Contribuições para a análise de sinais neuronais e biomédicos

Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering

Uma ferramenta para Análise Multiresolução de dados não regularmente amostrados

Digital signal processing (DSP) aims to extract specific information from digital signals. Digital signals are, by definition, physical quantities represented by a sequence of discrete values and from these sequences it is possible to extract and analyze the desired information. The unevenly sampled data can not be properly analyzed using standard techniques of digital signal processing. This work aimed to adapt a technique of DSP, the multiresolution analysis, to analyze unevenly smapled data, to aid the studies in the CoRoT laboratory at UFRN. The process is based on re-indexing the wavelet transform to handle unevenly sampled data properly. The was efective presenting satisfactory results

Projeto e Implementação em Ambiente Foundation Fieldbus de Filtragem Estocástica Baseada em Análise de Componentes Independentes (M175)

This work considers the development of a filtering system composed of an intelligent algorithm, that separates information and noise coming from sensors interconnected by Foundation Fieldbus (FF) network. The algorithm implementation will be made through FF standard function blocks, with on-line training through OPC (OLE for Process Control), and embedded technology in a DSP (Digital Signal Processor) that interacts with the fieldbus devices. The technique ICA (Independent Component Analysis), that explores the possibility of separating mixed signals based on the fact that they are statistically independent, was chosen to this Blind Source Separation (BSS) process. The algorithm and its implementations will be Presented, as well as the results

Comparison of different analysis techniques for the determination of muscle onset in individuals with patellofemoral pain syndrome

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise da fadiga muscular localizada em atletas e sedentários através de parâmetros de freqüência do sinal eletromiográfico

Embora a análise no domínio da freqüência do sinal eletromiográfico (EMG) seja empregada na caracterização do processo de fadiga muscular localizada, sua aplicação, especificamente a da freqüência mediana (Fmed), é pouco explorada no âmbito esportivo. O objetivo do presente estudo foi verificar a viabilidade da aplicação do sinal EMG, através de sua análise no domínio da freqüência, como parâmetro para determinação e diferenciação no comportamento da fadiga muscular localizada. Dois grupos de sujeitos, um caracterizado como atletas (n =12) e outro como sedentários (n =12), foram submetidos a análises baseadas em procedimentos executados em três diferentes situações experimentais, todos envolvendo a modalidade de exercício isométrico: i) teste máximo para determinação da contração isométrica voluntária máxima (CIVM); ii) teste de fadiga, sustentado por 35 seg. a 80% da CIVM; iii) teste de recuperação, sustentado por 10 seg. a 80% da CIVM; neste ultimo foi monitorado o comportamento da Fmed nos três primeiros (Fmedi) e três últimos segundos (Fmedf) do sinal EMG no músculo tibial anterior durante o teste de fadiga. Durante os 10 segundos do teste de recuperação foi calculada a Fmed referente a todo o período (Fmedr). parâmetro utilizado no cálculo do índice de recuperação muscular (IRM). Os resultados apontam que a Fmedf apresentou valor menor em relação à Fmedi em ambos os grupos (p < 0,05). Quando comparado com o grupo de sedentários, o grupo de atletas apresentou valores maiores de Fmedi e Fmedf (p < 0,05). O valor médio e desvio-padrão do IRM para o grupo de atletas foram de 62,1% ± 28,7 e, para o grupo de sedentários, de 55,2% ± 27,8 (p > 0,05). Dessa forma, os resultados apresentados neste estudo permitem inferir a viabilidade na aplicação de parâmetros no domínio da freqüência do sinal EMG para a determinação e diferenciação do comportamento da fadiga muscular localizada.

Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains

The aim of the present study was to compare heart rate variability (HRV) at rest and during exercise using a temporal series obtained with the Polar S810i monitor and a signal from a LYNX® signal conditioner (BIO EMG 1000 model) with a channel configured for the acquisition of ECG signals. Fifteen healthy subjects aged 20.9 ± 1.4 years were analyzed. The subjects remained at rest for 20 min and performed exercise for another 20 min with the workload selected to achieve 60% of submaximal heart rate. RR series were obtained for each individual with a Polar S810i instrument and with an ECG analyzed with a biological signal conditioner. The HRV indices (rMSSD, pNN50, LFnu, HFnu, and LF/HF) were calculated after signal processing and analysis. The unpaired Student t-test and intraclass correlation coefficient were used for data analysis. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV at rest and during exercise. The intraclass correlation coefficient demonstrated satisfactory correlation between the values obtained by the devices at rest (pNN50 = 0.994; rMSSD = 0.995; LFnu = 0.978; HFnu = 0.978; LF/HF = 0.982) and during exercise (pNN50 = 0.869; rMSSD = 0.929; LFnu = 0.973; HFnu = 0.973; LF/HF = 0.942). The calculation of HRV values by means of temporal series obtained from the Polar S810i instrument appears to be as reliable as those obtained by processing the ECG signal captured with a signal conditioner.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A New Approach for Real Time Fault Diagnosis in Induction Motors Based on Vibration Measurement

