SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
File(s)
Author(s)
Type
Journal Article
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.
Date Issued
2019-07-01
Date Acceptance
2019-03-17
Citation
Clinical Neurophysiology, 2019, 130 (7), pp.1083-1090
ISSN
1388-2457
Publisher
Elsevier
Start Page
1083
End Page
1090
Journal / Book Title
Clinical Neurophysiology
Volume
130
Issue
7
Copyright Statement
© 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/31078984
PII: S1388-2457(19)30133-6
Grant Number
EP/K503381/1
Subjects
Amyotrophic lateral sclerosis
Biomarker
Fasciculation
High-density surface EMG
Publication Status
Published
Coverage Spatial
Netherlands
Date Publish Online
2019-04-19