A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation

Qiu, Shuang; Feng, Jing; Xu, Rui; Xu, Jun; Wang, Kun; He, Feng; Qi, Hongzhi; Zhao, Xin; Zhou, Peng; Zhang, Lixin; Ming, Dong

doi:10.1109/tbme.2015.2407834

Cited by 31 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…EMG signals are affected by the stimulation artefacts caused by the stimulation current when NMES is used. This has been partially addressed in the literature with, for example, use of an EMG-amplifier with shut-down control [43], advanced filtering procedures [44] and optimizing the positioning of the EMG electrodes in relation to the stimulation electrodes [45]. However, these approaches do not fully eliminate the stimulation artefacts and we, therefore, were unable to use EMG as a validation of the method.…”

Section: Discussionmentioning

confidence: 99%

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading

Ming

Ding

et al. 2019

R. Soc. open sci.

Self Cite

View full text Add to dashboard Cite

Medial knee joint osteoarthritis (OA) is a debilitating and prevalent condition. Surgical treatment consists of redistributing the forces from the medial to the lateral compartment through osteotomy, or replacing the joint surfaces. As the mediolateral load distribution is related to the action of the musculature around the knee, the aim of this study was to devise a technique to redistribute these forces non-surgically through changes in muscle excitation. Eight healthy subjects participated in the experiment, and neuromuscular electrical stimulation was used to change the muscle forces around the knee. A musculoskeletal model was used to quantify the loading on the medial compartment of the knee, and a novel algorithm devised and implemented to simulate neuromuscular electrical stimulation. The forces and moments at the knee, ground reaction forces, walking velocity and step length were quantified before and after stimulation. Stimulation of the biceps femoris resulted in a significant decrease in the second peak of the medial knee joint loading by up to 0.17 body weight ( p = 0.016). Kinematic parameters were not significantly affected. Neuromuscular electrical stimulation can decrease the peak loads on the medial compartment of the knee, and thus offers a promising therapy for medial knee joint OA.

show abstract

Section: Discussionmentioning

confidence: 99%

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading

Ming

Ding

et al. 2019

R. Soc. open sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A wavelet-based denoising method with soft threshold was utilized. The db4 wavelet was chosen as mother wavelet because its shape is similar to that of a heart sound signal, and the scale was established as 7 based on signal–noise-ratio (SNR) and normalized root mean squared error (NRMSE) [ 13 ]. The denoised heart sound was written as .…”

Section: Methodsmentioning

confidence: 99%

An automatic segmentation method for heart sounds

Liu

Wang

2018

BioMed Eng OnLine

View full text Add to dashboard Cite

BackgroundThere are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value.MethodsThis paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time–frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification.ResultsIn order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively.ConclusionThe proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.

show abstract

“…Nestes casos, diversos algoritmos de modelagem adaptativa e remoção de artefato foram propostos 1,6,7,8 , os quais utilizam um sinal de referência a fim de estimar o artefato que deve ser removido. Esse sinal de referência pode ser obtido tanto através de um segundo eletrodo quanto de um segundo estimulo feito de forma a não gerar onda M 1 .…”

Section: Introductionunclassified

Avaliação de métodos adaptativos e baseados em software para compensação de artefato de estímulo em sEMG

et al. 2019

View full text Add to dashboard Cite

Potenciais evocados eletricamente, medidos por meio de sEMGs, têm várias aplicações clínicas tradicionais, como monitoramento intraoperatório de nervos e terapias de reabilitação. No entanto, o estímulo elétrico gera um artefato que pode interferir nos sinais medidos, o qual não é facilmente removido por promediação. Os artefatos são particularmente difíceis de serem removidos em aplicações como o monitoramento do nervo facial, onde os estímulos e os eletrodos de sinal ficam próximos e a sobreposição entre o artefato e a onda M é comum. Métodos adaptativos para a supressão de artefatos — utilizando um sinal de referência que contenha apenas o artefato de estímulo — foram propostos nas configurações off-nerve, estímulo duplo e sublimiar. Recentemente, foram propostos algoritmos usando inteligência computacional para proceder em relação à segmentação de sinais, mascaramento e reconstrução de ondas M, chamados de métodos baseados em software. Porém, uma comparação sistemática das técnicas de supressão de artefatos não é encontrada na literatura. Neste trabalho, implementou-se um modelo completo de geração de onda M e propagação de artefato, o qual foi usado para comparar métodos adaptativos e métodos baseados em software. Dois conjuntos de sinais aleatórios e artefatos com vários graus de sobreposição e amplitudes foram criados e empregados na comparação dos métodos. Os resultados apontam uma vantagem no uso dos algoritmos baseados em software, conforme avaliado pelos índices de coeficiente de correlação, erro RMS, erro de latência e erro de amplitude pico a pico.

show abstract

A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation

Abstract: GA-AMF could provide technical support for improving EMG feedback control of FES rehabilitation system.

Cited by 31 publications

References 27 publications

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading

An automatic segmentation method for heart sounds

Avaliação de métodos adaptativos e baseados em software para compensação de artefato de estímulo em sEMG

Contact Info

Product

Resources

About