肌肉形状对HDsEMG分解产量和精度的影响
Andra Oltmann1, Jan Graßhoff1, Tobias Knopp2
1Fraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Lübeck, Germany.
概括
模拟肌肉形状会影响运动单元 (MU) 分解的准确性. 解剖学准确的模型提高了产量,而简化增加了MU识别,但保持了高度一致,这对于电肌图分析至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 计算建模 计算建模
背景情况:
- 高密度表面电肌图 (HD-sEMG) 与分解算法相结合,可以解码发动机单元 (MU) 的发射.
- 基于模型的模拟对于评估这些分解算法的产量和准确性至关重要.
研究的目的:
- 为了研究模拟肌肉形状对运动单元分解算法的性能的影响.
- 使用解剖肌肉模型与简化模型比较分解结果.
主要方法:
- 开发了一个手臂模型,具有解剖学双臂臂形状,并通过实验数据验证了其可信性.
- 从解剖学模型中推导出两个简化的肌肉形状.
- 进行了一项模拟研究,使用盲源分离在解剖学和简化模型中进行分解,评估识别的MU数量及其一致率 (RoA).
主要成果:
- 所有模型都成功地确定了能量贡献最高的MU.
- 简化的肌肉形状导致了由于变化的电极-纤维距离而导致分解的MU的统计学上显著增加 (p <0.05).
- 在所有模型中,协议率 (RoA) 仍然很高,超过94.63±12.16%,表明可靠的MU物业提取.
结论:
- 在解剖学上准确的肌肉形状模型对运动单元分解有好处.
- 即使是肌肉形状模型中的微小简化也可以影响MU分解的产量.
- 该研究强调了在EMG信号处理模拟中详细解剖表示的重要性.
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