Study of muscle fatigue in isokinetic exercise with estimated conduction velocity and traditional electromyographic indicators

INTRODUCTION: In the study of human biomechanics, it is often desirable to evaluate fatigue in the muscles that are involved in performing a particular task. Identifying the phenomena responsible for this condition is a problem that in most cases is complex and requires appropriate research mechanisms. Isokinetic dynamometry (ID) and surface electromyography (SEMG) are two techniques widely used in studies on strength and muscle fatigue. Their effectiveness is conditioned upon a good understanding of their limitations and the adoption of procedures to fully exploit the potential of each one. The main goal of the present study is to verify whether the electromyographic parameters, especially the conduction velocity (CV), are sensitive to the fatigue instauration process within sets of maximal isokinetic contractions. CV is a basic physiological parameter directly related to muscle activity and still little explored in experiments combining ID and SEMG. METHODS: Instrumentation architecture that combines ID and SEMG was used to estimate electromyographic and biomechanical parameters in protocols of maximum intensity isokinetic knee extension exercises. This architecture allows for limiting the parameter estimates to a specific region of isokinetic exercise, called the isokinetic load range (ILR), where one can consider that the angular velocity is constant and the SEMG signals are cyclo-stationary. Electromyographic signals were acquired using an array of electrodes. CONCLUSION: The results suggest that CV and the other SEMG parameters, including amplitude and frequency descriptors, are sensitive to detect a fatigue process only in protocols that restrict the analysis to ILR and that also bring the subject to a state of fatigue quickly.

S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation.

BACKGROUND: Surface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work. METHODS: This paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape. RESULTS: The proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown. CONCLUSIONS: The decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data compression algorithm with the established techniques found in scientific literature have shown promising results.

Sistema de apoio ao diagnóstico da Doença de Chagas baseado em escalogramas e redes neurais auto-organizáveis / Computer-aided system for diagnosis of Chagas' disease based on scalograms and self-organizing neural networks

INTRODUÇÃO: A Doença de Chagas é uma endemia rural, prevalente em grande parte da América Central e América do Sul e, aproximadamente, metade dos pacientes contaminados com o parasita Trypanosoma cruzi não apresentam sinais clínicos, eletrocardiográficos e radiológicos de envolvimento cardíaco. Este trabalho, entretanto, propõe uma técnica de auxílio ao diagnóstico da Doença de Chagas baseada em sinais de eletrocardiografia, que extrai informações relevantes desses sinais. MÉTODOS: Duas abordagens são estudadas e implementadas. Ambas utilizam sinais de variabilidade da frequência cardíaca (VFC) e classificação por meio de rede neural, mais especificamente, o mapa auto-organizável de Kohonen. A VFC, que reflete a modulação neural autonômica simpática e parassimpática do coração, é avaliada com base em séries contínuas de intervalos RR do ECG convencional registradas durante 5 minutos. Na primeira abordagem, indicadores estatístico-temporais obtidos diretamente dos sinais de VFC são utilizados como entrada da rede neural para treinamento e teste do método de classificação. Na segunda proposta, são utilizados escalogramas wavelet com função de base DoG (derivative of Gaussian) para avaliação dos sinais de VFC. Indicadores obtidos dos escalogramas são utilizados como entrada da rede neural no treinamento e no teste do algoritmo. Os mapas topológicos de Kohonen são utilizados para comparar a capacidade dos indicadores calculados dos sinais de VFC em discriminar pacientes chagásicos cardiopatas, chagásicos indeterminados e indivíduos normais. Os indicadores temporais convencionais e os indicadores escalográficos são comparados. RESULTADOS: Os resultados mostram que os indicadores escalográficos têm poder discriminatório estatisticamente superior aos indicadores temporais convencionais. Em particular, a potência média da densidade de potência do escalograma na banda de altas frequências mostrou ser estatisticamente o indicador de maior poder discriminatório (p < 0,05 para os 3 casos). CONCLUSÃO: A metodologia proposta mostrou-se capaz de distinguir entre indivíduos normais, chagásicos cardiopatas e chagásicos indeterminados. Os índices escalográficos propostos mostraram maior capacidade classificatória que os índices temporais tradicionais.

