Fang J, Agarwal G C, Shahani B T
Department of Rehabilitation Medicine and Restorative Medical Sciences, University of Illinois at Chicago, USA.
IEEE Trans Biomed Eng. 1999 Jun;46(6):685-97. doi: 10.1109/10.764945.
We have developed a comprehensive technique to identify single motor unit (SMU) potentials and to decompose overlapped electromyographic (EMG) signals into their constituent SMU potentials. This technique is based on one-channel EMG recordings and is easily implemented for many clinical EMG tests. There are several distinct features of our technique: 1) it measures waveform similarity of SMU potentials in the wavelet domain, which gives this technique significant advantages over other techniques; 2) it classifies spikes based on the nearest neighboring algorithm, which is less sensitive to waveform variation; 3) it can effectively separate compound potentials based on a maximum signal energy deduction algorithm, which is fast and relatively reliable; and 4) it also utilizes the information on discharge regularities of SMU's to help correct possible decomposition errors. The performance of this technique has been evaluated by using simulated EMG signals composed of up to eight different discharging SMU's corrupted with white noise, and also by using real EMG signals recorded at levels up to 50% maximum voluntary contraction. We believe that it is a very useful technique to study SMU discharge patterns and recruitment of motor units in patients with neuromuscular disorders in clinical EMG laboratories.
我们已经开发出一种综合技术，用于识别单个运动单位（SMU）电位，并将重叠的肌电图（EMG）信号分解为其组成的SMU电位。该技术基于单通道EMG记录，并且易于在许多临床EMG测试中实施。我们的技术有几个显著特点：1）它在小波域中测量SMU电位的波形相似性，这使该技术相对于其他技术具有显著优势；2）它基于最近邻算法对尖峰进行分类，该算法对波形变化不太敏感；3）它可以基于最大信号能量扣除算法有效地分离复合电位，该算法快速且相对可靠；4）它还利用SMU放电规律的信息来帮助纠正可能的分解误差。通过使用由多达八个不同放电的SMU组成并被白噪声干扰的模拟EMG信号，以及通过使用在高达最大自主收缩50%水平记录的真实EMG信号，对该技术的性能进行了评估。我们认为，这是一种在临床EMG实验室研究神经肌肉疾病患者的SMU放电模式和运动单位募集的非常有用的技术。
