Surface EMG decomposition based on K-means clustering and convolution kernel compensation.
Surface EMG decomposition based on K-means clustering and convolution kernel compensation.
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DOI：
10.1109/jbhi.2014.2328497
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发表时间：
2015-03
影响因子：
7.7
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Ning Y;Zhu X;Zhu S;Zhang Y
通过结合 K 均值聚类（KMC）方法和改进的卷积核补偿（CKC）方法，开发了一种用于多通道表面肌电图（EMG）分解的新方法。 KMC 方法首先用于对不同时刻的观测向量进行聚类，然后估计初始神经支配脉冲序列（IPT）。 CKC 方法采用新颖的多步迭代过程进行修改，用于更新估计的 IPT。通过从模拟和实验表面 EMG 信号重建 IPT 来评估所提出的 K 均值聚类 - 改进的 CKC (KmCKC) 方法的性能。 KmCKC 方法成功地从模拟表面 EMG 信号中重建了所有 10 个 IPT，其真阳性率 (TPR) 超过 90%，信噪比 (SNR) 为 -10dB。当收缩力保持在 8 N 时，使用 KmCKC 方法还成功地从第一背侧骨间 (FDI) 肌肉的 64 通道实验表面 EMG 信号中提取了超过 10 个运动单位。进一步使用 64 通道表面肌电图信号进行“双源”测试。从两组独立的表面 EMG 信号重建的 IPT 之间，公共 MU 和公共脉冲的高百分比（在所有力水平下超过 92%）证明了所提出的 KmCKC 方法在多通道表面 EMG 分解中的可靠性和能力。模拟和实验数据的结果是一致的，并证实所提出的 KmCKC 方法可以在不同收缩水平上以高精度成功重建 IPT。
A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multi-step iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering - Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of −10dB. Over 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A ‘two-source’ test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multi-channel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.