Use of electromyographic signals for characterization of voluntary coughing in humans with and without spinal cord injury-A systematic review.

INTRODUCTION: Individuals with spinal cord injury (SCI) frequently have an ineffective cough ability due to dysfunctions in expiratory muscles. In such cases, several articles have reported the occurrence of residual muscular activity in muscles that are accessory to coughing. The knowledge about this activity may be useful for building cough assistance devices. The goal of this review is to investigate and to describe the electromyographic signals generated during voluntary coughing in healthy people and in patients with SCI. METHODS: Two researchers performed, blindly and independently, a systematic review of the following databases: PubMed, PEDro, LILACS, and Science Direct. We conducted the searches using descriptors in English, Portuguese, and Spanish, with no limitations regarding the publication year. The review included articles describing experiments performed in humans and with the use of electromyographic signals in the analysis of voluntary coughing. RESULTS: Among the 156 initially found articles, only nine had results that described the study of electromyographic signals associated with voluntary coughing. The results showed evidence that, during voluntary coughing, electromyographic signals are generated both in expiratory and accessory muscles in healthy subjects. In individuals with SCI below the 5th cervical level (C5), the electromyographic signal appeared only in the clavicular portion of the pectoralis major, especially in the explosive cough phase. CONCLUSION: Our evaluation of the current literature shows that, according to the analysed studies, the electromyographic signals are more pre-eminent in the expiratory phase of the pectoralis major, during voluntary cough of individuals with SCI (C5-T12).

Novas perspectivas de fisioterapia respiratória em lesão medular - uma revisão sistemática / New perspectives for chest physical therapy in spinal cord injury - a systematic review

Resumo Objetivo: Descrever e analisar parâmetros e efeitos da estimulação elétrica de superfície na função muscular respiratória de pessoas com lesão medular, sobretudo durante a tosse. Métodos: Foi realizada uma revisão sistemática da literatura, com base no Preferred Reporting items for Systematic Reviews and Meta-Analyses. A busca foi realizada nas bases de dados PubMed, PEDro e LILACS, por meio dos seguintes descritores: "estimulação elétrica funcional", "eletroestimulação, estimulação elétrica", "tosse", "higiene brônquica", "quadriplegia", "lesão medular espinhal", "tetraplegia" e "sujeito com tetraplegia" - em espanhol, inglês e português, sem restrição quanto ao ano de publicação. Foram incluídos artigos com amostra de indivíduos com lesão medular assistidos por estimulação elétrica com desfecho relacionado ao sistema respiratório, e foram excluídos artigos com ensaios invasivos de estímulo a tosse. Resultados: Os 12 artigos incluídos revelam heterogeneidade nos protocolos de eletroestimulação da função expiratória, que podem incluir frequências de 30 a 50 Hz, com pulsos de 25 a 400 μs, aplicada por até oito eletrodos distribuídos pelos músculos expiratórios e acessórios. O tempo de aplicação também foi variável e a amplitude de corrente frequentemente estimada pela percepção do paciente, podendo chegar a valores superiores a 100mA. Conclusão: Apesar de não ser possível estabelecer parâmetros rigorosos de fisioterapia por meio da estimulação elétrica, pela escassez e qualidade de estudos que comparem sistematicamente parâmetros de estimulação em subgrupos, foram observadas alterações positivas nas variáveis de função muscular respiratória avaliadas, como o pico de fluxo expiratório e de tosse, em pessoas com lesão medular cervical e torácica.

Abstract Objective: To describe and analyze parameters and effects of surface electrical stimulation on the respiratory muscular function among individuals with spinal cord injuries, especially while coughing. Methods: A systematic literature review was developed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The search was conducted in the PubMed, PEDro, and LILACS databases, using the following descriptors: "functional electrical stimulation," "electrostimulation, electrical stimulation," "coughing," "bronchial hygiene," "quadriplegia," "spinal cord injury", "tetraplegia", and "individual with tetraplegia" - in Spanish, English and Portuguese, with no restrictions on year of publication. Inclusion criteria were: articles describing studies with samples of individuals with spinal cord injuries treated with electrical stimulation and outcomes related to the respiratory system. Articles containing studies with invasive cough stimulation trials were excluded. Results: The 12 selected articles revealed the heterogeneity of electrostimulation protocols for expiratory function, which can include frequencies ranging from 30 to 50 Hz; pulse from 25 to 400 μs; applied in up to eight electrodes distributed across the expiratory and accessory muscles. Time of administration also varied, and the current amplitude was usually estimated by the patient's perception, reaching values higher than 100mA. Conclusion: Even though the review did not find rigorous parameters for physical therapy using electrical stimulation, because of the shortage and low-quality of the studies that systematically compare stimulation parameters among subgroups, positive changes were observed in the assessed respiratory muscle function variables, such as peak expiratory and cough flow in individuals with cervical and thoracic spinal cord injury.

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.

Spatially distributed surface electromyography signal simulator.

Electromyographic signals are of great importance to current biomedical research society since they may be used in several ways as, for example, in the diagnosis of neuromuscular diseases, the control of active prosthetic limbs as well as the test and validation of medical equipment.

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning.

BACKGROUND: The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored. METHODS: The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A1 (the classification error) and A2 (the correlation factor). Otherwise, the B factor has four levels, specifically B1 (the Sequential Forward Selection, SFS), B2 (the Sequential Floating Forward Selection, SFFS), B3 (Artificial Bee Colony, ABC), and B4 (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS. RESULTS: A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F0.01,3,72 = 4.0659 > fAB = 0.09), (2) the levels of factor A have significative effects on the classification error (F0.02,1,72 = 5.0162 < fA = 6.56), and (3) the levels of factor B over the classification error are not significative (F0.01,3,72 = 4.0659 > fB = 0.08). CONCLUSIONS: Considering the classification performance we found a superiority of using the factor A2 in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm.

Evaluating different combinations of feature selection algorithms and cost functions applied to iPCA tuning in myoelectric control systems.

A myoelectric control system extracts information from electromyographic (EMG) signals and uses it to control different types of prostheses, so that people who suffered traumatisms, paralysis or amputations can use them to execute common movements. Recent research shows that the addition of a tuning stage, using the individual component analysis (iPCA), results in improved classification performance. We propose and evaluate a set of novel configurations for the iPCA tuning, based on a biologically inspired optimization procedure, the artificial bee colony algorithm. This procedure is implemented and tested using two different cost functions, the traditional classification error and the proposed correlation factor, which involves lower computational effort. We compare the tuned system's performance, in terms of correct classifications, to that of a system tuned using two standard algorithms, the sequential forward selection and the sequential floating forward selection. The statistical analyses of the results don't find a significant difference among the classification performances associated with the search algorithms (p < 0.01). On the other hand, they establish a significant difference among the classification performances related to the cost functions (p < 0.02).