work summarizes the research related to digital speech signal processing with recurrence quantification analysis (RQA) applied to voice disorder assessment. The main motivation for these studies is the fact that RQA is able to exploit the nonlinear dynamical nature of the speech production system. Due to the use of recurrence quantification measures to represent the behavior of speech signals, promising results were obtained in the characterization and classification of laryngeal pathologies and voice disorders. These contributions may help one to evaluate the usability and efficiency of RQA in vocal disorder assessment.

Auditory Stimuli Coding by Postsynaptic Potential and Local Field Potential Features.

The relation between physical stimuli and neurophysiological responses, such as action potentials (spikes) and Local Field Potentials (LFP), has recently been experimented in order to explain how neurons encode auditory information. However, none of these experiments presented analyses with postsynaptic potentials (PSPs). In the present study, we have estimated information values between auditory stimuli and amplitudes/latencies of PSPs and LFPs in anesthetized rats in vivo. To obtain these values, a new method of information estimation was used. This method produced more accurate estimates than those obtained by using the traditional binning method; a fact that was corroborated by simulated data. The traditional binning method could not certainly impart such accuracy even when adjusted by quadratic extrapolation. We found that the information obtained from LFP amplitude variation was significantly greater than the information obtained from PSP amplitude variation. This confirms the fact that LFP reflects the action of many PSPs. Results have shown that the auditory cortex codes more information of stimuli frequency with slow oscillations in groups of neurons than it does with slow oscillations in neurons separately.

MRI segmentation using dialectical optimization.

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.