A technique for the detection, decomposition and analysis of the EMG signal.

In the present paper we have described a system for acquiring, processing and decomposing EMG signals for the purpose of extracting as many motor unit action potential trains as possible with the greatest level of accuracy. This system consists of 4 main sections. The first section consists of methodologies for signal acquisition and quality verification. Three channels of EMG signals are acquired using a quadripolar needle electrode designed to enhance discrimination among different MUAPs. An automated experiment control system is devised to free the experimenter from the burden of experiment detailed surveillance and bookkeeping; and to allow on-line assessment of the EMG signal quality in terms of decomposition suitability. The second section consists of methodologies for signal sampling and conditioning. The EMG signal is bandpass filtered (between 1 kHz and 10 kHz), sampled and compressed by eliminating parts of the signal under a preset threshold level. The third section consists of a signal decomposition technique where motor unit action potential trains are extracted from the EMG signal using a highly computer assisted interactive algorithm. The algorithm uses a continuously updated template matching routine and firing statistics to identify MUAPs in the EMG signal. The templates of the MUAPs are continuously updated to enable the algorithm to function even when the shape of a specific MUAP undergoes slow variations. The fourth section deals with ways in which to analyze and display the results. The more frequently used representation formats are: (1) display of MUAP wave shapes; (2) impulse trains representing motor unit firings; (3) IPI plots where time interval between successive firings of the same motor unit is plotted vs. time of the muscle contraction; (4) firing rate plots where the estimated time-varying mean firing rate of the detected motor units is plotted vs. time of the muscle contraction. The performance of the system has been tested in terms of: (1) consistency among results obtained by different operators; (2) accuracy evaluated on synthetic EMG signal; (3) indirect measure of accuracy on real EMG signal by comparing results pertaining the same motor unit action potential trains derived by two EMG signals, independently and simultaneously recorded from two different electrodes.