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.

Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

“…However, when there is fatigue, S g will fall, which can be seen clearly in Fig.5. This also concludes that there is a change of MU recruitment during the third walking trial as in [10,12]. A measure of Linearity based on whether or not the estimated bicoherence is constant, follows the reverse pattern with that of Gaussianity.…”

“…The analysis revealed that SEMG turn into less Gaussian on escalating mean voluntary contraction (MVC). Different research works using HOS showed that MUAP waveform increased according to the increase in load weight where there is no involvement of Motor Units (MUs) recruitment in the resting muscle [10][11][12]. The point at which a contraction can no longer be maintained (the failure point) has been generally designated as the point at which the muscle is said to fatigue [13].…”

Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

“…Therefore, a wearable measurement system was constructed to detect the biosignals [3] and [4]. In general, the sEMG signal provides information about the performance of muscles and nerves [5], [6], also in the context of neurological diagnosis of myopathies and neuropathies.…”

Cepstrum of bispectrum—A new approach to blind system reconstruction

“…Our approach has been successfully used in recovery of biomedical signals [20]. In the present work, we present the details of the method more fully, in particular providing a detailed account of the phase unwrapping approach used.…”