High Density Surface EMG System Based on ADS1298-front end

Favretto, M. A.; Cossul, Sandra; Andreis, Felipe Rettore; Balotin, A. F.; Marques, Jefferson Luiz Brum

doi:10.1109/tla.2018.8444157

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The HD-sEMG signals were recorded from the TA muscle using a 32-channel EMG system in a monopolar configuration. The sampling frequency was 2000 Hz, 8x gain, and digitized with a 24-bit A/D converter (Favretto et al, 2018). A 13x5 electrode array (ELSCH064NM2, OT Bioelettronica, Torino, Italy, 2 mm diameter, 8 mm inter-electrode distance) was used for the recordings.…”

Section: Data Acquisitionmentioning

confidence: 99%

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities

Favretto¹,

Andreis²,

Cossul³

et al. 2023

Journal of Electromyography and Kinesiology

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Section: Data Acquisitionmentioning

confidence: 99%

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities

Favretto¹,

Andreis²,

Cossul³

et al. 2023

Journal of Electromyography and Kinesiology

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“…The HD-sEMG signals were recorded from the TA muscle using a 32-channel electromyography system, unity gain and digitised with a 24-bit A/D converter at 2 kHz per channel [33]. We used a two-dimensional array of 64 electrodes (ELSCH064NM4, OT Bioelettronica, Torino, Italy) for the acquisition, consisting of thirteen rows and five columns, with a 1 mm diameter and a distance between electrodes of 4 mm.…”

Section: Data Acquisitionmentioning

confidence: 99%

Alterations of tibialis anterior muscle activation pattern in subjects with type 2 diabetes and diabetic peripheral neuropathy

Favretto

Cossul

Andreis

et al. 2022

Biomed. Phys. Eng. Express

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Diabetic peripheral neuropathy (DPN) is associated with loss of motor units (MUs), which can cause changes in the activation pattern of muscle fibres. This study investigated the pattern of muscle activation using high-density surface electromyography (HD-sEMG) signals from subjects with type 2 diabetes mellitus (T2DM) and DPN. Thirty-five adults participated in the study: 12 healthy subjects (HV), 12 patients with T2DM without DPN (No-DPN) and 11 patients with T2DM with DPN (DPN). HD-sEMG signals were recorded in the tibialis anterior muscle during an isometric contraction of ankle dorsiflexion at 50% of the maximum voluntary isometric contraction (MVIC) during 30-s. The calculated HD-sEMG signals parameters were the normalised root mean square (RMS), normalised median frequency (MDF), coefficient of variation (CoV) and modified entropy (ME). The RMS increased significantly (p = 0.001) with time only for the DPN group, while the MDF decreased significantly (p < 0.01) with time for the three groups. Moreover, the ME was significantly lower (p = 0.005), and CoV was significantly higher (p = 0.003) for the DPN group than the HV group. Using HD-sEMG, we have demonstrated a reduction in the number of MU recruited by individuals with DPN. This study provides proof of concept for the clinical utility of this technique for identifying neuromuscular impairment caused by DPN.

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“…The reference electrode was placed on the tuberosity of the tibia. The details of the experimental setup and the acquisition system have been previously reported in [29,30]. The signals were digitally filtered in MATLAB 2018 (MathWorks, MA, USA) by an eight order Butterworth bandpass filter with a frequency range of 10-400 Hz.…”

Section: Subjects and Experimental Protocolmentioning

confidence: 99%

Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy

Andreis

Favretto

Cossul

et al. 2020

Med Biol Eng Comput

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This article demonstrates the power and flexibility of linear mixed-effects models (LMEM) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting the limitations of traditional approaches by building a linear model with only fixedeffects. Then, we showed how the model adequacy could be increased by including random-effects, as well as by adding alternative correlation structures. The models were compared with the Akaike information criterion and the Bayesian information criterion, as well as the likelihood ratio test. The results showed that the inclusion of the random-effects of intercept and slope, along with an autoregressive moving average correlation structure is the one that best describes the data (p < .01). Furthermore, we demonstrate how the inclusion of additional variance structures can accommodate heterogeneity in the residual analysis and therefore increase model adequacy (p < .01). Thus, in conclusion, we suggest that adopting LMEM to repeated measures such as electromyography can provide additional information from the data (e.g., test for alternative correlation structures of the RMS value), and hence provide new insights into HD-sEMG related work.

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High Density Surface EMG System Based on ADS1298-front end

Cited by 8 publications

References 18 publications

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities

Alterations of tibialis anterior muscle activation pattern in subjects with type 2 diabetes and diabetic peripheral neuropathy

Linear mixed-effects models for the analysis of high-density electromyography with application to diabetic peripheral neuropathy

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