Effect of optimal filtering parameters for autoregressive model AR(p) on motor unit action potential signal

Error is one element of the autoregressive (AR) model, which is supposed to be white noise. Correspondingly assumption that white noise error is a normal distribution in electromyography (EMG) estimation is one of the common causes for error maximization. This paper presents the effect of a suitable choice of filtering function based on the non-invasive analysis properties of motor unit action potential signal, extracted from a non-invasive method-the high spatial resolution (HSR) electromyography (EMG), recorded during low-level isometric muscle contractions. The final prediction error procedure is used to find the number of parameters in the model. The error signal parameter, the simulated deviation from the actual signals, is suitably filtered to obtain optimally appropriate estimates of the parameters of the automatic regression model. It is filtered to acquire optimally appropriate estimates of the parameters of the automatic regression model. Then appropriate estimates of spectral power shapes are obtained with a high degree of efficiency compared with the robust method under investigation. Extensive experiment results for the proposed technique have shown that it provides a robust and reliable calculation of model parameters. Moreover, estimates of power spectral profiles were evaluated efficiently.

Common Peroneal Nerve Injury in a Patient with COVID-19 Infection

This report described a 46-year man with the characteristic Computerized Tomography (CT) scan findings of Corona Virus Disease Infection 19 (COVID-19) who presented to the hospital with right ankle weakness three weeks after the pneumonitis. He had been initially hospitalized, complaining of fever, myalgia, cough, and dyspnea. Electromyogram (EMG) revealed obvious evidence of increased insertional activity (IA) and significant denervation potentials, including positive sharp waves (PSW) and fibrillation potentials, particularly in ankle dorsiflexor muscles. Moreover, no voluntary motor unit action potential (MUAP) was observed. Eventually, the patient was diagnosed with severe axonal mononeuropathy of the right CPN, which could be considered a rare complication of COVID-19.

Monitoring Involuntary Muscle Activity in Acute Patients with Upper Motor Neuron Lesion by Wearable Sensors: A Feasibility Study

Sustained involuntary muscle activity (IMA) is a highly disabling and not completely understood phenomenon that occurs after a central nervous system lesion. We tested the feasibility of in-field IMA measuring at an acute rehabilitation ward. We used wearable probes for single differential surface EMG (sEMG), inclusive of a 3D accelerometer, onboard memory and remote control. We collected 429 h of data from the biceps brachii of 10 patients with arm plegia. Data quality was first verified in the time and frequency domains. Next, IMA was automatically identified based on the steady presence of motor unit action potential (MUAP) trains at rest. Feasibility was excellent in terms of prep time and burden to the clinical staff. A total of 350.5 h of data (81.7%) were reliable. IMA was found in 85.9 h (25%). This was often present in the form of exceedingly long-lasting trains of one or a few MUAPs, with differences among patients and variability, both within and between days in terms of IMA duration, root mean square (RMS) and peak-to-peak amplitude. Our results proved the feasibility of using wearable probes for single differential sEMG to identify and quantify IMA in plegic muscles of bedridden acute neurological patients. Our results also suggest the need for long-lasting acquisitions to properly characterize IMA. The possibility of easily assessing IMA in acute inpatients can have a huge impact on the management of their postures, physiotherapy and treatments.

Effects of detection system parameters on cross-correlations between MUAPs generated from parallel and inclined muscle fibres

The aim of this study was to investigate the effects of inter-electrode distance (IED), electrode radius (ER) and electrodes configurations on cross-correlation coefficient (CC) between motor unit action potentials (MUAPs) generated in a motor unit (MU) of parallel fibres and in a MU of inclined fibres with respect to the detection system. The fibres inclination angle (FIA) varied from 0° to 180° by a step of 5°. Six spatial filters (the longitudinal single differential (LSD), longitudinal double differential (LDD), bi-transversal double differential (BiTDD), normal double differential (NDD), an inverse binomial filter of order two (IB2) and maximum kurtosis filter (MKF)), three values of IED and three values of ER were considered. A cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers was used to simulate the MUAPs. The cross-correlation coefficient analysis showed that with the increase of the FIA, the pairs of MUAPs detected by the IB2 system were more correlated than those detected by the five other systems. For each FIA, the findings also showed that the MUAPs pairs detected by BiTDD, NDD, IB2 and MKF systems were more correlated with smaller IEDs than with larger ones, while inverse results were found with the LSD and LDD systems. In addition, the pairs of MUAPs detected by the LDD, BiTDD, IB2 and MKF systems were more correlated with large ERs than with smaller ones. However, inverse results were found with the LSD and NDD systems.