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.

中文翻译：

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用表面肌电图记录近端肌肉网络的活动以评估婴儿运动发育

客观的

开发记录和分析婴儿近端肌肉活动的方法。

方法

三个月大的婴儿（N = 18）在自发运动和受控姿势变化期间记录了躯干肌肉的表面肌电图（sEMG）。婴儿也根据运动表现分为两组。我们开发了一种去除动态心脏伪影的有效方法，以允许 i) 准确估计单个肌肉激活，以及 ii) 肌肉网络的定量表征。

结果

心脏伪影的自动去除允许对躯干肌肉活动进行定量，这在姿势变化期间显示出可预测的影响，并且表现良好和表现不佳的婴儿之间存在差异。 在仰卧位和俯卧位之间的自发运动期间，肌肉网络显示出网络密度的一致变化。此外，每对背部肌肉的活动相关性与婴儿的运动表现有关。

结论

特此开发的 sEMG 分析方法是可行的，并且可以揭示高表现和低表现婴儿之间的差异。肌肉网络的分析可能为运动的中央控制提供新的见解。

意义

婴儿肌肉活动和肌肉网络的定量分析有望对运动系统进行客观的神经发育评估。