Swallowing in Patients with Parkinson’s Disease: A Surface Electromyography Study

Coriolano, Maria das Graças Ws; Belo, Luciana Rodrigues; Carneiro, Danielle; Asano, Amdore Guesel; Oliveira, Paulo José Al; Silva, Douglas Monteiro da; Lins, Otávio Gomes

doi:10.1007/s00455-012-9406-0

Cited by 32 publications

(28 citation statements)

References 26 publications

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“…Other studies analyzing the characteristics of PD vs. control swallows via sEMG have found significant differences in swallowing behavior compared to controls (Coriolano et al, 2012; Tawadros, Cordato, Cathers, & Burne, 2012). Tawadros et al characterized the sEMG activity at the submental and anterior neck during swallowing water boluses of varying volumes in participants with PD and healthy, age-matched controls, reporting that PD swallows had increased duration (Tawadros et al, 2012).…”

Section: Discussionmentioning

confidence: 93%

Visuomotor control of neck surface electromyography in Parkinson's disease

Malloy

Valentin

Hands

et al. 2014

NRE

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OBJECTIVE: To compare performance of individuals with Parkinson's disease (PD) and age-matched controls on a visuomotor tracking task controlled via surface electromyography (sEMG). METHODS: Twenty-seven adults with PD and twenty-four older controls produced dry swallows and completed a visuomotor tracking task utilizing both static and dynamic targets. sEMG was recorded at the anterior neck and submental surface during both tasks. RESULTS: There was no significant difference in visuomotor tracking ability between cohorts. Post hoc analyses indicated that there was no significant difference between participant groups in the strength or duration of swallows as measured by sEMG but that participants with PD showed a trend for decreased swallow durations at the anterior neck (p adj = 0.067) whereas controls showed a trend for increased durations at the anterior neck (p adj = 0.112), compared to the submental surface. However, there were no significant correlations between swallowing behavior and visuomotor tracking ability. CONCLUSION: There were no significant differences in visuomotor tracking performance between individuals with PD and controls. Furthermore, there was no relationship between tracking ability and swallowing behavior. We conclude that sEMGmediated biofeedback may have limited promise as a tool for treating PD-related dysphagia.

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Section: Discussionmentioning

confidence: 93%

Visuomotor control of neck surface electromyography in Parkinson's disease

Malloy

Valentin

Hands

et al. 2014

NRE

View full text Add to dashboard Cite

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“…It has been more discussed in studies on the first two functions. [20][21][22][23] These studies, however, do not seem to converge on the best standard to normalize the SEMG signal, that is, by the peak, 20,24-26 mean peak, 22 mean MVC, or MEA. 21,23 In studies on phonation, according to a review addressing the topic, 27 despite the MVC being the most commonly used and most referred standard, studies also do not converge regarding the muscles evaluated, namely, the SH, 6,9,14,16 IH, [5][6][7][8]10 sternocleidomastoid, 11,12,15 and scalene.…”

Section: E6mentioning

confidence: 99%

Normalization Patterns of the Surface Electromyographic Signal in the Phonation Evaluation

et al. 2015

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“…The EMG signals of the suprahyoid muscles are detectable via electrodes placed on the skin of the underside of the jaw [33][34][35]. The suprahyoid muscles comprise several muscle groups such as digastric muscles, stylohyoid muscles, mylohyoid muscles, and geniohyoid muscles, as presented in Fig.…”

Section: Emg Measurement Approachmentioning

confidence: 99%

Tongue interface based on surface EMG signals of suprahyoid muscles

et al. 2016

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The research described herein was undertaken to develop and test a novel tongue interface based on classification of tongue motions from the surface electromyography (EMG) signals of the suprahyoid muscles detected at the underside of the jaw. The EMG signals are measured via 22 active surface electrodes mounted onto a special flexible boomerang-shaped base. Because of the sensor's shape and flexibility, it can adapt to the underjaw skin contour. Tongue motion classification was achieved using a support vector machine (SVM) algorithm for pattern recognition where the root mean square (RMS) features and cepstrum coefficients (CC) features of the EMG signals were analyzed. The effectiveness of the approach was verified with a test for the classification of six tongue motions conducted with a group of five healthy adult volunteer subjects who had normal motor tongue functions. Results showed that the system classified all six tongue motions with high accuracy of 95.1 ± 1.9 %. The proposed method for control of assistive devices was evaluated using a test in which a computer simulation model of an electric wheelchair was controlled using six tongue motions. This interface system, which weighs only 13.6 g and which has a simple appearance, requires no installation of any sensor into the mouth cavity. Therefore, it does not hinder user activities such as swallowing, chewing, or talking. The number of tongue motions is sufficient for the control of most assistive devices.

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Swallowing in Patients with Parkinson’s Disease: A Surface Electromyography Study

Cited by 32 publications

References 26 publications

Visuomotor control of neck surface electromyography in Parkinson's disease

Visuomotor control of neck surface electromyography in Parkinson's disease

Normalization Patterns of the Surface Electromyographic Signal in the Phonation Evaluation

Tongue interface based on surface EMG signals of suprahyoid muscles

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