Decomposition of multiunit electromyographic signals

The authors 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 the authors´ 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. The authors 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.