Condition monitoring is used to increase machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient real time vibration measurement and analysis instruments is capable of providing warning and predicting faults at early stages. In this paper, a new methodology for the implementation of vibration measurement and analysis instruments in real time based on circuit architecture mapped from a MATLAB/Simulink model is presented. In this study, signal processing applications such as FIR filters and fast Fourier transform are treated as systems, which are implemented in hardware using a system generator toolbox, which translates a Simulink model in a hardware description language - HDL for FPGA implementations.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Low voltage four-quadrant current multiplier: an improved topology for n-well CMOS process

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Scintigraphic validation of AC Biosusceptometry to study the gastric motor activity and the intragastric distribution of food in humans

Abnormal intragastric distribution of food (IDF) and a phasic contractility in the proximal stomach have been related to dyspeptic symptoms. Thus, the behaviour of the stomach and the proximal region, in particular, continues to attract attention and demand for reliable and comfortable techniques. The aims of this study were to employ AC Biosus-ceptometry (ACB) and scintigraphy to evaluate IDF and gastric motor activity in humans. Fifteen healthy volunteers ingested 60 mL of yogurt containing 2 mCi of Tc-99m and 4 g of ferrite. Each volunteer had gastric motility and IDF evaluated twice on separate days; on one occasion by ACB and another by scintigraphy. Digital signal processing was performed in MatLab (Mathworks Inc., Natick, MA, USA). Results were expressed as mean +/- SD. Similar results of distal accumulation time (P < 0.001) were obtained for scintigraphy (6.93 +/- 3.25 min) and for ACB (7.04 +/- 3.65 min). Fast Fourier Transform revealed two dominant frequencies (P > 0.9). Besides the well-know frequency of 3 cpm, our results showed identical frequencies in proximal stomach recordings (P < 0.001) for scintigraphic (1.01 +/- 0.01 cpm) and ACB (0.98 +/- 0.06 cpm). In summary, our data showed that scintigraphy and ACB are promising techniques to evaluate several aspects of gastric motility. Moreover, ACB is non-invasive, radiation-free and deserves the same importance as conventional methods for this kind of analysis.

Sistema de aquisição de dados para estudos de transiente de pressão e detecção de vazamentos em oleodutos

In February 2011, the National Agency of Petroleum, Natural Gas and Biofuels (ANP) has published a new Technical Rules for Handling Land Pipeline Petroleum and Natural Gas Derivatives (RTDT). Among other things, the RTDT made compulsory the use of monitoring systems and leak detection in all onshore pipelines in the country. This document provides a study on the method for detection of transient pressure. The study was conducted on a industrial duct 16" diameter and 9.8 km long. The pipeline is fully pressurized and carries a multiphase mixture of crude oil, water and natural gas. For the study, was built an infrastructure for data acquisition and validation of detection algorithms. The system was designed with SCADA architecture. Piezoresistive sensors were installed at the ends of the duct and Digital Signal Processors (DSPs) were used for sampling, storage and processing of data. The study was based on simulations of leaks through valves and search for patterns that characterize the occurrence of such phenomena

Algoritmos e Arquiteturas VLSI para Detectores MIMO com Decisão Suave

The use of Multiple Input Multiple Output (MIMO) systems has permitted the recent evolution of wireless communication standards. The Spatial Multiplexing MIMO technique, in particular, provides a linear gain at the transmission capacity with the minimum between the numbers of transmit and receive antennas. To obtain a near capacity performance in SM-MIMO systems a soft decision Maximum A Posteriori Probability MIMO detector is necessary. However, such detector is too complex for practical solutions. Hence, the goal of a MIMO detector algorithm aimed for implementation is to get a good approximation of the ideal detector while keeping an acceptable complexity. Moreover, the algorithm needs to be mapped to a VLSI architecture with small area and high data rate. Since Spatial Multiplexing is a recent technique, it is argued that there is still much room for development of related algorithms and architectures. Therefore, this thesis focused on the study of sub optimum algorithms and VLSI architectures for broadband MIMO detector with soft decision. As a result, novel algorithms have been developed starting from proposals of optimizations for already established algorithms. Based on these results, new MIMO detector architectures with configurable modulation and competitive area, performance and data rate parameters are here proposed. The developed algorithms have been extensively simulated and the architectures were synthesized so that the results can serve as a reference for other works in the area