AbstractOBJECTIVESFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.METHODSSix ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability.RESULTSBy applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/-0.2 SEM), specificity of 91.6% (+/-0.1 SEM) and classification accuracy of 87.9% (+/-0.1 SEM). An amplitude exclusion threshold (100μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p=0.394).CONCLUSIONWe have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE).SIGNIFICANCELongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.HighlightsSPiQE combines serial high-density surface EMG with an innovative signal-processing methodologySPiQE identifies fasciculations in ALS patients with high sensitivity and specificityThe optimal noise-responsive model achieves an average classification accuracy of 88%
A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control