Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

Belavý, Daniel L.; Mehnert, Andrew; Wilson, Stephen J.; Richardson, Carolyn A.

doi:10.1016/j.jelekin.2007.02.018

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In addition, several studies have shown that a muscle may act as tonic or phasic muscle depending on the subject's posture or movement performed [22, 23]. …”

Section: Introductionmentioning

confidence: 99%

Differences in Muscle Activation Patterns during Sit to Stand Task among Subjects with and without Intellectual Disability

Cuesta‐Vargas

Gónzalez-Sánchez

2013

BioMed Research International

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The aim of this study is to analyse the differences in muscle activity between subjects who have intellectual disability and healthy subjects when they make the transition from sitting to standing positions. A cross-sectional study. A group of adults was divided into two subgroups: with and without intellectual disability (ID). The means of the basic features in both groups were 22.13 and 22.83 for age, 66.38 and 67.67 for weight, and 173.38 and 174.33 for height, for the ID (n = 8) and without ID (n = 7) groups, respectively. Each subject performed three sets of five repetitions during which, starting from sitting, they had to get up and sit on the chair. The recording of muscle activity was performed using surface electromyography taking the measures of muscle activity of different muscles of the lower limbs. The results showed differences in the pattern of muscle activity between groups during sitting to standing movement.

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“…In addition, several studies have shown that a muscle may act as tonic or phasic muscle depending on the subject's posture or movement performed [22, 23]. …”

Section: Introductionmentioning

confidence: 99%

Differences in Muscle Activation Patterns during Sit to Stand Task among Subjects with and without Intellectual Disability

Cuesta‐Vargas

Gónzalez-Sánchez

2013

BioMed Research International

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“…Because our dEMG signal is robust and consistent, using energy differential methods alone is sufficient to reliably capture all respiratory bursts for analysis. We measure average burst rectified area as the primary outcome in quantitatively assessing diaphragm function, which has previously been used and validated in analysis of chronic EMG 14. Other methods of quantitative EMG analysis based on similar parameters such as average amplitude and root‐mean‐square amplitude were also analyzed (data not shown) and found to be consistent with the rectified area results 15.…”

Section: Discussionmentioning

confidence: 99%

Analysis of continuous diaphragm electromyographic signal: Results from a patient with amyotrophic lateral sclerosis

et al. 2011

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“…The existent approaches in EMG reproduction include the use of a sum of diphasic waves [ 4 , 5 ], the implementation of a random EMG tonic wave and multiplication by a sinus wave [ 6 ] or using autoregressive models and mixing them with gaussian noise [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Biosignals learning and synthesis using deep neural networks

et al. 2017

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BackgroundModeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.MethodThe present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself.Results and conclusionsThe resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models’ prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

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Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

Cited by 7 publications

References 20 publications

Differences in Muscle Activation Patterns during Sit to Stand Task among Subjects with and without Intellectual Disability

Differences in Muscle Activation Patterns during Sit to Stand Task among Subjects with and without Intellectual Disability

Analysis of continuous diaphragm electromyographic signal: Results from a patient with amyotrophic lateral sclerosis

Biosignals learning and synthesis using deep neural networks

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