An Algorithm for Sequential Signal Estimation and System Identification for EMG Signals

This paper presents a new algorithm for optimal adaptation of the signal templates of a matched filter bank used in the detection of the motor unit action potential waveforms (abbreviated as MUAP’s) in an electromyogram (EMG). It is of interest, for clinical diagnosis and therapy, to detect as many MUAP’s as possible in a single measurement, and to determine for each motor unit the repetition rate of its respective MUAP. For this purpose, we have developed a computer program which, in addition to other subprograms, contains the adaptive filter bank mentioned above. The templates in this filter bank have to be adapted to nonpredetermined changes in measurement conditions such as the movement of the needle electrode inserted in the muscle. In the present paper, the above templates are estimated by means of a “tumbling algorithm,” so called because the successive MUAP’s from a given motor unit are used as noisy data vectors in a time-varying Kalman filter-predictor framework, which alternately estimates their evolving shapes and identifies the time-varying parameters of the model generating them. The algorithm has been applied with success to synthetic and real EMG data.