Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

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.

Electromyographic evaluation of muscle recovery after isometric fatigue.

Despite growing interest in the behavior of electromyographic signals during muscle fatigue, few studies investigate fatigue recovery. In this work, we use surface electromyographic signals to determine the recovery time after isometric fatigue of the biceps brachii muscle in 90° flexion of the non-dominant elbow. Sixty volunteers were arranged into six experimental groups. Experiments were performed in three stages: reference phase (REF), fatigue resistance phase (RES), and recovery phase (REC). An isometric exercise was performed during the RES stage. The time interval between the RES and REC stages was different for each experimental group: 1, 2, 4, 8, 24 and 48 hours. Surface electromyographic signals were acquired during each phase, and the following electromyographic variables were calculated for each phase: median frequency (MDF), root mean squared (RMS) value, and maximum voluntary contraction (MVC). The REF data were compared with the REC data using a paired Wilcoxon test. The results show that the MVC is recovered 2 hours after the exercise. The MDF seems not to be fully recovered after 48 hours, but displays an apparent recovery trend.

Evaluation of a mobile electrode for electrical stimulation of the perineal muscle.

This work evaluates the effectiveness of a new type of electrode for functional electrical stimulation of the perineal muscle in women. The new electrode is shaped like a pen, with an active stimulation electrode located on its tip. The goals of the study are to (i) demonstrate that stimulation using the new device results in increased muscle strength; and (ii) compare the performance of the new device with that of a traditional (fixed) electrode. Eight women were evaluated, following a blind study protocol. The preliminary results suggest that stimulation with the new electrode achieves better results than stimulation with traditional electrodes, as higher increases in strength were observed in the group that used the mobile electrode.

Improved segmentation of echocardiographic images using fusion of images from different cardiac cycles.

In this work, an algorithm for the detection of the left ventricular border in two-dimensional long axis echocardiographic images is presented. In its preprocessing stage, images fusion was applied to a sequence of images composed of three cardiac cycles. This method exploits the similarity of corresponding frames from different cycles and produces contrast enhancement in the left ventricular boundary. This result improves the performance of the segmentation stage which is based on watershed transformation. The obtained left ventricle border is quantitatively and qualitatively compared with contours manually segmented by a cardiologist, and with results obtained using seven different techniques from the literature.

Fusion of electromyographic signals with proprioceptive sensor data in myoelectric pattern recognition for control of active transfemoral leg prostheses.

This paper presents a myoelectric knee joint angle estimation algorithm for control of active transfemoral prostheses, based on feature extraction and pattern classification. The feature extraction stage uses a combination of time domain and frequency domain methods (entropy of myoelectric signals and cepstral coefficients, respectively). Additionally, the methods are fused with data from proprioceptive sensors (gyroscopes), from which angular rate information is extracted using a Kalman filter. The algorithm uses a Levenberg-Marquardt neural network for estimating the intended knee joint angle. The proposed method is demonstrated in a normal volunteer, and the results are compared with pattern classification methods based solely on electromyographic data. The use of surface electromyographic signals and additional information related to proprioception improves the knee joint angle estimation precision and reduces estimation artifacts.

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.