IEEE Trans Neural Syst Rehabil Eng. 2023;31:3641-3651. doi: 10.1109/TNSRE.2023.3309546. Epub 2023 Sep 18.
Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ±3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.
目的-本研究旨在开发一种新的高密度表面肌电（HD-sEMG）信号分解框架，具有更高的分解效率和准确性，特别是对于低能量 MU。方法-提出了一种迭代卷积核补偿-剥离（ICKC-P）框架，该框架由三个步骤组成：使用迭代卷积核补偿（ICKC）方法对相对较大能量的运动单位（MU）进行分解，以及使用后处理器和新的“剥离”策略提取低能量 MU。结果-通过模拟和实验 HD-sEMG 信号评估了所提出框架的性能。我们的模拟结果表明，在 120 个模拟 MU 中，与 K-means 卷积核补偿（KmCKC）方法相比，在所提出的框架中，在六个噪声水平下提取到的 MU 更多。并且，在所提出的框架中，“剥离”策略在六个噪声水平下比在渐进式 FastICA 剥离（PFP）框架中提出的“剥离”策略估计出更准确的 MUAP 波形。对于从肱二头肌记录的实验 sEMG 信号，每个收缩中平均可识别出 16.1 ±3.4 个 MU，而 KmCKC 方法仅可识别出 10.0 ± 2.8 个 MU。结论-从模拟和实验 HD-sEMG 信号分解出的 MU 的高产量和准确性表明，与现有的 HD-sEMG 信号分解方法相比，所提出的框架在分解低能量 MU 方面具有优越性。意义-所提出的框架使我们能够构建更具代表性的运动单位池，从而增强我们对各种神经病理条件的理解，并为神经肌肉疾病和运动神经元疾病的诊断和治疗提供宝贵信息。
