SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis. Issue 7 (July 2019)

• Record Type: Journal Article
• Authors: Bashford, J.; Wickham, A.; Iniesta, R.; Drakakis, E.; Boutelle, M.; Mills, K.; Shaw, C.
• Is Part Of: Clinical neurophysiology. Volume 130:Issue 7(2019:Jul.)
• Page Start: 1083
• Page End: 1090
• Publication Date: 2019-07
• Subjects: Amyotrophic lateral sclerosis -- Fasciculation -- High-density surface EMG -- Biomarker; ALS amyotrophic lateral sclerosis -- ATexc amplitude exclusion threshold -- ATinc amplitude inclusion threshold -- AUC area under the curve -- BFS benign fasciculation syndrome -- FP fasciculation potential -- (HD)SEMG (High-density) surface electromyography -- IFI inter-FP interval -- MMN multifocal motor neuropathy -- MU motor unit -- NEMG needle electromyography -- ROC receiver operating characteristic -- SEM standard error of the mean -- SPiQE surface potential quantification engine; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2019.03.032
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: SPiQE combines serial high-density surface EMG with an innovative signal-processing methodology. SPiQE identifies fasciculations in ALS patients with high sensitivity and specificity. The optimal noise-responsive model achieves an average classification accuracy of 88%. Abstract: Objectives: Fasciculations 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. Methods: Six 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. Results: By 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 (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). Conclusion: We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). Significance: Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.