Ren Xiaomei, Zhang Chuan, Li Xuhong, Yang Gang, Potter Thomas, Zhang Yingchun
School of Electrical Engineering and Information, Sichuan University, Chengdu, China.
Department of Biomedical Engineering, University of Houston, Houston, TX, United States.
Front Neurol. 2018 Jan 23;9:2. doi: 10.3389/fneur.2018.00002. eCollection 2018.
A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.
提出了一种新颖的肌电图（EMG）信号分解框架，用于对肌内EMG信号进行全面而精确的分析。该框架首先检测所有活跃的运动单位动作电位（MUAP），并将单个MUAP片段分配给其相应的运动单位。然后，使用剥离方法将发现叠加的MUAP波形分解为其组成的单个MUAP，并进行类似的分配。该方法由六个分析程序阶段组成：预处理、分割、对齐和特征提取、聚类和细化、监督分类以及叠加波形解析。使用合成EMG信号和从健康参与者及中风患者获得的真实记录对所提出的分解框架的性能进行了评估。对于合成信号和真实信号，MUAP的总体检测率均为100%。合成EMG信号的平均准确率为87.23%。从健康参与者和中风患者获得的真实EMG信号的平均分配准确率分别为88.63%和94.45%。结果表明，所开发的框架能够以更高的准确性和效率分解肌内EMG信号，我们相信这将极大地有利于EMG在中风患者运动障碍诊断和康复中的临床应用。
