Recording activity in proximal muscle networks with surface EMG in assessing infant motor development

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http://hdl.handle.net/10138/335265

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Hautala , S , Tokariev , A , Roienko , O , Häyrinen , T , Ilen , E , Haataja , L & Vanhatalo , S 2021 , ' Recording activity in proximal muscle networks with surface EMG in assessing infant motor development ' , Clinical Neurophysiology , vol. 132 , no. 11 , pp. 2840-2850 . https://doi.org/10.1016/j.clinph.2021.07.031

Title: Recording activity in proximal muscle networks with surface EMG in assessing infant motor development
Author: Hautala, Sini; Tokariev, Anton; Roienko, Oleksii; Häyrinen, Taru; Ilen, Elina; Haataja, Leena; Vanhatalo, Sampsa
Other contributor: University of Helsinki, University of Helsinki
University of Helsinki, HUSLAB
University of Helsinki, HUS Children and Adolescents
University of Helsinki, Children's Hospital
University of Helsinki, Children's Hospital
University of Helsinki, HUS Medical Imaging Center








Date: 2021-11
Language: eng
Number of pages: 11
Belongs to series: Clinical Neurophysiology
ISSN: 1388-2457
DOI: https://doi.org/10.1016/j.clinph.2021.07.031
URI: http://hdl.handle.net/10138/335265
Abstract: Objective: To develop methods for recording and analysing infant's proximal muscle activations. Methods: Surface electromyography (sEMG) of truncal muscles was recorded in three months old infants (N = 18) during spontaneous movement and controlled postural changes. The infants were also divided into two groups according to motor performance. We developed an efficient method for removing dynamic cardiac artefacts to allow i) accurate estimation of individual muscle activations, as well as ii) quantitative characterization of muscle networks. Results: The automated removal of cardiac artefacts allowed quantitation of truncal muscle activity, which showed predictable effects during postural changes, and there were differences between high and low performing infants. The muscle networks showed consistent change in network density during spontaneous movements between supine and prone position. Moreover, activity correlations in individual pairs of back muscles linked to infant́s motor performance. Conclusions: The hereby developed sEMG analysis methodology is feasible and may disclose differences between high and low performing infants. Analysis of the muscle networks may provide novel insight to central control of motility. Significance: Quantitative analysis of infant's muscle activity and muscle networks holds promise for an objective neurodevelopmental assessment of motor system.
Subject: Cardiac artefact
Infants
Muscle networks
Neurodevelopment
Surface electromyography
POSTURAL ADJUSTMENTS
CHILDREN
RESPONSES
HIGH-RISK
GENERAL MOVEMENTS
SYNERGY
BRAIN NETWORKS
CEREBRAL-PALSY
PRETERM
TYPICALLY DEVELOPING INFANTS
3123 Gynaecology and paediatrics
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