Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Department of Biomedical Sciences, University of Padova, Padua, Italy.
J Neural Eng. 2022 Aug 2;19(4). doi: 10.1088/1741-2552/ac823d.
High-density surface electromyography (HD-sEMG) allows the reliable identification of individual motor unit (MU) action potentials. Despite the accuracy in decomposition, there is a large variability in the number of identified MUs across individuals and exerted forces. Here we present a systematic investigation of the anatomical and neural factors that determine this variability.. We investigated factors of influence on HD-sEMG decomposition, such as synchronization of MU discharges, distribution of MU territories, muscle-electrode distance (MED-subcutaneous adipose tissue thickness), maximum anatomical cross-sectional area (ACSA), and fiber cross-sectional area. For this purpose, we recorded HD-sEMG signals, ultrasound and magnetic resonance images, and took a muscle biopsy from the biceps brachii muscle from 30 male participants drawn from two groups to ensure variability within the factors-untrained-controls (UT = 14) and strength-trained individuals (ST = 16). Participants performed isometric ramp contractions with elbow flexors (at 15%, 35%, 50% and 70% maximum voluntary torque-MVT). We assessed the correlation between the number of accurately detected MUs by HD-sEMG decomposition and each measured parameter, for each target force level. Multiple regression analysis was then applied.ST subjects showed lower MED (UT = 5.1 ± 1.4 mm; ST = 3.8 ± 0.8 mm) and a greater number of identified MUs (UT: 21.3 ± 10.2 vs ST: 29.2 ± 11.8 MUs/subject across all force levels). The entire cohort showed a negative correlation between MED and the number of identified MUs at low forces (= -0.6,= 0.002 at 15% MVT). Moreover, the number of identified MUs was positively correlated to the distribution of MU territories (= 0.56,= 0.01) and ACSA(= 0.48,= 0.03) at 15% MVT. By accounting for all anatomical parameters, we were able to partly predict the number of decomposed MUs at low but not at high forces.Our results confirmed the influence of subcutaneous tissue on the quality of HD-sEMG signals and demonstrated that MU spatial distribution and ACSAare also relevant parameters of influence for current decomposition algorithms.
高密度表面肌电图（HD-sEMG）可实现对单个运动单位（MU）动作电位的可靠识别。尽管在分解方面具有很高的准确性，但在个体和施加的力之间，识别的 MU 数量存在很大的可变性。在这里，我们系统地研究了决定这种可变性的解剖学和神经因素。我们研究了影响 HD-sEMG 分解的因素，例如 MU 放电的同步性、MU 区域的分布、肌-电极距离（皮下脂肪组织厚度）、最大解剖横截面积（ACSA）和纤维横截面积。为此，我们从两个组中招募了 30 名男性参与者，从他们的肱二头肌中记录了 HD-sEMG 信号、超声和磁共振图像，并进行了肌肉活检。参与者进行了等长斜坡收缩，肘部弯曲（在 15%、35%、50%和 70%最大自愿扭矩-MVT 下）。我们评估了在每个目标力水平下，HD-sEMG 分解准确检测到的 MU 数量与每个测量参数之间的相关性。然后应用多元回归分析。ST 组的 MED 较低（UT=5.1±1.4mm；ST=3.8±0.8mm），识别的 MU 数量较多（UT：21.3±10.2 个；ST：29.2±11.8 个/MU/个体，所有力水平）。整个队列在低力时，MED 与识别的 MU 数量之间呈负相关（=-0.6，在 15%MVT 时=0.002）。此外，在 15%MVT 时，识别的 MU 数量与 MU 区域的分布呈正相关（=0.56，=0.01）和 ACSA（=0.48，=0.03）。通过考虑所有解剖学参数，我们能够部分预测低力但不能预测高力时分解的 MU 数量。我们的结果证实了皮下组织对 HD-sEMG 信号质量的影响，并表明 MU 空间分布和 ACSA 也是当前分解算法的相关影响因素。
