title: Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential.
abstract: The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.
authors.affiliation: g.tec Guger Technologies OEG, Herberstein str. 60, 8020, Graz, Austria. shahid@gtec.at
authors.forename: Shahjahan
authors.initials: S
authors.lastname: Shahid
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Walker
authors.forename: Gerard M
authors.initials: GM
authors.lastname: Lyons
authors.forename: Ciaran A
authors.initials: CA
authors.lastname: Byrne
authors.forename: Anand Vishwanath
authors.initials: AV
authors.lastname: Nene
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D007719:Knee Joint / Q000502:physiology; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018485:Muscle Fibers, Skeletal / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*