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SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
Authors:J. Bashford  A. Wickham  R. Iniesta  E. Drakakis  M. Boutelle  K. Mills  C. Shaw
Affiliation:1. UK Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom;2. Department of Bioengineering, Imperial College London, United Kingdom;3. Department of Biostatistics and Health Informatics, King’s College London, United Kingdom
Abstract:
ObjectivesFasciculations 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 (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).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.
Keywords:Corresponding author.  Amyotrophic lateral sclerosis  Fasciculation  High-density surface EMG  Biomarker  ALS  amyotrophic lateral sclerosis  amplitude exclusion threshold  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
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