INTRODUCTION: Chagas' disease is an endemic rural disease prevalent in much of Central America and South America, and approximately half of the patients infected with the parasite Trypanosoma cruzi show no clinical, electrocardiographic and radiological cardiac involvement. This paper, however, proposes a technique for the diagnosis of Chagas' disease based on ECG signals, which extracts relevant information from these signals. METHODS: Two approaches are studied and implemented. Both approaches use heart rate variability (HRV) signals, and classification by a neural network, more specifically, the Kohonen self-organizing map. The HRV, which reflects sympathetic and parasympathetic autonomic neural modulation of the heart, is evaluated based on continuous series of RR intervals, calculated from 5-minute records of conventional ECG. In the first approach, statistical/temporal indexes obtained directly from the HRV signals are used as neural network inputs for training and testing of the classification method. In the second approach, derivative of Gaussian (DoG) wavelet scalograms are used to evaluate the HRV signals. Scalographic indexes are used as neural network inputs for training and testing of the algorithm. Kohonen topological maps are used to compare the ability of these HRV indicators of discriminating between patients with Chagas heart disease, Chagas indeterminate heart disease, and normal subjects. Conventional temporal indicators and indicators obtained from DoG scalograms are compared. RESULTS: Results of the application of the proposed methods to HRV signal databases, and performance comparisons, are presented. The results show that scalographic indicators have superior discriminatory power than conventional time-domain indicators. Based on an analysis of statistical significance, we show that the average power of the high-frequency band of the scalogram power spectral density is the indicator with greatest discriminatory power (p < 0,05 for all 3 cases). CONCLUSION: The proposed method has the ability to discriminate between normal subjects, subjects with Chagas cardiomyopathy, and subjects with the indeterminate form of Chagas' disease. It was observed that scalographic neural networks present greater discrimination ability than temporal neural networks.

On the behavior of surface electromyographic variables during the menstrual cycle.

The goal of this work is to study the behavior of electromyographic variables during the menstrual cycle. Ten female volunteers (24.0 ± 2.8 years of age) performed fatiguing isometric contractions, and electromyographic signals were measured on the biceps brachii in four phases of the menstrual cycle. Adaptations of classical algorithms were used for the estimation of the root mean square (RMS) value, absolute rectified value (ARV), mean frequency (MNF), median frequency (MDF), and conduction velocity (CV). The CV estimator had a higher (p = 0.002) rate of decrease at the end of the follicular phase and at the end of the luteal phase. The MDF (p = 0.002) and MNF (p = 0.004) estimators had a higher rate of decrease at the beginning of the follicular phase and at the end of the luteal phase. No significant differences between phases of the menstrual cycle were detected with the ARV and RMS estimators (p > 0.05). These results suggest that the behavior of the muscles in women presents different characteristics during different phases of the menstrual cycle. In particular, women were more susceptible to fatigue at the end of the luteal phase.

The influence of velocity overshoot movement artifact on isokinetic knee extension tests.

EXERCISE ON AN ISOKINETIC DEVICE INVOLVES THREE DISTINCT MOVEMENT PHASES: acceleration, constant velocity, and deceleration. Inherent in these phases are unique occurrences that may confound test data and, thereby, test interpretation. Standard methods of data reduction like windowing and other techniques consist of removing the acceleration and deceleration phases in order to assure analysis under constant velocity conditions. However, none of these techniques adequately quantify the velocity overshoot (VO) movement artifact which is a result of the devices resistance imposed to the limb. This study tested the influence of VO on isokinetic data interpretation. A computational algorithm was developed to accurately identify each movement phase and to delineate the VO segment. Therefore, the VO was then treated as a fourth and independent phase. A total of sixteen healthy men (26.8 ± 4.7 yrs, 1.76 ± 0.05 m, and 79.2 ± 9.4 kg) performed two sets of ten maximal concentric extension repetitions of their dominant knee (at 60°·s(-1) and 180°·s(-1)), on separate days and in a counterbalanced order, on a Biodex System 3 Pro dynamometer. All the phases of the isokinetic exercise were measured in terms of their biomechanical descriptors and according to the developed algorithm, the windowing method, and a data reduction technique that eliminates the first and last 10° of the total range of motion. Results showed significant differences (p < 0.05) between the constant velocity phases found by each method: the largest segment was obtained with the windowing method; the second one, with the algorithm; and the smallest, with data reduction technique. The point of peak torque was not affected by none of the techniques, but significant differences (p < 0.05) were found between the data including and not including the VO phase, concerning total work, time interval, and average length of load range: VO represents more than 10% of the amount calculated in constant velocity phase. As a consequence, the correct removal of VO was suggested as a required procedure to adequately interpret isokinetic tests. Therefore, the use of the proposed algorithm is advisable in order to perform analysis according to the isokinetic definition. Key pointsIsokinetic test interpretation must be focused on the constant velocity range; traditional analysis usually removes the acceleration and deceleration phases but does not give particular attention to velocity overshoot range.The study of effects of velocity overshoot artifact requires a specific method for accurately delineate its interval and investigate its impact over biomechanical descriptors; this paper proposed a computational algorithm for identifying the velocity overshoot interval.Velocity overshoot has significant impact over biomechanical descriptors analyzed during isokinetic knee extension tests at 60°·s(-1) and 180°·s(-1); the algorithm proposed is an advisable method for performing isokinetic tests analysis according to the isokinetic definition.

Compression of electromyographic signals using image compression techniques.

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, few studies have addressed the compression of such signals. In this article we present an algorithm for compression of electromyographic signals based on the JPEG2000 coding system. Although the JPEG2000 codec was originally designed for compression of still images, we show that it can also be used to compress EMG signals for both isotonic and isometric contractions. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.75% to 13.7%. For isotonic EMG signals, the algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.4% to 7%. The compression results using the JPEG2000 algorithm were compared to those using other algorithms based on the wavelet transform.

Uma rede de sensores para monitoração do corpo humano com suporte à programação / A body sensor netword with programming support

Este trabalho apresenta uma rede de sensores utilizada para monitoração de variáveis fisiológicas do corpo humano com suporte à programação. A tarefa de configuração dese tipo de sistema é usualmente realizada por desenvolvedores especializados, profissionais da computação com grande conhecimento de linguagens de programação. Entretanto, para que essa tecnologia se torne clinicamente viável, é necessário que os próprios profissionais da área de saúde possam fazê-lo. A programação e a reconfiguração à distância de uma rede de sensores sem fios é o principal objetivo deste trabalho. Como contribuição maior é apresentada uma arquitetura de software denominada SOAB (Software Architecture for Bodyworn Sensor Networks Project). Concebida com base em uma abordagem top-down, a arquitetura SOAB é constituída por quatro camadas independentes: i)uma interface gráfica direcionada aos profissionais de saúde; ii)middleware para interconexão da rede de sensores para monitoração do corpo humano com a Internet;iii)um servidor para execução dos serviços solicitados pelos programadores; e iv)um sistema operacional com suporte para multitarefa que será embutido nos sós-sensores. Esse sistema operacional foi denominado MedOS e visa aumentar a sobrevida dos nós-sensores (tempo de operação), promovendo a redução do consumo de energia elétrica por meio do escalonamento de tarefas com base em políticas adaptadas para aplicações biomédicas. A sistematização dessas políticas foi obtida por meio da utilização de um modelo baseado em autômatos. Para avaliar a arquitetura SOAB foi elaborado e aplicado um teste de carga, cujo objetivo foi quantificar o tempo gasto para programação de um nó-sensor do eletrocardiograma (ECG). Os resultados dos testes mostraram que o sistema proposto tem um bom potencial para se tornar uma ferramenta eficiente para a programação de redes de sensores para monitoração do corpo humano por profissionais não-especializados na área de informática.

Algoritmo híbrido para segmentação do ventrículo esquerdo em imagens de ecocardiografia bidimensional / Hybrid algorithm for segmentation of the left ventricle in two-dimensional echocardiography

Este trabalho apresenta uma técnica para segmentação automática da cavidade ventricular esquerda em ecocardiogramas bidimensionais. O algoritmo realiza a identificação e a extração das bordas do ventrículo esquerdo, que permite a obtenção de parâmetros clínicos utilizados no diagnóstico de diversas patologias cardíacas. O processo de extração de bordas é constituído de três etapas: o pré-processamento, a segmentação e a detecção do contorno. Na etapa de pré-processamento é realizada, primeiramente, a aplicação de um algoritmo de minimização do ruído mediante técnica que utiliza transformada de wavelets, seguida da aplicação de uma técnica de elevação do contraste, que prepara a imagem para a próxima etapa. Na etapa de segmentação é utilizada a técnica de watersheds. Na última etapa, procedimentos de morfologia matemática são utilizados para detecção e extração do contorno. A avaliação do desempenho da técnica apresentada foi realizada por meio da comparação dos resultados obtidos pela segmentação automática do ventrículo esquerdo com os resultados obtidos com segmentação manual, feita por um especialista. Os resultados obtidos foram também comparados com os resultdos de outras técnicas descritas na literatura. Para os testes do algoritmo foram descritas na literatura. Para os testes de algoritmo foram utilizadas 60 imagens de ecocardiogramas bidimensionais de pacientes atendidos no serviço de ecocardiolografia do Hospital Universitário de Brasília. Os resultados experimentais mostraram um bom desempenho do método, mesmo em imagens de baixa qualidade, sendo esses resultados superiores aos de outros métodos apresentados na literatura.